An improved method of DHP for optimal control in the clarifying process of sugar cane juice

Clarifying process of sugar cane juice is a dynamic nonlinear system which has the characteristics of strong non-linearity, multi-constraint, large time-delay, multi-input and other characteristics of complex nonlinear systems. In this paper, Elman neural network is applied to the model of the clarifying process of sugar cane juice. An improved method of dual heuristic programming (DHP) affiliated to the approximate dynamic programming (ADP) family is employed to the optional control of neutralized pH value and purified juice pH value in clarifying process of sugar cane juice. The main advantage of this method is to add an ldquoapproximaterdquo neural network, which takes use of the states and actions during delay-time of the system to calculate partial derivative of state with respect to action, consequently it overcomes the difficulty of using the system model to calculate the partial derivative of state with respect to action because of the large lagging time. As a result the DHP algorithm is more suitable for real-time process control and online application. Finally, the results of the simulation indicate that the improved DHP method has good control effect.