ADAPTIVE-OBSERVER AND NONLINEAR CONTROL STRATEGY FOR CHEMICAL REACTORS VIA NEURAL NETWORKS

An adaptive observer and nonlinear feedback control strategy with cons traints on control action are developed by using a supervised learning rule of a neural network and the theory of functional-link networks. The convergence of the adaptive observer and the stability of the cont rol system are pr oven. They are applied to the control of an exotherm ic stirred-tank reactor. It is shown that an adaptive observer for con centration can be constructed for a reaction system when only temperat ure measurements are available on line. An adaptive observer is used t o identify the pre-exponential Arrhenius constant and to provide on li ne estimation of the unmeasured reactant concentration for a nonlinear state-feedback controller. Simulations show that the combined observe r/controller provides satisfactory closed-loop behaviour, fast respons es and strong robustness. Estimated and actual concentration are in go od agreement. A nonlinear feedback controller can provide effective fe edback control over a wide range of operating conditions.