Design and experimental evaluation of an evolutionary neural-net based PID controller

PID control schemes have been widely used for most industrial processes which are represented by nonlinear systems. This paper presents a new design scheme of evolutionary neural-net based PID controllers, whose connection weights are adjusted by using a real-coded genetic algorithm. The newly proposed control scheme is numerically evaluated on a simulation example, and experimentally evaluated on a pilot-scale temperature control system.