NEUROCONTROLLER FOR THERMAL SYSTEM WITH DEAD TIME AND MEASUREMENT LAG

Abstract

element of a given physical process to yield its desired response. In classical feedback control our main purpose is to increase the robustness for a control system, i.e., increase the degree to which the system performs when there is uncertainty. Adaptive control techniques have been developed for systems that must perform over a large range of uncertainties due to large variation in parameter values, environmental conditions and signal inputs. The objective of the design of an intelligent controller is similar to that for an adaptive control system. The object with intelligent control is to design a system with acceptable performance characteristics over a wide range of uncertainties. The term neurocontroller refers to a neural network based controller. The advantages of neural networks are two fold. One is its learning ability and another is its versatile mapping capabilities from input to output. Versatile mapping capabilities should provide a means of controlling complex systems which cannot be carried out well with conventional feedback controllers. The learning ability can reduce human effort in designing controllers and it even suggests in discovering better control schemes than presently known. The mathematical models of a simple thermal system with dead time and measurement lag have been developed for both first order system and second order system. The difference between the first order and second order system is that in the first order system no heat dissipates to the atmosphere, while in the second order system the heat dissipation to the atmosphere from tank is present. In first order system, outlet temperature depends upon the present and previous values of inlet temperature, outlet temperature, mass flow rate of fluid. The second order system depends upon these same parameters as well as atmospheric temperature. For feedback to the controller due to the