2D Iterative Learning Predictive Fault Tolerant Control For Batch Processes With State Time Delay

Batch process with its high added value and multi varieties has become a popular production mode,attracting a large number of domestic and foreign experts to study it.With the improvement of industrialization level,the possibility of system fault is greatly increased,and the fault has a great impact on the stability of the system.Time delay is easy to make the production delay and affect the production efficiency.Predictive control can predict the optimal control law at the next time,so that the system can find a better state.Therefore,the research on predictive fault-tolerant control of batch process under the influence of time delay and fault has an important effect on efficient production.The main work of this paper is as follows:For the batch process with state delay and actuator fault,firstly,the state error and output tracking error are introduced,and the 2D state space model is transformed into the equivalent2D-Roesser closed-loop model by using iterative learning control theory.According to the designed optimization performance index and Lyapunov stability theory,the sufficient conditions for MPC to be solvable are given in the form of LMI,and the theoretical proof of system stability is given.Thus,a new fault-tolerant control method based on 2D constraint iterative learning predictive with time-delay is proposed.Finally,the simulation results show that the method is feasible and effective.The 2D robust model predictive fault-tolerant control is studied for batch processes with multiple delays,disturbances and actuator faults.Firstly,the equivalent 2D-Roesser system model with multiple time delays is established by introducing state error and output tracking error between batches and combining with iterative learning control law.On the basis of this model,the definition of invariant set is proposed,and the sufficient conditions for the state of this model to have invariant set property are established.Then,the predictive controller is designed and the performance index function with terminal constraints is selected to resist external interference,and some constraints are given.Lyapunov-Razumikhin function(LRF)is used to replace Lyapunov-krasovski function(LKF)to construct a predictive control system satisfying terminal constraints.The state of the system still has sufficient conditions of invariant set.Finally,compared with the traditional one-dimensional method,the simulation results show that the proposed method is feasible and effective.For the multi-phase batch process with multiple delays and actuator faults,firstly,combined with iterative learning control law,LRF is selected.By introducing the definition ofstate error and output error,the existing model is transformed into the equivalent 2D-Roesser state space model.On the basis of this model,the invariant set is defined,and the sufficient conditions for this model to have invariant set property are established.The mean residence time method is used to prove the exponential stability of the system.Then,the prediction model along time and batch direction is constructed,the prediction controller is designed and the performance index function with terminal constraints is selected to resist external interference,besides the update law and output constraints are given.Under these conditions,the sufficient conditions that the terminal constraint set of the prediction model is an invariant set are given,so the optimal control algorithm is constructed.Through the simulation of injection molding process,the feasibility of this method is proved.