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A multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems with input deadzones. This multilayer NN controller has an adaptive critic NN architecture with two NNs for compensating the deadzone nonlinearity and a third NN for approximating the dynamics of the nonlinear system. A reinforcement learning scheme in discrete-time is proposed for the adaptive critic NN deadzone compensator, where the learning is performed based on a certain performance measure, which is supplied from a critic. The adaptive generating NN rejects the errors induced by the deadzone whereas a second NN based critic generates a signal, which is used to tune the weights of the action generating NN so that the deadzone compensation scheme becomes adaptive whereas a third multilayer NN simultaneously approximate the nonlinear dynamics of the system. Using the Lyapunov approach, the uniform ultimately boundedness (UUB) of the closed-loop tracking error and weight estimates of action generating NN, critic NN and the third NN are shown by using a novel weight update.
- Lyapunov Approach,
- Adaptive Control,
- Adaptive Critic-Based Neural Network Controller,
- Closed Loop Systems,
- Closed-Loop Tracking Error,
- Control Nonlinearities,
- Control System Synthesis,
- Deadzone Compensation Scheme,
- Deadzone Nonlinearity,
- Discrete Time Systems,
- Dynamics Approximation,
- Learning (Artificial Intelligence),
- Multilayer Neural Network Controller,
- Multilayer Perceptrons,
- Neurocontrollers,
- Nonlinear Control Systems,
- Reinforcement Learning Scheme,
- Tracking Performance,
- Uncertain Nonlinear Systems,
- Uncertain Systems,
- Uniform Ultimately Boundedness,
- Unknown Deadzones,
- Weight Estimates
Available at: http://works.bepress.com/jagannathan-sarangapani/24/