Intelligent Predictive Control Algorithms And Its Application

Control systems of modern industrial processes become large and complex recently, It tends to satisfy the more and more rigorous requirements in products process. The traditional PID control strategy is unable to solve the control problems of complex production processes with the multi-variable, highly non-linear. With the development of self-adapt control and self revise control, we can resolve these uncertainty problem, but it is indispensability to establish system model. Model predictive control is a form of control that developed rapidly and gained extensive application in recent years. Conquering the traditional control idea model predictive control(MPC) establish object model through the input-output characteristic.The fore two chapters of this paper, It completely reviewed the development of model predictive control and analyzed the main types of model predictive control in the literature. Simulation aimed at two-stairs , two-stairs with pure lag and Pendulum object with each MPC arithmetic, and validate the anti-jamming character.A neural network predictive control simulation was making, Aimed at a diesel oil blending process which is a strong non-linearity, multi-perturbation, MIMO system, a linear Programming was applied on optimal calculation. Simulation results show that the predictive control strategy can takes on the good performances of both set-point tracking and anti-perturbations.In the forth and fifth chapter, integrated with OPC and DDE technique, narrate the collection method of real time data and history data, MPC and it's optical Arithmetic was done with the collecting data. Collection data was send to computer through DDE data exchange manner. Afterwards, we can using abundance resource of PC and deal with those data.At last, the future research and development of the theory and application in this area are also presented.