Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems With Trajectory-Based Initial Control Policy

The policy gradient adaptive dynamic programming (PGADP) technique has gained recognition as an effective approach for optimizing the performance of nonlinear systems. Nonetheless, existing PGADP algorithms often demand a substantial volume of expensive or potentially risky interaction data with the...

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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 54; číslo 3; s. 1489 - 1501
Hlavní autoři: Xu, Jiahui, Wang, Jingcheng, Rao, Jun, Wu, Shunyu, Zhong, Yanjiu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive control
Adaptive dynamic programming (ADP)
Algorithms
Closed loops
Controllers
Discrete time systems
Dynamic programming
Feedback control
Heuristic algorithms
initial control policy
Neural networks
Nonlinear control
Nonlinear systems
Optimal control
Optimization
OptNet
Predictive control
System dynamics
System effectiveness
Training
Trajectory
Trajectory control
ISSN:2168-2216, 2168-2232
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Shrnutí:The policy gradient adaptive dynamic programming (PGADP) technique has gained recognition as an effective approach for optimizing the performance of nonlinear systems. Nonetheless, existing PGADP algorithms often demand a substantial volume of expensive or potentially risky interaction data with the system. Moreover, the utilization of neural networks in these algorithms can result in suboptimal learning efficiency and unstable training procedures. To address these challenges, a novel algorithm, referred to as OptNet-PGADP, has been introduced. This algorithm integrates an initially tailored control policy based on OptNet to tackle the optimization of control problems in discrete-time nonlinear systems. The OptNet-PGADP algorithm operates through a two-step process. Initially, the input-output trajectory of the system is computed using the nonlinear model predictive control (NMPC) method. Subsequently, an initial admissible control policy is acquired through OptNet. This policy is iteratively enhanced using the PGADP algorithm to attain the optimal controller. The resulting closed-loop control policy can be readily deployed in real-time applications. The implementation of the algorithm employs OptNet for the actor network and integrates an experience replay mechanism to bolster the controller's learning efficiency. Furthermore, a convergence and optimality analysis of the algorithm is included. Simulation and experimental results conducted on two nonlinear systems conclusively demonstrate that the approach outperforms traditional PGADP and NMPC algorithms. These findings underscore the efficacy of OptNet-PGADP in mitigating the constraints of current methods and achieving superior control performance for nonlinear systems.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2023.3327450