Adaptive Optimal Control of Unknown Constrained-Input Systems Using Policy Iteration and Neural Networks

By Hamidreza Modares, Frank L. Lewis, Mohammad-Bagher Naghibi-Sistani. IEEE Transactions on Neural Networks and Learning Systems, 2013

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Data-Driven Optimal Consensus Control for Discrete-Time Multi-Agent Systems With Unknown Dynamics Using Reinforcement Learning Method (2017, methodology): “Among these classical RL methods, ADP is regarded as one of the core methodologies to achieve optimal control policies for diversities of optimal control issues, such as optimal control with constrained control input [16]–[19], optimal tracking control [20]–[24], optimal control with time delays [25”
Adaptive Critic Nonlinear Robust Control: A Survey (2017, methodology): “This motivates us to develop an approximate strategy to overcome the difficulty [66], [68], [69], [76], [77], [81]–[85], [120], [126], [131], which results in the ADP-based neural control design.”
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning (2018, background): “It is well known that the adaptive NN control has a learning capability [15]–[20], and has been considered as a powerful tool to identify any nonlinear function to any desired accuracy in control and applications for nonlinear systems [21]–[27].”
Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints (2021, background): “Han and Yan [28] and Zhang and Yan [29] developed learning-based adaptive optimal control for constrained-input continuous-time systems.”