Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 50, Issue 8, Pages 3433-3443Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2921057
Keywords
Artificial neural networks; Reinforcement learning; Convergence; Adaptive systems; Optimal control; Discrete-time systems; Discrete-time systems; input saturation; multigradient recursive (MGR); neural networks (NNs); reinforcement learning
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Funding
- National Natural Science Foundation of China [61673072, 61751202, 61803064]
- Guangdong Natural Science Funds for Distinguished Young Scholar [2017A030306014]
- Innovative Research Team Program of Guangdong Province Science Foundation [2018B030312006]
- Science and Technology Innovation Funds of Dalian [2018J11CY022]
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In this paper, an adaptive neural network (NN) control problem is investigated for discrete-time nonlinear systems with input saturation. Radial-basis-function (RBF) NNs, including critic NNs and action NNs, are employed to approximate the utility functions and system uncertainties, respectively. In the previous works, a gradient descent scheme is applied to update weight vectors, which may lead to local optimal problem. To circumvent this problem, a multigradient recursive (MGR) reinforcement learning scheme is proposed, which utilizes both the current gradient and the past gradients. As a consequence, the MGR scheme not only eliminates the local optimal problem but also guarantees faster convergence rate than the gradient descent scheme. Moreover, the constraint of actuator input saturation is considered. The closed-loop system stability is developed by using the Lyapunov stability theory, and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed approach is further validated via some simulation results.
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