Regularized extreme learning machine–based intelligent adaptive control for uncertain nonlinear systems in networked control systems

Since the learning ability of adaptive control (including multi-model adaptive control) is actually very limited and the persistent excitation (PE) condition is also not satisfied in the design of uncertain nonlinear system controllers, an improved networked control method based on regularized extreme learning machine for a class of nonlinear systems. The networked controller is built to compensate for the modeling error and system uncertainties. In the designed controller, its hidden node parameters are modified using the recently proposed regularized extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the L1/L2 norm penalty to guarantee the stability of the overall control system. The proposed model is finally applied to the internal model control in networked control system. The simulation results demonstrate good tracking performance of the proposed control scheme and have better control performance, anti-interference ability, and robustness for nonlinear system.