劉 康志

This study presents a two-dimensional (2-D) repetitive control method to address the issues of periodic tracking and disturbance suppression in uncertain Takagi-Sugeno systems. The disturbance and uncertainty are treated as an equivalent-input-disturbance (EID). However, the conventional EID estimators typically suppress the EID through high gain. Meanwhile, the low-pass filter associated with EID causes a certain degree of phase lag. A proportional-integral (PI) filter is integrated with an EID estimator to develop a PI-EID structure to improve the estimation accuracy. Based on the self-learning mechanism of repetitive control, the 2-D repetitive controller is used to achieve a high level of tracking. Unlike the conventional nonlinear repetitive control methods, the state observer and the PI-EID estimator are membership function dependent. The gains of both controllers switch in line with the signs of the time derivative of the normalized premise variables, and this framework takes full account of the information of the nonlinear membership functions. The controller design procedures and the stability conditions are detailedly presented. Finally, a rotation speed control experiment is conducted to validate the developed PI-EID method.

Optimization control of the rate of penetration (ROP) is crucial due to its vital role in maximizing the drilling efficiency. In this article, a dynamic optimization-based intelligent control system for drilling ROP is proposed considering the drilling characteristics. First of all, the framework of the proposed system is described, which has two layers (intelligent optimization layer and basic automation layer). In the former layer, two stages (ROP modeling stage and ROP optimization/implementation stage) are executed alternatively by using the moving window strategy. Moving window-Extreme learning machine and tenfold cross validation are used to establish the dynamic model between the rotation speed (RPM), weight on bit (WOB), depth, and ROP. In addition, hybrid bat algorithm is introduced to search the controllable parameters while the inputs for ROP optimization are RPM, WOB, depth, constraints, and ROP model, and the outputs are the optimized RPM and optimized WOB. After that, the outputs are recommended to the driller to be used as the setpoints of the basic automation layer. Comparison results of simulation with seven well-known methods demonstrate the effectiveness of the proposed system. In addition, in an industrial application to a real-world drilling process in the Xiangyang area, Central China, the ROP was improved by 15.9%.

The phase-shaping technique is a vital element for the control design of parametric systems. However, how to extend the technique to the matrix case is still an unsolved problem because we lack an easily applicable characterization of phase angle for transfer matrices. This article tackles this problem based on a model called the sectorial ball. Phase shaping is carried out via a scope matrix, and the corresponding design conditions are established. Furthermore, a metaheuristic algorithm is tailored for the design. Then, it is shown in detail how to apply this phase-shaping algorithm to design a high-performance gain-scheduled controller for parametric systems as well as saturated systems. Finally, this method is applied to an electronic throttle system with input saturation. Experiments and comparisons with other saturation control methods validate the effectiveness of the proposed method.

This article is concerned with the stability and stabilisation of switched time-delay systems (STDSs) with exponential uncertainty. Based on the Hurwitz convex combination and the energy attenuation principle, an improved state-dependent switching strategy is proposed, which switches to the next modes to obey the minimum energy. This approach fully considers the system dynamic of subsystems, which is more general. Considering the complex switching and delay dynamics, a mode-dependent Lyapunov–Krasovskii functional (LKF) that contains a triple integral term is constructed. The generalised free-matrix-based integral inequality (GFMBII) is used to estimate the integral terms in the derivative of the LKF, and an improved delay-dependent stability criterion is established in the form of linear matrix inequalities (LMIs). Further, to guarantee the stability of the STDSs with a large time-varying delay, a controller that considers the time delay and the exponential uncertainty is designed. Under this controller, a less conservative delay-dependent robust stabilisation criterion for STDSs with exponential uncertainty is established. The validity of the proposed methods is validated by two numerical examples and an application in river pollution control.

This paper develops a composite output consensus control protocol for a general linear multiagent system subject to mismatched disturbances, which incorporates active disturbance-rejection control and fully distributed adaptive consensus control. To estimate and then cancel out the effect of mismatched disturbances on the outputs of the agents, heterogeneous generalized equivalent-input-disturbance estimators are constructed in the inner loop. Then a fully distributed adaptive feedback controller is designed to achieve consensus control based on the states of the designed heterogeneous observers for the agents. The restriction on the disturbances is lowered, the requirement for the global information of the communication topology is removed, and the exchanging information among agents is only relative estimated states. Further, the output consensus performance is analyzed for the closed-loop multiagent system. Our results complement and improve the results of the existing literature. Lastly, the effectiveness and superiority of the developed method are demonstrated through a numerical simulation and a comparison with the distributed extended-state-observer-based method.

This article investigates the stability and unweighted L1-gain analysis of switched time-delay positive systems (STDPSs) with stable and unstable subsystems. The persistent dwell time (PDT) switching is considered for the first time, which is more general compared with the typical dwell time (DT) or average dwell time (ADT) techniques. Under the PDT switching, the activation time of an unstable subsystem is less than an upper bound instead of greater than a given constant, which makes it easier to achieve stability in practice. Considering the stable and unstable subsystems, a piecewise multiple co-positive Lyapunov–Krasovskii functional is constructed to obtain the stability criterion of the STDPSs. Subsequently, the unweighted L1-gain for STDPSs is explored, which has explicit physical meaning compared with the weighted results and can be calculated by solving the linear programming problem. Finally, a water-quality model and a numerical example are provided to indicate the validity of the proposed methods.

Weight learning forms a basis for the machine learning and numerous algorithms have been adopted up to date. Most of the algorithms were either developed in the stochastic framework or aimed at minimization of loss or regret functions. Asymptotic convergence of weight learning, vital for good output prediction, was seldom guaranteed for online applications. Since linear regression is the most fundamental component in machine learning, we focus on this model in this paper. Aiming at online applications, a deterministic analysis method is developed based on LaSalle's invariance principle. Convergence conditions are derived for both the first-order and the second-order learning algorithms, without resorting to any stochastic argument. Moreover, the deterministic approach makes it easy to analyze the noise influence. Specifically, adaptive hyperparameters are derived in this framework and their tuning rules disclosed for the compensation of measurement noise. Comparison with four most popular algorithms validates that this approach has a higher learning capability and is quite promising in enhancing the weight learning performance.

A soft-switching interleaved bidirectional dc-dc converter, which is the combination of the conventional one and the introduced auxiliary circuit, is proposed in this article. The auxiliary branch composed of a capacitor, an inductor and two switches connected in series, is attached to the switching nodes of the two phases and serves them in a shared manner. Due to voltage continuity of the auxiliary capacitor, the zero-voltage switching (ZVS) condition is realized nearly for primary switches at turn-off. With the designed driving sequences, the current gradually flows through auxiliary inductor ahead of primary switches turn-on, which is key to realizing ZVS condition for the switches. Moreover, the limited current slope guarantees the zero-current switching condition for auxiliary switches. Operating principle in buck mode is elaborated and design guideline is presented. Considering the parasitic resonance between the auxiliary inductor and the output capacitors of auxiliary switches, a clamping structure is given. A 1.2-kW prototype of the proposed converter is built and tested. The experimental results show the high performance.

Rate of penetration (ROP) accurate prediction can effectively reduce drilling cycle time and operation cost. However, the complexity and variability of drilling process is not conducive to accurate ROP modeling. The existing ROP models focus on static prediction considering drilling data, which cannot effectively solve the ROP mutation problem caused by complex formations. Some ROP online prediction studies have great real-time capture capability, but without considering the formation lithology adequately, and can be limited in practical application. In this paper, a multi-source information fusion-based dynamic model for online prediction of ROP in drilling process is proposed, which consists of three stages (drilling data pre-processing, optimized ROP modeling, and ROP model updating). In the first stage, three drilling parameters including weight on bit, rotational speed, and torque are filtered by using the outlier removing and wavelet filtering techniques. In the second stage, a novel ROP modeling method, named as hybrid bat algorithm optimized - restricted Boltzmann machine - back propagation neural network (HBAO-RBM-BPNN) is proposed to build the ROP prediction model. In the last stage, formation drillability variation and time interval are used as update conditions, and the established ROP model is updated via a moving window strategy to realize high accuracy online prediction of ROP. Multi-source (formation and drilling) information fusion is considered in this key stage. Finally, the proposed method and seven ROP prediction methods (two offline and five online) are compared through the use of industrial data from a drilling site in Dandong area, Northeast China. The comparison results verify the effectiveness of the proposed method, which lays a foundation for intelligent optimization control of complex geological drilling process.

This paper focuses on the stability problem and the design method of PI-type H∞ controller for load frequency control (LFC) of power systems with multiple time-varying delays (MTD). In the existing researches, the model of LFC system with time delays is not accurate enough since it is established based on the assumption that the time delays are all the same. This paper proposes a novel model of multi-area LFC system with MTD. Both the stability analysis and controller design in this paper are based on the improved model to ensure the applicability of the proposed method in general. First, a novel Lyapunov-Krasovskii functional (LKF) containing delay-product-type term (DPT) is constructed and auxiliary function-based integral inequality (AFBII) is utilized to handle the integral term of the derivative of LKF. Then, an improved reciprocally convex inequality is proposed to obtain the less conservative stability criteria in terms of linear matrix inequalities (LMIs). Next, an effective H∞ controller design method is developed based on the proposed stability criteria. Finally, simulation experiments are done to illustrate the superiority of proposed criteria and effectiveness of proposed design method.

Smart grid allows the penetration of a high percentage of power from renewable energy sources (RES). The intermittent nature of RES makes accurate prediction of its output power impossible, and the prediction uncertainty is inevitable. This imposes a great challenge to the operation of smart grid. This paper focuses on photovoltaic (PV) generation and proposes a robust stochastic approach for the grid operation. First, we show that the uncertainty can be significantly reduced by the smoothing effect of PVs and build a simplified statistical model for the prediction of the total PV power, together with a bound of prediction uncertainty. After that, a criterion for the frequency maintenance is derived, which relates the allowable bound of frequency deviation to that of the PV power uncertainty. Then, a robust stochastic economic dispatch of the thermal power is formulated based on this criterion. Further, we propose an optimal PV dispatch method based on a peak-cut operation to maximize the PV output while guaranteeing the frequency performance. Finally, the operation performance is analyzed in detail on an annual basis by simulations using actual solar radiation data. It is revealed that the frequency quality is maintained with a 5% increase in fuel cost and an 11% reduction of PV power for a penetration ratio over 30%, compared with the case w/o frequency maintenance.

In this paper, a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph. It is of two-degree-of-freedom nature. Specifically, a robust distributed controller is designed for consensus tracking, while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances. The condition for asymptotic disturbance rejection is derived. Moreover, even when the disturbance model is not exactly known, the developed method also provides good disturbance-rejection performance. Then, a robust stabilization condition with less conservativeness is derived for the whole multi-agent system. Further, a design algorithm is given. Finally, comparisons with the conventional one-degree-of-freedom-based distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method. Manuscript received May 13, 2022; accepted June 12, 2022. This work was supported by the National Natural Science Foundation of China (62003010, 61873006, 61673053), the Beijing Postdoctoral Research Foundation (Q6041001202001), the Postdoctoral Research Foundation of Chaoyang District (Q1041001202101), and the National Key Research and Development Project (2018YFC1602704, 2018YFB1702704). Recommended by Associate Editor Xiaohua Ge.

Crowbar is a popular device used to facilitate the fault ride-through of doubly fed induction generator (DFIG), which protects the power converter from the damage of overcurrent/overvoltage. The crowbar is activated once an overcurrent/overvoltage is detected, and it is desirable to release the crowbar as soon as possible in order to output active/reactive powers to the grid. But releasing the crowbar at an inappropriate timing may result in the recurrence of overcurrent. So, it is important to release the crowbar at an adequate timing. To this end, we analytically investigate the behavior of rotor current, and reveal that the current amplitude fluctuates periodically with a decaying magnitude. Hence, there is a set of release timings which may prevent overcurrent/overvoltage. Further, it is shown that these release timings are robust to the variations of voltage dip depth, generator parameters as well as wind speed. This analysis not only provides a justification for existing release algorithms, but also forms a solid basis for further improvement of crowbar operation. Finally, an algorithm is proposed for the crowbar operation which can prevent overcurrent/overvoltage while minimizing the adverse impact on the grid. The analysis is validated and compared via detailed simulations.

Saturation is mainly characterized by its passivity and magnitude bound. But most of the saturation control methods only make use of either of these features. To enhance the performance of saturated systems, this paper develops a novel method capable of fully using both of these two features. This method is a two-stage design scheme which integrates the phase-shaping technique with the gain-scheduled control. The phase-shaping fully uses the passivity of saturation while the gain-scheduling actively utilizes the magnitude bound of saturation. In this way, the design conservatism associated with existing methods is reduced substantially. Specifically, a matrix-type phase-shaping method is developed through the placement of systems’ frequency loci, and a meta-heuristic method is devised for the design of the phase-shaping function. Furthermore, the gain-scheduled control is transformed into the robust performance problem of a passive uncertain system, and designed by the passivity-based robust control method of the authors. Application to two practical control systems validates the effectiveness of the proposed method. The superiority is demonstrated via comparisons with typical saturation control methods.

The flexible DC distribution network has the characteristics of low line loss, good power quality, fast system response, strong control and adjustment capabilities. It has become one of the mainstream trends in the development of the future energy internet. The effective detection of high impedance fault (HIF) is currently one of the key issues to be solved urgently in the flexible DC distribution network. For this reason, HIF detection method based on color relation analysis classifier (CRAC) is proposed. First, the complete ensemble empirical mode decomposition with adaptive noise algorithm is used to extract the intrinsic modal function (IMF) components. An IMF with the highest similarity is selected to calculate the IMF energy value in different states. Then, a starting threshold is set to distinguish between normal and abnormal states. At last, the CRAC is used to distinguish HIF, capacitor switching (CS), load switching (LS). Among them, the specific algorithm of CRAC includes the following steps: Firstly, the absolute value of the vector difference is obtained by subtracting the IMF components under normal and abnormal operation states. The absolute value is converted into Euclidean distance. Then, the Euclidean distance is transformed into gray grade. The mean value, maximum and minimum values of gray grade are converted into a red, green, and blue model. The model is transformed into a Hue-Saturation-Value color space model. At last, HIF, CS, and LS are distinguished according to the size of the hue angle. A large number of tests have verified the effectiveness of the proposed detection method.

This paper proposes a novel optimal control method for integrated systems of Wind farms (WFs) and hydrogen production systems (HPSs). Green hydrogen production via renewable power generation (RPG), such as wind power generation, is a promising technology to overcome environmental problems. RPG has the potential to become more widespread if we can produce hydrogen in an HPS using the output fluctuation and the output surplus of RPG, which cause power outages due to supply-demand imbalances. The proposed optimal control maximizes the capacity factor of HPS while producing hydrogen constantly and satisfying the technical requirement related to the output fluctuation of the WF. The proposed control is also easy to implement and needs no WF output forecast. We demonstrate the effectiveness of the proposed optimal control through simulated comparative analysis with conventional methods.

The disturbance rejection problem of T-S fuzzy systems is concerned. Since the T-S fuzzy system is characterised by its membership function, less conservative stabilisation conditions can be derived from membership function-dependent Lyapunov function which contributes to the improvement of disturbance rejection performance. Specifically, we utilise a configuration composed of a membership function-dependent state observer for the state estimation, a membership function-dependent equivalent-input-disturbance estimator for the estimation and compensation of disturbance and an internal model for the reference tracking. It is revealed that this membership function-dependent Lyapunov function naturally leads to control gains switching in accordance with the derivative signs of the normalised premise variables. The switching rules and the design conditions for all control gains are obtained explicitly. In particular, the free-weighting-matrix approach is used to lessen the conservatism in the stability condition. Moreover, a concrete procedure for the controller design including the switching rule is given. Finally, the developed method is tested via simulations. The advantage of the membership function-dependent equivalent-input-disturbance method is validated by comparing with conventional methods.

This paper focuses on the stability problem of delayed neural networks with time-varying delay. A general quadratic negative-determination lemma (GQNL) is presented in this paper. GQNL takes full use of the introduced parameter and the interval endpoints, which can further reduce the conservatism of stability criteria without increasing the number of decision variables (NoVs). A stability criterion for delayed neural networks is obtained by employing GQNLand the superiority of the proposed criterion is demonstrated by two numerical examples.

A fair and reasonable price mechanism is fundamental in improving the efficiency of the crowdsensing market. Being an emerging paradigm that utilizes widely distributed wireless communication network and smart sensing devices to collect sensing data, crowdsensing service offers a plausibility for collecting real-time data. However, previous studies ignored the complex competition among participants in the free data trade market and the fresh data demands of increasingly popular real-time applications. This paper introduces the age of information (AoI) as a criterion to measure the freshness of data geared towards constructing incentive models for the data provider and data requester. Subsequently, considering the opening of the market, we design a price mechanism framework based on non-cooperative games, which can help the crowdsensing platform provide each participant a stable sensing strategy to maximize profit. For the AoI-sensitive case, i.e., when the data quality deteriorates with the time. A co-evolutionary algorithm based on heuristic search is designed to solve the sensing strategy for each provider to achieve Nash equilibrium. Furthermore, for the case where AoI is insensitive. An improved relaxation algorithm is proposed to provide a stable strategy for the data provider. Numerical findings reveal that the developed algorithm can effectively identify stable sensing strategies in a Nash equilibrium. (C) 2022 Elsevier Inc. All rights reserved.

The spatial disturbance, which depends on position rather than time, is widespread in rotary machines. In this article, an improved control method named accurate cycle aligned repetitive control (ACARC) is proposed to deal with its suppression. In the ACARC, the data-storage technique is used to realize the spatial internal model and a design requirement is presented for the rejection of spatial disturbance with a better response. Further, the cycle offset, caused by the auxiliary stabilizing compensator, is significantly reduced by the combination of a spatial low-pass compensator and a time-advance element. Moreover, a cascaded structure of ACARCs is presented together with a detailed design procedure for disturbance with multiple components occurring in actual rotary systems. The effectiveness and superiority of the proposed control method are verified and compared with other methods by the simulations and experiments.

These days, networked control systems (NCSs) in which data is transmitted via communication have been actively studied for many potential applications. In an NCS, data dropout degrades control performance depending on network conditions. For an NCS with data dropout, the authors propose a model-predictive-control-based input optimisation, representing data dropout as both a Bernoulli model and a finite-order Markov chain. Using the proposed NCS data dropout model, the authors derive an optimal input that provides the estimated error between the expected state of the plant and a given reference. The proposed control problem is formulated as its equivalent quadratic programming, as executed at each online sampling. The authors also demonstrate simulations and experiments to show the effectiveness of the proposed method.

This paper proposes a soft switching full bridge ac-dc converter consisting of its hard switching counterpart and simple active auxiliary branch. The auxiliary branch is composed of an inductor and two power switches. The proposed converter features zero voltage switching (ZVS) turn-on for main switches and zero current switching (ZCS) condition for auxiliary switches. The ZVS condition is created by turning on auxiliary switches ahead of main switch turn-off signal to reversely excite auxiliary inductor. Two mutually coupled inductors connected in series in opposing magnetic field manner are designed as the main filter inductor and to provide positive voltage for auxiliary inductor to degauss it as well. Meanwhile, the auxiliary inductor contributes ZCS turn-on and turn-off to the auxiliary switches because it slows down the change rate of the current through the switches. Considering the parasitic capacitors of the auxiliary switches inevitably resonate with the auxiliary inductor, a clamping technique is given to alleviate the additional voltage stresses caused by the resonance. Then, the mathematical parameter design is deduced to guide the converter realization. Finally, a laboratory prototype with 1000W rated power operating at 50kHz switching frequency is completed and tested to demonstrate the soft switching features. The efficiency of the proposed converter is up to 97.67% at full load and the maximum efficiency improvement is 5.3% at light load, which further highlights the outstanding performance.

The original model-based wide-area damping control (WADC) is difficult to obtain good damping performance due to the complex structure of interconnected power systems. In this paper, an improved data-driven model-free adaptive control (ID-MFAC) algorithm is proposed to design WADC for interconnected power systems with stochastic communication delays. First, the multiple-input multiple-output power system can be decoupled as multiple single-input single-output subsystems by considering controller interactions and system disturbances. Second, a step-size vector is designed to adjust the pseudo-partial derivative and make the algorithm more flexible by considering both input and output data adequately. Furthermore, an adaptive delay compensator is designed to compensate stochastic communication delays. Last, simulations are conducted to verify that the ID-MFAC algorithm can efficiently suppress multiple inter-area oscillations modes under various stochastic communication delays.

Optimization control of the rate of penetration (ROP) is crucial in the drilling process due to its vital role in improving the drilling efficiency and safety. In this paper, an improved dynamic optimization control system for ROP is proposed and successfully applied to a drilling site. First, a three layers (intelligent optimization layer, basic automation layer, and process monitoring layer) system framework is proposed according to the drilling characteristics. After that, “If-Then” strategy is used as the method to identify the rotary drilling condition from four different working conditions. Moving-window strategy is selected to establish the dynamic ROP model that can better adapt to environmental changes. Moreover, Jaya algorithm, a new meta-heuristic optimization method is introduced to solve the dynamic ROP optimization issue. In the simulations, the optimization performance of the proposed system is better than moving window-extreme learning machine-hybrid bat algorithm with more stable (fewer switch times) drilling operational parameters. Finally, in an industrial application to a drilling process in Dandong area, Northeast China, the ROP was improved by 20.79% compared with manual operation. Both simulation and industrial application results indicated that the proposed system has significantly improved the drilling efficiency and safety.

There is a problem of insufficient reference tracking accuracy in servo system driven by permanent magnet synchronous motor (PMSM), which limits its application. This paper aims to propose a modified error-driven preset-input least mean square adaptive filter (EPLMS-AF) to improve the tracking accuracy. The reference tracking error is used to drive the update of adaptive weights in EPLMS-AF. The designed preset-inputs makes the reference tracking error converge to zero according to the form of reference. On the premise of choosing an appropriate iteration size to ensure the stability of the filter, the tracking error is able to reduce to zero along the gradient direction. At last, numerical simulations are provided to illustrate the high efficiency and wide applicability of the proposed method.

Spatially cyclic disturbances exist widely in rotating machines. They usually have fixed spatial cycles rather than constant time periods, which affect the stationarity of angular speed tracking. An input matching least mean square (LMS) adaptive filter (IMLMS-AF) is proposed to cope with the effects of spatially cyclic disturbances. The IMLMS-AF sets a part of the inputs as spatially cyclic signals for disturbance rejection and forms another part as a time-dependent function for reference tracking. Furthermore, an updated law and convergence of the pending weights are given. The system's stability is proved by combining the instantaneous gradient with Lyapunov theory. Moreover, the IMLMS-AFs are parallelized to reject disturbance with multiple components and reference tracking. The effectiveness and superiority of the proposed control method are verified and compared with other methods by simulations and experiments.

Accurate prediction of the rate of penetration (ROP) is a difficult issue in the drilling process, especially under complex formation conditions. Many methods, such as mechanism and machine learning, were introduced to investigate it. However, most of them are offline prediction methods which may not be capable of capturing the online trend of ROP. In this paper, a novel dynamic model for ROP prediction is proposed considering the process characteristics, which consists of three stages. In the first stage, the correlations between ROP and eight drilling parameters are analyzed, and the rotational speed, weight on bit, depth are selected as the model inputs. In the second stage, the drilling data are pre-processed by using the filtering and re-sampling techniques. In the last stage, the moving window strategy, extreme learning machine, and 10-fold cross validation are used to establish the ROP model. Our main idea of online prediction of ROP lies in this last stage. Specifically, two steps (modeling and prediction) are executed alternately in the moving drilling depth windows so as to predict the ROP more accurately. Finally, the proposed ROP prediction model is applied to the drilling well ZK3 in Xiangyang area, Central China. The prediction accuracy is improved by at least 7% compared with seven well-known ROP prediction methods, two online and five offline, which validates the effectiveness of the proposed method. It is believed that the proposed model provides a basis for intelligent optimization control in drilling process. (C) 2021 Elsevier Ltd. All rights reserved.

The development of 5G and internet of things technologies has promoted the application of crowdsensing services. Consequently, online crowdsensing markets, based on data trade, have emerged. In this article, we first investigate the status quo of the current crowdsensing and crowdsourcing markets, then analyse behavioural characteristics among participants. Thereafter, we design a unified crowdsensing market framework based on supply and demand. These are aimed at encouraging mobile users and data requesters to participate in market activities, with special attention paid to participant incentive models affected by the market environment. Next, we formally consider several new features in the crowdsensing service to provide iterative methods for solving sensing strategy of all participants, with the aim of achieving Nash equilibrium, including task assignment for mobile users and price guidance for data requesters. We validate our proposed methods by providing some numerical results, and discuss several challenges and open issues to be solved.

Disturbances are inevitable in control practice. Many methods have been reported for disturbance rejection. Among them, equivalent-input-disturbance (EID) approach is effective for both matched and mismatched disturbances. However, since the disturbances are usually unknown, conventional methods cannot be used to analyze the control performance of disturbance rejection, especially the dynamic performance. In this paper, by treating the Luenberger observer as the role of an ideal system, an auxiliary variable, i.e., the output error betwween the disturbed system and the ideal system, is introduced to aid the disturbance-rejection performance analysis of EID approaches. The relation between the control performance and the output error are revealed. Then, a nonlinear EID estimator is constructed to speed up the disturbance-rejection control. The finite-time dynamic performance and uniformly ultimately bounded steady-state performance are guaranteed. Further, a design algorithm is developed for the NEID-based closed-loop control system. Finally, by comparing with a conventional linear EID-based method, simulation results illustrate the validity and superiority of the developed method.

Passive power filters are indispensable for grid-connected inverters (GCIs) to eliminate current harmonics. However, typical filters require large inductance, which not only increases the filter dimension and costs but also leads to slower current response and a high voltage drop across the filter. This article proposes a novel filter called LCTCL filter, which effectively attenuates harmonics with small inductance. In addition, this article presents a controller synthesis to the GCI with LCTCL filter without an additional resistor. We demonstrate the effectiveness of the proposed LCTCL filter through comparative analysis with typical filters via simulations and experiments. We clarify that the proposed LCTCL filter has excellent features in terms of filter dimension, frequency characteristics, robustness for parameter variations, harmonic suppression, and total harmonic distortion.

Networked control systems have received increasing attention from many researchers because of their vast potential. The insertion of communication networks in a controlled system brings network-induced defects, which are mainly caused by limited network resources. This paper proposes a codesign of periodic communication scheduling and a controller using sparsity for efficient use of the network while improving initial control performance. The effectiveness of the proposed method is verified through two numerical simulations.

In recent years, formation control has received a great deal of attention as one of the most interesting issues in multiple mobile robots systems. In variable formation control systems, multiple mobile vehicles can form an appropriate formation from a list of possible formations, such that all mobile vehicles can pass even when the width of the course is narrow, and simultaneously the vehicles maintain a set distance between the course and each vehicle. This paper presents a predictive-based automatic formation control. In our formation control system, a unique formation is determined from feasible formations at any given sampling point. This control is formulated by its corresponding mixed-integer quadratic programing by introducing binary variables that are used to specify the formation. The effectiveness of the proposed methods is verified by applying two-wheeled mobile vehicles through simulations and experiments.

This paper focuses on the asymptotic stability problems of the generalized neural networks (GNNs) with two additive time-varying delays (ATDs) and general activation function. First of all, a simple Lyapunov-Krasovskii functional (LKF) is established. Then not only the generalized free-weighting-matrix-based (GFWM-based) inequality together with its optimization strategy on choosing an arbitrary vector is utilized to estimate the single integral terms in derivative of the constructed LKF, but also the general activation function method is applied to introduce more information on cross terms of neuron activation function. Finally, a less conservative stability criterion together with its corollary is derived with convex combination technique, whose feasibility and superiority can be demonstrated with two numerical examples.

In crowdsensing, the diversity of the sensing tasks and an enhancement of the smart devices enable mobile users to accept multiple types of tasks simultaneously. In this study, we propose a new practical framework for dealing with the challenges of task assignment and user incentives posed by complex heterogeneous task scenarios in a crowdsensing market full of competition. First, based on the non-cooperative game property of mobile users, the problem is formulated into a Nash equilibrium problem. Then, to provide an efficient solution, a judgment method based on constraints (sensing time and sensing task dimension) is designed to decompose the problems into different situations according to the complexity. We propose a genetic-algorithm-based approach to find the combination of tasks that maximizes the utility of users and adopts a co-evolutionary model to formulate a stable sensing strategy that maintains the maximum utility of all users. Furthermore, we reveal the impact of competition between users and tasks on user strategies and use a cooperative weight to reflect it mathematically. Based on this, an infeasible solution repair method is designed in the genetic algorithm to reduce the search space, thus effectively accelerating the convergence speed. Extensive simulations demonstrate the effectiveness of the proposed method.

The recent development of the communication technology accelerates studies of real-time networked control systems using networks. The data dropout is essentially unavoidable, especially in wireless networks and it results from transmission errors and network traffic congestion. Multiple time-varying network traffic status given by discrete-time homogeneous Markov chains is assumed. The authors estimate the network traffic status characterised by the probability matrix of the Markov chain online from the data dropout history. According to the estimation of network traffic status, an appropriate controller is selected to improve the control performance. The effectiveness of the proposed method is verified through simulations and experiments.

In robust control problems, how to elicit and utilize the uncertainty information is the key in achieving a good robust performance. For passive uncertainties, the phase bound plays a fundamental role. For nonpassive uncertainties, the gain bound is used up to date. The former leads to the passivity approach and the later to the small-gain approach. A recently developed bounded positive real model succeeded in extracting both the phase and the gain information from passive uncertainties. However, it is still not known how to model the phase bound of a nonpassive uncertainty. Aiming at achieving an even higher performance for nonpassive uncertain systems, this article proposes a new model together with a modeling procedure for a class of nonpassive uncertainties. This model well captures both the gain and the phase features of the nonpassive uncertainty. Further, a numerically tractable design method is developed by extending the passivity method. The advantage of the proposed method is demonstrated through a real-world case study.

Filters are inevitable for grid-connected inverters to attenuate the current harmonics caused by the pulse width modulation which is usually used in power conversion systems. High-order filters have attracted much attention because they attenuate the current harmonics effectively. Nevertheless, the high-order filters have some resonances which cause instability of the system. In addition, the resonance frequencies shift to high as the inductors and capacitors are smaller. It implies that the resonance frequencies may be beyond the Nyquist frequency in downsizing the filter. This complicates the stability and performance analyses of the system. This paper investigates rigorous input-output stability and analyses a robust performance based on the sampled-data control theory regardless of whether the resonance frequencies are beyond the Nyquist frequency or not. Our analysis contributes to downsizing the filter synthesis while the stability and the robustness are guaranteed even if the resonance frequencies are beyond the Nyquist frequency. The effectiveness of the proposed method is verified through simulations and experiments.

Compared to control bandwidth, low-frequency uncertainties or disturbances like step signals can be well rejected by many methods having two-degree-of-freedom (2-DOF). Due to robustness constraint, technically more challenging is the rejection of medium frequencies, especially for bandwidth-limited systems. Here, the equivalent-input-disturbance (EID) approach is extended to deal with the main medium-frequency oscillation of a pantograph-catenary system. First, a general EID estimator is developed with a low-frequency estimator as a special case. Then, a fair comparison is conducted to clarify the essential differences between the conventional 1-DOF-based and the developed 2-DOF-based control systems. Furthermore, a robust stability condition is derived for the 2-DOF-based closed-loop control system. A design algorithm together with design guidelines is provided, where the frequency characteristics of the uncertainties are utilized in the parameter design. Finally, simulations are carried out to validate the developed 2-DOF-based method for the pantograph-catenary system in realistic environment.

Position sensorless vector control of interior permanent magnet synchronous motors (IPMSMs) has been used for downsizing, cost reduction, and high reliability. In the position sensorless vector control, the q-axis inductance decreases as the q-axis current increases due to the load variation, which results in deterioration of the position estimation performance without considering the q-axis inductance. Here, the authors propose an innovative adaptive estimation based on the dynamic certainty equivalence principle for high-performance position sensorless control for IPMSMs. The effectiveness of the proposed adaptive scheme is verified through experiments.

The robust performance design of parametric systems is a long-standing unsolved problem, even though its analysis has seen significant success. Most existing design methods usually treat the parameter uncertainty as other types, such as norm-bounded uncertainty, and apply the corresponding approach. However, such treatment inevitably broadens the range of uncertainty and brings about design conservatism consequentially. To overcome such difficulty and establish a less conservative performance design method for parametric systems, this article looks back at the passivity theory and put forward a phase-shaping approach. This approach is composed of a generalized Popov transformation and a phase-shaping method for the nominal system. The key idea is to transform an uncertain but positive parameter into a positive real function while shaping the phase of the nominal system via a meta-heuristic method. This design freedom of phase shaping makes it possible to achieve a higher performance. Furthermore, this method is applied to the control design of drivetrain system: a test bed for automobile drivetrains. Its superiority is validated experimentally on an industrial setup.

This paper proposes a novel nonlinear decentralized control method for a class of multi-machine power systems. The aim is to construct a suitable decentralized feedback control law so as to guarantee that the large-scale interconnected power system is exponentially stable and robust against load fluctuations. To this end, we propose a novel design algorithm based on the backstepping, which involves five steps and actively utilizes the locally measurable part of the tie-line power to improve the performance. The effectiveness of the proposed control scheme is demonstrated through extensive simulation studies.

spatial model of the 3D formation drillability field is crucial for drilling optimization in the petroleum area. In this paper, a new spatial modeling method is proposed for the 3D formation drillability field, which has two stages. In the first stage, the number of formation modes is determined according to the formation characteristics and these modes are identified by the fuzzy c-means clustering algorithm. In the second stage, random forest models are built separately for all formation modes. X, Y ground coordinates and depth coordinate are selected as the model inputs while the model output is the formation drillability. After that, these spatial 3D formation models are combined into one spatial 3D formation model for the whole drillability field. The proposed method and four compared methods (Random forest, ScatteredInterpolant, Support vector regression, and Kriging) are cross-validated and tested by using the data from eight drilling wells in the Xujiaweizi area, Northeast China. The results indicate the effectiveness of proposed method in spatial 3D formation drillability modeling.

In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.

The control method based on the equivalent-input-disturbance (EID) estimator and the Luenberger state observer has received much attention in recent years. However, the property of EID-based control systems is still not well investigated. A number of design procedures were proposed but lacked sufficient theoretic justification. In this article, the two-degree-of-freedom nature of an EID-based control system is revealed. Specifically, the reference-tracking is determined by an outer loop, while the disturbance rejection and robustness is determined by an inner loop consisting of the EID estimator and state observer. The bandwidth constraints on the inner loop are analyzed for plants having zeros or poles in the open right-half plane by using the Bode and the Poisson integral formulas. These analyses provide a theoretic justification for conventional design procedures. Further, a coordinated design algorithm is provided for the EID-based uncertain control systems. In addition, a comprehensive comparison of the EID-based and the disturbance observer (DOB) based control systems is conducted in the system design aspect. Lastly, comparative studies of the EID-based and DOB-based control methods are given for various types of plants to validate the developed design algorithm.

In this paper, a time series model based on hybrid-kernel least-squares support vector machine (HKLSSVM) with three processes of decomposition, classification, and reconstruction is proposed to predict short-term wind power. Firstly, on the basis of the maximal wavelet decomposition (MWD) and fuzzy C-means algorithm, a decomposition method decomposes wind power time series and classifies the decomposition time series components into three classes according to amplitude- frequency characteristics. Then, time series models on the basis of least-squares support vector machine (LSSVM) with three different kernels are established for these three classes. Non-dominated sorting genetic algorithm II optimizes the parameters of each forecasting model. Finally, outputs of forecasting models are reconstructed to obtain the forecasting power. The proposed model is compared with the empirical-mode-decomposition least-squares support vector machine (EMD-LSSVM) model and wavelet-decomposition least-squares support vector machine (WDLSSVM) model. The results of the comparison show that proposed model performs better than these benchmark models.(c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

This paper proposes a novel optimization method for energy storage systems (ESSs) to smooth wind farm output to satisfy the technical requirements and reduce the rated power (rated energy capacity) and charge/discharge loss of the ESS. The state of charge of the ESS operated by the proposed method can be regulated, guaranteeing some constraints. The proposed method has a very simple structure, does not require the wind farm output forecast and numerical optimization, such as particle swarm optimization. Therefore, a high-grade functional computation device is not needed. The effectiveness of the proposed method is verified by comparative analysis with conventional approaches through simulations.