This paper presents a new approach for the PID (proportional, integral, and derivative) multivariable controller design based on a neural network and a genetic algorithm. The multivariable PID controller design is divided in three steps. In the first step, a radial basis neural network is utilized for multivariable process identification. In the second step, the controller design is realized by an off-line procedure. This procedure is based on tuning of the PID controller gains by optimization with genetic algorithms aiming to control the model obtained by radial basis neural network. In the third step, the PID controller gains obtained with genetic optimization and neural model are validated in a practical process. Performance of this approach to multivariable control design is presented and discussed. The control algorithm design is validated of experimental way in a multivariable process called ball-and-plate. This process consists of a plate pivoted at its center such that the slope of the plate can be manipulated in two perpendicular directions. The basic control objective is to control the position of a free rolling ball on a plate, by applying tensions to two DC motors, according to the ball postion, as measured by vision system.
neural network; genetic algorithm; nonlinear identification; multivariable control; intelligent control; hybrid intelligent systems
