Reinforcement learning-based nonlinear tracking control system design via LDI approach with application to trolley system

Tu, Yidong; Fang, Haiyang; Yin, Yanyan; He, Shuping

doi:10.1007/s00521-021-05909-8

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…An online actor‐critic algorithm is proposed to solve the continuous‐time infinite horizon optimal control problem in Reference 26. A novel algorithm for the nonlinear tracking problem is designed in Reference 20. An online adaptive optimal control problem for a class of nonlinear Markov jump systems (MJSs) is studied in Reference 21.…”

Section: Introductionmentioning

confidence: 99%

“…Reinforcement learning is a technique in which the agent interacts with the environment and learns an optimal policy, which avoids the need for system dynamics when designing controllers. Recently, the ideas of reinforcement learning have been used to solve the optimal control problem, [14][15][16][17][18][19][20][21] that is to design optimal controllers for the unknown or partially unknown system. Adaptive dynamic programming (ADP) [22][23][24][25] is a typical technique using the idea of reinforcement learning to solve adaptive optimal control problems.…”

Section: Introductionmentioning

confidence: 99%

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Output‐feedback Q‐learning for discrete‐time linear H^∞ tracking control: A Stackelberg game approach

Ren

Wang

Duan

2022

Intl J Robust & Nonlinear

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In this article, an output‐feedback Q‐learning algorithm is proposed for the discrete‐time linear system to deal with the H∞$$ {H}_{\infty } $$ tracking control problem. The problem is formulated as a zero‐sum game in the Stackelberg game framework with a discount factor to make the value function bounded. According to the principle of optimality, the game algebraic Riccati equation (GARE) is derived and solved by the Q‐learning algorithm to get the optimal solution of the Stackelberg game without requiring the knowledge of system dynamics and state. It is proved that the solution of the algorithm converges to the optimal control input and the worst‐case disturbance with excitation noises during training, and the Stackelberg strategy can achieve a lower L2$$ {L}_2 $$ disturbance attenuation level than the Nash one. Moreover, the impacts of the discount factor on the stability of the closed‐loop system and solvability of the GARE are analyzed to provide some criteria for the choice of the discount factor. Simulation examples are provided to validate the effectiveness of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Output‐feedback Q‐learning for discrete‐time linear H^∞ tracking control: A Stackelberg game approach

Ren

Wang

Duan

2022

Intl J Robust & Nonlinear

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show abstract

“…+is topic is not a challenging one only because this class of time-delayed systems is highly confronted in the industrial process, but mainly due to the restrictions in proving the desired closed-loop performance objectives, in addition to the stability requirement. As needs be, the delicacy identified with the tracking control problem is intensively considered by researchers nowadays [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Combining Augmented Error Modeling Technique and Block-Pulse Functions Method for Tracking Control Design of Nonlinear Polynomial Systems with Multiple Time-Delayed States Subject to Nonsymmetric Input Saturation

Warrad

2022

Mathematical Problems in Engineering

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The present research work is intended to synthesize a novel tracking control strategy for a class of nonlinear polynomial systems characterized by multiple well-defined delays in state variables under the presence of nonsymmetric input saturation. The design strategy makes full use of an associate’s memory nonlinear state feedback control with integral-based actions. An original control scheme joining block-pulse functions method combined with the augmented error modeling technique is used to infer the controller’s tracking gains. The objective is to convert the investigated nonlinear algebraic problem governed by specifying constraints into a constrained linear one that can be solved in the constrained least square methodology. Detailed novel sufficient conditions proving the closed-loop augmented system’s practical stability are elaborated. The instance of a twofold inverted pendulums benchmark is considered so as to exhibit the benefits of the proposed control approach.

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“…MahmoudZadeh et al [3] present an efficient data collection strategy exploiting a team of unmanned aerial vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture and renewable energy applications. Tu et al [4] study a novel scheme for the tracking problem of nonlinear systems, where two reinforcement learning algorithms are proposed to design the optimal control law. Tang el al.…”

mentioning

confidence: 99%

Special issue on computational intelligence-based modeling, control and estimation in modern mechatronic systems

Hai

Zheng

et al. 2022

Neural Comput & Applic

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Reinforcement learning-based nonlinear tracking control system design via LDI approach with application to trolley system

Cited by 10 publications

References 29 publications

Output‐feedback Q‐learning for discrete‐time linear H^∞ tracking control: A Stackelberg game approach

Output‐feedback Q‐learning for discrete‐time linear H^∞ tracking control: A Stackelberg game approach

Combining Augmented Error Modeling Technique and Block-Pulse Functions Method for Tracking Control Design of Nonlinear Polynomial Systems with Multiple Time-Delayed States Subject to Nonsymmetric Input Saturation

Special issue on computational intelligence-based modeling, control and estimation in modern mechatronic systems

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Reinforcement learning-based nonlinear tracking control system design via LDI approach with application to trolley system

Cited by 10 publications

References 29 publications

Output‐feedback Q‐learning for discrete‐time linear H∞ tracking control: A Stackelberg game approach

Output‐feedback Q‐learning for discrete‐time linear H∞ tracking control: A Stackelberg game approach

Combining Augmented Error Modeling Technique and Block-Pulse Functions Method for Tracking Control Design of Nonlinear Polynomial Systems with Multiple Time-Delayed States Subject to Nonsymmetric Input Saturation

Special issue on computational intelligence-based modeling, control and estimation in modern mechatronic systems

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Output‐feedback Q‐learning for discrete‐time linear H^∞ tracking control: A Stackelberg game approach

Output‐feedback Q‐learning for discrete‐time linear H^∞ tracking control: A Stackelberg game approach