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Journal of Systems science & Complexity 2024年第5期37卷 1907-1922页

作者： ZHANG Bao-Qiang WANG Bing-Chang CAO Ying School of control science and engineering Shandong University

In this paper, the authors design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic n-players non-zero sum differential game with completely unknown dynamics. For each player, a critic network is used to estimate the Q-function, and an actor network is used to estimate the control input. A model-free online Q-learning algorithm is obtained for solving this kind of problems. It is proved that under some mild conditions the system state and weight estimation errors can be uniformly ultimately bounded. A simulation with five players is given to verify the effectiveness of the algorithm.

关键词： Actor-critic algorithm model-free adaptive control nonzero-sum stochastic game reinforcement learning

Value iteration algorithm for continuous-time linear quadratic stochastic optimal control problems

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science China(Information sciences) 2024年第2期67卷 170-180页

作者： Guangchen WANG Heng ZHANG School of control science and engineering Shandong University

In this study, we investigate a continuous-time infinite-horizon linear quadratic stochastic optimal control problem with multiplicative noise in control and state variables. Using the techniques of stochastic stability, exact observability, and stochastic approximation, a value iteration algorithm is developed to solve the corresponding generalized algebraic Riccati equation. Unlike the existing policy iteration algorithm, this algorithm does not rely on an initial stabilizing control. Further, this algorithm can also be used to compute policy evaluation steps that arise in the policy iteration algorithm. Herein, a simulation example is provided to validate the obtained results.

关键词： stochastic systems optimal control linear quadratic stochastic problem generalized algebraic Riccati equation value iteration algorithm

Global adaptive output-feedback tracking with prescribed performance for uncertain nonlinear systems

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science China(Information sciences) 2024年第5期67卷 150-164页

作者： Yuan WANG Yungang LIU School of control science and engineering Shandong University

At present, one typical control strategy for guaranteeing transient and steady-state performance is funnel control and prescribed performance control. The strategy features completely discarding the system nonlinearities, even if they are completely known and available. Such an intrinsic feature requires the controller to produce a larger control effect to eliminate the negative impact caused by the high nonlinearities, leading to a conservative controller. In this paper, we fully take advantage of known information on nonlinearities in control design, instead of completely discarding it as done in funnel control. Particularly, we leverage adaptive techniques(i.e., high-gain dynamic compensation) to deal with unknown system nonlinearities. Meanwhile, we integrate the tools of output feedback, tracking control, and the performance *** such, an adaptive output-feedback scheme is developed for global tracking with spatiotemporal performance specifications: arbitrarily given tracking accuracy and accuracy-ensured time. A simulation example supports the developed approach.

关键词： uncertain nonlinear systems output feedback prescribed performance unknown control direction dynamic high gain global tracking

An online value iteration method for linear-quadratic mean field social control with unknown dynamics

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science China(Information sciences) 2024年第4期67卷 38-39页

作者： Bing-Chang WANG Shumei LI Ying CAO School of control science and engineering Shandong University

Mean field(MF) models have been widely applied to economics, control theory, and other fields. Its prominent feature is that the individual influence on the overall population is negligible and the impact of the entire system to the single agent is significant and cannot be ignored. As a classical problem in control theory, linear quadratic(LQ) control for MF models has been widely investigated(e.g., [1, 2]).

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Linear-Quadratic Pareto Cooperative Game for Mean-Field Backward Stochastic System

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Journal of Systems science & Complexity 2024年第3期37卷 947-964页

作者： WANG Yu School of control science and engineering Shandong UniversityJinan 250061China

This paper focuses on a Pareto cooperative differential game with a linear mean-field backward stochastic system and a quadratic form cost functional. Based on a weighted sum optimality method, the Pareto game is equivalently converted to an optimal control problem. In the first place,the feedback form of Pareto optimal strategy is derived by virtue of decoupling technology, which is represented by four Riccati equations, a mean-field forward stochastic differential equation(MF-FSDE),and a mean-field backward stochastic differential equation(MF-BSDE). In addition, the corresponding Pareto optimal solution is further obtained. Finally, the author solves a problem in mathematical finance to illustrate the application of the theoretical results.

关键词： Backward stochastic differential equation linear-quadratic control mean-field Pareto optimality

Autonomous navigation method of satellite constellation based on adaptive forgetting factors

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Chinese Journal of Aeronautics 2024年第7期37卷 317-332页

作者： Dong WANG Jing YANG Kai XIONG School of Automation science and Electrical engineering Beihang UniversityBeijing 100191China science and Technology on Space Intelligent control Laboratory Beijing Institute of Control EngineeringBeijing 100094China

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with Adaptive Forgetting Factors(UKF-AFF)is *** process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting *** adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more *** intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency *** that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed *** analysis of the proposed method is also *** effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison *** estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

关键词： Constellation autonomous navigation Unscented Kalman filter Adaptive forgetting factor Model uncertainty Stability analysis

Resilient sliding mode control for 2D CPSs under denial-of-service attacks

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science China(Information sciences) 2024年第6期67卷 262-278页

作者： Lingling LI Rongni YANG Zhiguang FENG Ligang WU College of Intelligent Systems science and engineering Harbin Engineering University School of control science and engineering Shandong University School of Astronautics Harbin Institute of Technology

This study addresses the resilient sliding mode control(SMC) problem for two-dimensional cyber-physical systems(2D CPSs) characterized by the Roesser model under denial-of-service attack(DoSA), which can interfere with signal transmission over the communication network. First, the DoS-A model is established by introducing constraints on the Do S frequency and duration. Then, based on active or silent attack situations, the considered system is described as a switched mode. Furthermore, together with Lyapunov theory, the average dwell time technique is employed to deduce sufficient criteria that assure the existence of the desired sliding mode controller. Finally, verification examples are provided to show the validity of the established SMC scheme.

关键词： two-dimensional systems cyber-physical systems denial-of-service attack resilient sliding mode control

Deterministic Learning-Based Neural PID control for Nonlinear Robotic Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1227-1238页

作者： Qinchen Yang Fukai Zhang Cong Wang The authors are with the School of control science and engineering Shandong UniversityJinan 250061China

Traditional proportional-integral-derivative(PID)controllers have achieved widespread success in industrial ***,the nonlinearity and uncertainty of practical systems cannot be ignored,even though most of the existing research on PID controllers is focused on linear ***,developing a PID controller with learning ability is of great significance for complex nonlinear *** article proposes a deterministic learning-based advanced PID controller for robot manipulator systems with *** introduction of neural networks(NNs)overcomes the upper limit of the traditional PID feedback mechanism’s *** proposed control scheme not only guarantees system stability and tracking error convergence but also provides a simple way to choose the three parameters of PID by setting the proportional *** the partial persistent excitation(PE)condition,the closed-loop system unknown dynamics of robot manipulator systems are accurately approximated by *** on the acquired knowledge from the stable control process,a learning PID controller is developed to further improve overall control performance,while overcoming the problem of repeated online weight *** studies and physical experiments demonstrate the validity and practicality of the proposed strategy discussed in this article.

关键词： PID robot system

Deterministic learning-based neural output-feedback control for a class of nonlinear sampled-data systems

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science China(Information sciences) 2024年第9期67卷 164-183页

作者： Yu ZENG Fukai ZHANG Tianrui CHEN Cong WANG School of Automation science and engineering South China University of Technology School of control science and engineering Shandong University

This study investigates the deterministic learning(DL)-based output-feedback neural control for a class of nonlinear sampled-data systems with prescribed performance(PP). Specifically, first, a sampleddata observer is employed to estimate the unavailable system states for the Euler discretization model of the transformed system dynamics. Then, based on the observations and backstepping method, a discrete neural network(NN) controller is constructed to ensure system stability and achieve the desired tracking performance. The noncausal problem encountered during the controller deduction process is resolved using a command filter. Moreover, the regression characteristics of the NN input signals are demonstrated with the observed states. This ensures that the radial basis function NN, based on DL theory, meets the partial persistent excitation condition. Subsequently, a class of discrete linear time-varying systems is proven to be exponentially stable, achieving partial convergence of neural weights to their optimal/actual values. Consequently, accurate modeling of unknown closed-loop dynamics is achieved along the system trajectory from the output-feedback control. Finally, a knowledge-based controller is developed using the modeling *** controller not only enhances the control performance but also ensures the PP of the tracking error. The effectiveness of the scheme is illustrated through simulation results.

关键词： deterministic learning neural networks adaptive neural control predefined performance nonlinear sampled-data systems

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2135-2153页

作者： Xuerui Zhang Zhongyang Han Jun Zhao School of control science and engineering Dalian University of TechnologyDalian 116024China IEEE

Ingredient optimization plays a pivotal role in the copper industry,for which it is closely related to the concentrate utilization rate,stability of furnace conditions,and the quality of copper *** acquire a practical ingredient plan,which should exhibit long duration time with sufficient utilization and feeding stability for real applications,an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace *** address the complex challenges posed by this integer programming model,including multiple coupling feeding stages,intricate constraints,and significant non-linearity,a multi-stage differential-multifactorial evolution algorithm is *** the proposed algorithm,the differential evolutionary(DE)algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed ***,unlike traditional time-consuming serial approaches,the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm caused by the feeding stability in a parallel ***,a repair algorithm is employed to adjust infeasible ingredient lists in a timely *** addition,a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary ***,the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted,which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed *** is practically helpful for reducing material cost as well as increasing production profit for the copper industry.

关键词： Copper industry differential-multifactorial evolution ingredient optimization multi-stage optimization