The Intelligent Predictive Functional Control Method About Anode Baking Temperature

Anode baking temperature system is a MIMO control system which has cross-coupling, time-delay and nonlinear. In order to control multivariable system accurately, firstly it is needed to solve accuracy problem of system models. The method of system identification for particle swarm optimization (PSO) algorithm is used by contrast with traditional system identification. Finally it is needed to solve the multivariable coupling phenomenon in the Anode baking temperature system. We used the PSO algorithm to identification the multivariable model as the initial predictive models. A new predictive function decoupling control algorithm for the MIMO systems is proposed, that has been applied to the multivariable predictive functional decoupling control for the anode baking temperature.PSO algorithm will be used in model identification and parameter optimization of Anode baking temperature system. The second order plus time delay control model of the anode baking temperature is identified based on the data gathered from the anode baking furnace scene. The PSO algorithm turned system identification problems into optimization problems in parameter space by qualitatively analyze the scope of system parameter space, which can effectively avoid getting into local optimum and is used to obtain the optimal solution by searching in the whole parameter space in parallel. By the simulation and experiment results in the process of vacuum freeze-drying and Anode baking temperature system, we can see that the PSO algorithm has certain advantages about model identification, and its most attractive feature is simple and easy to implement and more powerful global optimization capability.The method about multivariable predictive function decoupling control algorithm mainly used the Prediction function control (PFC) technical characteristic. This algorithm can decompose the decoupling control problem of MIMO system into predictive function controls of several SISO systems. The control method sacrifices the overall performance as the cost to adopt to deal with coupled variables instead of the whole optimization, follow the reference trajectory and find a set of basis function weighting coefficients to get an analytical linear decoupling control equation, and then we can depose the multivariable system into many signal systems. The Anode baking temperature system is a nine-dimension multivariable system, which has the coupling in the flue. We mainly consider the adjacent flue coupling as the important coupling to use the multivariable prediction function decoupling control algorithm. The simulation and practice control results show that the proposed control system better control precision and robust properties than the tradition PID control system and is efficient and effective.