Internal Model Based Sampling Control Design And Learning Optimization For Industrial Processes With Input Delay

Input or output delays are ubiquitous in industrial processes.The existence of time delay has a great impact on the performance of the closed-loop system.Aiming at control system with input delay,the discrete-time domain PID controller design method and the iterative learning optimization based on the two-degree-of-freedom(2DOF)internal model control(IMC)structure are studied in this thesis.A discrete-time domain PID controller design method based on IMC structure is proposed for a stable process with input delay.Firstly,the design method of the IMC controller is given.Then the equivalent controller in the unit closed-loop is derived by using the equivalent relationship between the standard IMC structure and unit closed-loop structure.The Taylor series are used to expand the equivalent controller formula,and first three terms of that formula are taken to construct proportional,integral and derivative terms compared to the proposed PID controller.The PID controller can be tuned through one parameter to achieve a trade-off between the set-point tracking and load disturbance rejection.According to the first-order and second-order time delay models commonly used in the industry,the corresponding PID controllers are designed separately and the robust stability is analyzed using the small gain theorem.Meanwhile,the robust stability condition of the first-order time delay model under the gain uncertainty is derived.Illustrative examples in the literature show the advantages of the proposed algorithm.The iterative learning control(ILC)combined with 2DOF IMC structure is proposed for integral and unstable batch processes with input delay,so as to achieve complete tracking of the desired trajectory from batch to batch.The initial run adopts the 2DOF IMC scheme,and an ILC law is employed starting from the second batch to gradually achieve accurate tracking of the desired trajectory by using historical batch data.To ensure the convergence of the proposed algorithm,the iteration rate between current cycle and previous cycle is derived,and an iterative controller in the form of PD is also obtained.Furthermore,robust tuning conditions are proposed to deal with model uncertainty.Relative independence is therefore obtained for designing the 2DOF IMC to maintain the control system robust stability and the ILC control law to realize perfect tracking,respectively.Graphical numerical tuning guidelines are given to facilitate practical applications.Examples from the literature are used to demonstrate the effectiveness of the control method.To control the temperature in the fluidized bed dryer,the step-response identification algorithm is used to establish the integral time delay model for the fluidized bed heating-up process.The 2DOF IMC method is designed and implemented in LabVIEW.MatLab is used to make simulation of the identified model to determine controller parameters,and then those parameters are set to LabVIEW in the upper computer for experiments,which communicates with the fluidized bed through the communication interface.The experimental results of temperature control show that the proposed 2DOF IMC has a good control effect on the integral process with time delay.