Hybrid neural network control of uncertain switched nonlinear systems with bounded disturbance

In this work, we focus on the design of a hybrid neural tracking controller for a class of uncertain switched nonlinear systems with bounded disturbance. A new tracking control model is constructed using adaptive particle swarm optimization (APSO) based neural network called hybrid neural network (HNN). Hybrid neural tracking controller is developed by combining the backstepping approach and neural network approximation ability along with complexity analysis. A common Lyapunov function (CLF) is used for the stability of the proposed model, to develop a CLF for the switched system, a virtual control function is developed via adaptive law and HNN. By appropriately choosing the design parameters, it has been proven that all closed-loop signals are semi-globally uniformly ultimately bounded and the tracking error converges to a small bounded region around the origin. Finally, two numerical examples and a real-life example about one-link manipulator systems demonstrate the effectiveness of the proposed hybrid controller.