Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.

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► This paper presents an adaptive PI Hermite neural control system based on a Hermite neural network for multi-input multi-output uncertain nonlinear systems.
► A proportional–integral learning algorithm is derived to speed up the convergence of the tracking error.
► The proposed control system is applied to control an inverted double pendulums and a two-link robotic manipulator.
► Simulation results verify that the proposed control scheme can achieve high-precision tracking performance.