Decentralized optimal tracking control for large‐scale nonlinear systems with tracking error constraints

Wang, Yujia; Wang, Tong; Yang, Xuebo; Yang, Jiae

doi:10.1002/acs.3248

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…They also showed improved transient response despite having only partial information about the outputs of other subsystems 19 . Decentralised optimal tracking problem has been considered in Reference 24 using adaptive dynamical programming. Partially decentralised controllers are designed for output regulation 25 …”

Section: Introductionmentioning

confidence: 99%

Multiple models for decentralised adaptive control of discrete‐time systems

Makam,

George

2024

Adaptive Control & Signal

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This paper investigates decentralised adaptive control of discrete‐time systems consisting of linear time‐invariant systems and a class of nonlinear systems. The parameters of the subsystems and the interconnection strengths between the subsystems are assumed unknown. Multiple adaptive prediction models with switching are used in this paper to address the relatively poor transient performance that typically results from a single prediction model. However, the application of this methodology requires indirect model reference discrete‐time adaptive control, and there appears to be very little focus in the literature on this. The principal contribution of this paper is to fill this void by arriving at proof of global stability of such decentralised adaptive systems using the theory of Lyapunov and properties of square‐summable sequences. The chosen representation of the system enables the same results to be directly applicable to both linear time‐invariant and linear‐in‐the‐parameters nonlinear subsystems. We consider two parametric update algorithms, one of which has a normalisation factor. Simulation studies included in this paper demonstrate the improvement in transient performance.

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Section: Introductionmentioning

confidence: 99%

Multiple models for decentralised adaptive control of discrete‐time systems

Makam,

George

2024

Adaptive Control & Signal

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“…To achieve optimality while ensuring system safety, various novel RL methods have been developed in recent works, for example, References 5‐10. For example, barrier function‐based safe reinforcement learning (SRL) methods were developed, where barrier functions can be utilized to design the performance index function, or incorporated as an additional term in the performance function to prevent the system from approaching the unsafe region by optimizing its control policy.…”

Section: Introductionmentioning

confidence: 99%

Control Lyapunov‐barrier function‐based safe reinforcement learning for nonlinear optimal control

Wang,

2023

AIChE Journal

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This article develops a safe reinforcement learning (SRL) algorithm for optimal control of nonlinear systems with input constraints. First, we design a novel performance index function by taking advantage of control Lyapunov‐barrier functions (CLBF) with inherent safety and stability properties to ensure closed‐loop stability and safety during operation under the optimal control policy. Additionally, since it is challenging to represent the CLBF‐based value function as an explicit function of process states, neural networks (NNs) are used to approximate the value function using the process operational data that indicate safe and unsafe operations. Theoretical results on the stability, safety, and optimality of the SRL algorithm are developed, accounting for the approximation error of the NN‐based value function. Finally, the efficacy of the proposed safe optimal control scheme is shown using an application to a chemical process example.

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“…The cost function is estimated by approximate structure to solve the problem caused by DP. Therefore, many intelligent system designs based on discrete time, [16][17][18] continuous time, 19,20 and a large amount of calculation, and nonlinear systems containing constraints and environmental collisions are now solved by doing with the ADP method to work out the optimal control law. [21][22][23] Some insightful papers have introduced event-triggering control into ADP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Event‐trigger‐based approximate optimal control of modular robot manipulators using zero‐sum game

Dong

Chen

Pan

et al. 2022

Optim Control Appl Methods

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An event-triggered-based approximate optimal control is developed for modular robot manipulators (MRMs) using zero-sum game strategy. By utilizing the joint torque feedback method, robotic systems' dynamic model is formulated and state space equation is derived. According to event-triggered mechanism and neural network algorithm, the tracking control issue is converted to a zero-sum game optimal control issue. The optimal control policy and worst disturbance policy are acquired by Hamilton-Jacobi-Isaacs function respectively. The MRM system's tracking error is ultimately uniformly bounded. Finally, experiments demonstrate advantages of the method.

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Decentralized optimal tracking control for large‐scale nonlinear systems with tracking error constraints

Cited by 6 publications

References 34 publications

Multiple models for decentralised adaptive control of discrete‐time systems

Multiple models for decentralised adaptive control of discrete‐time systems

Control Lyapunov‐barrier function‐based safe reinforcement learning for nonlinear optimal control

Event‐trigger‐based approximate optimal control of modular robot manipulators using zero‐sum game

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