Recursive terminal sliding mode approximate optimal control strategy based on reconstructed nonlinear systems

For a class of nonlinear systems with unknown internal states and actuator failures, this paper proposes a recursive terminal sliding-mode fault-tolerant control method based on reconfigurable nonlinear systems and further designs an approximate optimal compensated controller based on adaptive dynamic programming, which employs a neural network (NN) to estimate the optimal value function. The system ensures that the tracking error converges to a small set around zero with faster speed and higher accuracy even when the actuator fails. Finally, the convergence of the expanded state observer and the single-evaluation NN weights is demonstrated based on the Lyapunov theory, and the stability of the whole closed-loop system is given. Simulation results and comparisons verify the effectiveness of the proposed control strategy.