Adaptive control of nonlinear MIMO systems with transport delay: conventional, rule based or neural?

We discuss the problem of control of zero order MIMO nonlinear systems with transport delay. This problem appears in numerous manufacturing control applications that are characterized with slow dynamics which can be ignored with respect to the sampling rate. We propose three alternative indirect adaptive control algorithms: 1) a conventional application of adaptive control based on a linearized model combining the Kalman filter estimation of a linearized (Jacobian) model with constrained optimization; 2) an intelligent control derived from conventional adaptive control with a linearized model (the first approach) that is integrated with a fuzzy rule-base; and 3) a neural net model (multilayer perceptron) online learning approach. Control updates are calculated by applying a constrained optimization algorithm. All three algorithms are compared and evaluated through simulation of a nonlinear plant with significant transport delay