title: Self-tuning control with a filter and a neural compensator for a class of nonlinear systems.
abstract: Considering the mismatching of model-process order, in this brief, a self-tuning proportional-integral-derivative (PID)-like controller is proposed by combining a pole assignment self-tuning PID controller with a filter and a neural compensator. To design the PID controller, a reduced order model is introduced, whose linear parameters are identified by a normalized projection algorithm with a deadzone. The higher order nonlinearity is estimated by a high order neural network. The gains of the PID controller are obtained by pole assignment, which together with other parameters are tuned on-line. The bounded-input bounded-output stability condition and convergence condition of the closed-loop system are presented. Simulations are conducted on the continuous stirred tank reactors system. The results show the effectiveness of the proposed method.
authors.forename: Yue
authors.initials: Y
authors.lastname: Fu
authors.forename: Tianyou
authors.initials: T
authors.lastname: Chai
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005246:Feedback; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors