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Data-driven based optimal output feedback control with low computation cost

INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2024年第8期38卷 2790-2809页

作者： Yu, Xinyi Yu, Jiaqi Zhang, Yongqi Wu, Jiaxin Wei, Yan Ou, Linlin Zhejiang Univ Technol Dept Automat Hangzhou 310000 Peoples R China

A partial model-free, data-driven adaptive optimal output feedback (OPFB) control scheme with low computational cost continuous-time is proposed in this paper. The design objective is to obtain the optimal control law by using measurable input and output data, without some knowledge of system model information. Firstly, the system states are decoupled into measurable and unmeasurable parts, and a new state-space equation is built to estimate the unmeasurable states by using a reduced-order observer. Based on this, a parametrization method is utilized to reconstruct the system states. Subsequently, by using the reconstructed states, the adaptive dynamic programming (ADP) Bellman equations based on policy-iteration (PI) and value-iteration (VI) are presented to solve the control problems with initially stable and unstable conditions, respectively. Then, the convergence of the system is proved. Compared with the early proposed OPFB algorithms, only the unknown internal state needs to be reconstructed. Therefore, the computation cost and design complexity are reduced for the proposed scheme. The effectiveness of the proposed scheme is verified through two numerical simulations. In addition, a practical inverted pendulum experiment is carried out to demonstrate the performance of the proposed scheme.

关键词： adaptive dynamic programming data-driven output feedback policy-iteration value-iteration

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Zero-Sum Differential Game-Based Optimal Fault-Tolerant Control for Interconnected Systems With Actuator Faults

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2024年第3期11卷 1287-1299页

作者： Liu, Qingyi Zhang, Ke Jiang, Bin Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

This article investigates the zero-sum game-based optimal fault-tolerant control problem for a series of strict-feedback nonlinear interconnected systems with both multiplicative and additive actuator failure. According to the command-filter-based backstepping theory, the feedforward controller is derived, and the optimal fault-tolerant tracking problem can be converted into an equivalent game problem for the affine tracking error system. Afterward, the adaptive dynamic programming algorithm is utilized to deal with the two-player zero-sum differential game problem, in which a critic and two actor networks are constructed to evaluate the value function, the optimal controller, and the worst-case lumped faults, respectively. It is shown that the obtained feedforward controller and the game-based optimal controller can ensure the boundedness of tracking errors while minimizing the value function. Finally, several comparison results are conducted to illustrate the reliability and effectiveness of the proposed control scheme from the aspects of network weight and optimality.

关键词： Fault tolerant systems Fault tolerance Interconnected systems Game theory Games Additives Actuators adaptive dynamic programming command-filtered backstepping fault-tolerant optimal control interconnected systems zero-sum game

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Robust adaptive optimal trajectory tracking control for underactuated AUVs with position and velocity constraints in three-dimensional space

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第15期34卷 10704-10730页

作者： Gong, Huibin Er, Meng Joo Liu, Yi Dalian Maritime Univ Inst Artificial Intelligence & Marine Robot Coll Marine Elect Engn Dalian 116026 Peoples R China

The safety and optimality of underactuated autonomous underwater vehicles (AUVs) during operations are essential factors to consider. In this context, a three-dimensional robust adaptive optimal trajectory tracking control method under position and velocity constraints, unknown dynamics, and environmental disturbances is proposed. The main features of the method are: (1) The outputs of an underactuated AUV system are redefined to handle the underactuation problem. (2) The system with position and velocity constraints is transformed into an unconstrained system by a nonlinear state-dependent transformation. (3) A critic-identifier architecture is constructed using adaptive dynamic programming and neural networks in a backstepping framework. Specifically, critic networks and weight update laws without requiring initial stability control are designed to solve Hamilton-Jacobi-Bellman equations in kinematic and dynamic subsystems, and optimal virtual and actual control laws are obtained. (4) A neural network identifier is developed to estimate unknown dynamics. Disturbances are overcome by improving the cost function and solving for optimal control of the nominal dynamic subsystem. By stability analysis, tracking errors in the AUV closed-loop system can converge to an arbitrarily small compact set of the origin, and the other signals are uniformly ultimately bounded. Simulation comparisons demonstrate the effectiveness and superiority of the proposed method.

关键词： adaptive dynamic programming autonomous underwater vehicles position and velocity constraints robust optimal control trajectory tracking control unknown dynamics

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Value Iteration-Based Cooperative adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 690-697页

作者： Yun Zhang Lulu Zhang Yunze Cai the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai 200240China

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence *** players are divided into two groups in the learning process and adapt their policies *** method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based ***,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control *** efficacy of our method is illustrated by three examples.

关键词： adaptive dynamic programming incomplete information multi-player differential game value iteration

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Optimal control of partially unknown constrained-input systems: A dynamic event-triggered-based approach

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第1期45卷 64-84页

作者： Zou, Haoming Zhang, Guoshan Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

This article presents an identifier-based dynamic event-triggered optimal control scheme for partially unknown constrained-input systems. First, an event-triggered-based neural network (NN) identifier is constructed to estimate the unknown system dynamics. Then, an adaptive dynamic programming algorithm with actor-critic NN structure is adopted to obtain an approximate solution of the Hamilton-Jacobi-Bellman equation. The above considers that transmitted measurements are only available at the triggering instants, and the update of all three NN weights depends on the established dynamic event-triggered mechanism. Different from existing static event-triggered mechanism, the proposed dynamic event-triggered mechanism can further obtain a reasonable trade-off between performance and communication resources by introducing a dynamic variable, and the Zeno behavior can be excluded by devising an exponential term. It is proved that all the closed-loop system signals are uniformly ultimately bounded under the established event-triggered mechanism. Finally, two numerical examples are provided, including the spring-mass-damper system, to validate the proposed control scheme.

关键词： actor-critic structure adaptive dynamic programming constrained input dynamic event-triggered mechanism neural network identifier

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Parametrized guaranteed cost method with prescribed performance via critic-only ADP for uncertain nonlinear systems and disturbances

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第7期34卷 4650-4676页

作者： Liu, Yuxian Li, Yan Northwestern Polytech Univ Sch Automat Xian 710129 Shaanxi Peoples R China

In this paper, a novel parametrized guaranteed cost control (PGCC) method with prescribed performance control (PPC) is proposed for nonlinear systems with unknown bounded uncertainties and external disturbances described by Euler-Lagrange (EL) equations. The proposed method is capable of ensuring a robust optimal performance in the presence of dynamics constraints without the specific information of nonlinear perturbation. As a consequence, the conditions in the form of disturbances and uncertainties are also relaxed. The augmented model is converted by transforming the uncertainties and disturbances in the original system to a mismatched term using asymmetric PPC. By designing and modifying the novel guaranteed cost function to account for the maximum value of the nonlinear perturbation, an infinite-horizon PGCC problem is proposed by the unconstrained stationary optimal control problem. The relevant proposed linear parametrized Hamilton-Jacobian-Bellman (PHJB) equation is approximated to solve by a critic-only neural network (NN) with efficient computationally. Uniformly ultimately bounded stability is guaranteed via a Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme.

关键词： adaptive dynamic programming prescribed performance control robust optimal control uncertainty nonlinear systems

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Event-Triggered Sliding Mode Control Under Partial Model Information: Design Framework and Experimental Validation

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 6941-6953页

作者： Song, Jun Huang, Long-Yang Niu, Yugang Lv, Wenjun Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Anhui Prov Engn Res Ctr Unmanned Syst & Intelligen Sch Artificial Intelligence Hefei 230601 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Univ Sci & Technol China Inst Adv Technol Hefei 230088 Peoples R China

This paper develops an event-triggered sliding mode control (ETSMC) strategy for partially unknown disturbed systems via adaptive dynamic programming, where the system matrix is considered to be unknown to the designer. Both the sliding function and ETSMC scheme are constructed without using system matrix. An input-based event-triggered mechanism is introduced between the plant and the sliding mode controller to reduce the communication frequency. Compared with existing results on ETSMC, the proposed event-triggered mechanism can guarantee the reachability to the ideal sliding surface $s(t)=0$ and thus the external disturbances can be eliminated completely. An online policy iteration algorithm is formulated to implement the partial-model-free ETSMC strategy. It is proven that in all policy iteration steps, the reachability of the prescribed sliding surface and the optimal control performance of the sliding mode dynamics are ensured simultaneously by the proposed online updated ETSMC scheme as well as the Zeno phenomenon of the proposed event generator can be excluded. Finally, the effectiveness and the applicability of the proposed ETSMC scheme are illustrated by a numerical example and a real experiment on the permanent magnet synchronous motor speed regulation system. Note to Practitioners-ETSMC is an effective robust control strategy for the practical networked control systems that can compensate the matched disturbances in the plant as well as reduce the information transmission frequency between the plant and controller. However, the design of the existing ETSMC strategies depends on the completely known system dynamics, which is difficult or expensive to be obtained in many engineering applications. Meanwhile, the ideal sliding motion cannot be attained by the existing ETSMC approaches so that the disturbance rejection performance is degraded unsatisfactorily. To address these concerns, this paper develops a novel partial-model-free ETSMC strategy based on ada

关键词： Optimal control System dynamics Heuristic algorithms dynamics dynamic programming Sliding mode control Information processing event-triggered mechanism adaptive dynamic programming policy iteration permanent magnet synchronous motor

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ADPA Optimization for Real-Time Energy Management Using Deep Learning

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ENERGIES 2024年第19期17卷 4821页

作者： Wan, Zhengdong Huang, Yan Wu, Liangzheng Liu, Chengwei China Southern Power Grid Energy Dev Res Inst Guangzhou 510530 Peoples R China Cent Southern China Elect Power Design Inst Co Ltd China Power Engn Consulting Grp Wuhan 430071 Peoples R China

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based adaptive dynamic programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

关键词： microgrid real-time pricing deep learning adaptive dynamic programming

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Zero-sum game-based H∞ optimal finite-time prescribed performance control for nonlinear multiagent systems with actuator faults

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2024年第13期34卷 9019-9039页

作者： Zhang, Bowen Zhang, Linchuang Pan, Yingnan Bohai Univ Coll Control Sci & Engn Jinzhou Liaoning Peoples R China Bohai Univ Coll Informat Sci & Technol Jinzhou 121013 Liaoning Peoples R China

This article proposes an H-infinity optimal leader-follower consensus control protocol with finite-time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero-sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error-related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult-to-verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real-time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed-loop system are bounded and the expected finite-time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single-link robots manipulator system is executed to validate the efficacy of the developed scheme.

关键词： adaptive dynamic programming finite-time prescribed performance control nonlinear multiagent systems zero-sum game

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Distributed Remote Secure ADP-Based Control for Interconnected Power Systems Under Cyber-Attacks

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IEEE INTERNET OF THINGS JOURNAL 2024年第12期11卷 21827-21840页

作者： Zhang, Longjie Chen, Yong Abdelhameed, Esam H. Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Sichuan Prov Engn Technol Res Ctr Elect Vehicle Dr Chengdu 611731 Sichuan Peoples R China Aswan Univ Fac Energy Engn Aswan 81528 Egypt

The interconnected power systems (IPSs) generally with complex communication topology are sensitive to cyber-attacks. In this article, the problem of cyber-attacks on the IPSs is considered, and distributed remote secure adaptive dynamic programming (DRSADP) is proposed. Considering the cyber-attacks on the nodes of the sensor and actuator networks of the IPSs, the distributed remote estimators (REs) are first constructed based on the unscented Kalman filter (UKF), and the cyber-attacks models for the sensor and actuator networks are constructed. Then, to compensate for the influence of cyber-attacks and optimize the system state, the estimator-based distributed optimal control strategy is proposed. By constructing the control objective with the control performance index and estimation performance index, the novel optimal attack compensation approach is proposed. Furthermore, the distributed RE-based adaptive dynamic programming with the online finite-time actor-critic algorithm is implemented to solve the secure control strategy effectively. To illustrate the stability of the IPSs with the proposed control strategy, the property of the proposed control scheme is analyzed theoretically, and the finite-time convergence performance with the finite-time gradient learning algorithm is illustrated, which is different from the asymptotic convergence for the existing work. Finally, the results analysis shows that the proposed secure control strategy for IPSs with 9-machines has superiority compared with existing work and the RTLAB-dSPACE platform is applied to further implement the effect of the proposed method.

关键词： Cyberattack Power system stability Security Optimal control Generators dynamic programming Convergence adaptive dynamic programming cyber-attacks distributed remote estimator (RE) interconnected power systems (IPSs)

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