Self-Evolving Data Cloud-Based PID-Like Controller for Nonlinear Uncertain Systems

In this chapter, a novel self-evolving data cloud-based PID-like controller (SEDCPID) is proposed for uncertain nonlinear systems. The proposed SEDCPID controller is constructed by using the EFS based on data clouds whose rules are comprised of the non-parametric data cloud-based antecedence and PID-like consequence. The antecedent data clouds adopt the relative data density to represent the fuzzy firing strength of input variables instead of the explicit design of the membership functions in the classical sense. The proposed SEDCPID controller has the advantages of evolving structure and adapting parameters concurrently in an online manner. The density and distance information of data clouds are proposed to achieve the addition and deletion of data clouds and also a stable recursive method is proposed to update the parameters of the PID-like sub-controllers for the fast convergence performance. Based on the Lyapunov stability theory, the stability of the proposed controller is proven and the proof shows the tracking errors converge to a small neighborhood. Numerical and experimental results illustrate the effectiveness of the proposed controller in handling the uncertain nonlinear dynamic systems.