Firefly optimized neural network-based trajectory tracking control of partially unknown multiplayer nonlinear systems

In this paper, we develop an integral reinforcement learning (IRL)-based trajectory tracking control (TTC) scheme via firefly optimized neural networks for partially unknown multiplayer nonlinear systems. Under the developed TTC scheme, IRL is proved to be equivalent to the classical policy iteration, which guarantees the convergence of the IRL algorithm. By implementing the IRL method, the requirement of the drift dynamics is obviated. The TTC policy for each player is obtained by solving the coupled Hamilton–Jacobi equation with a critic neural network, whose weight vector is tuned by the firefly algorithm. The tracking error of the closed-loop system is guaranteed to be stable via the Lyapunov's direct method. Simulation results illustrate the effectiveness and superiority of the present IRL-based TTC scheme, and show that the success rate of system operation is increased by introducing the firefly algorithm.