Adaptive iterative learning control based on unfalsified strategy for Chylla-Haase reactor

An adaptive iterative learning control based on unfalsified strategy is proposed to solve high precision temperature tracking of the Chylla-Haase reactor, in which iterative learning is the main control method and the unfalsified strategy is adapted to adjust the learning rate adaptively. It is encouraged that the unfalsified control strategy is extended from time domain to iterative domain, and the basic definition and mathematics description of unfalsified control in iterative domain are given. The proposed algorithm is a kind of data-driven method, which does not need an accurate system model. Process data are used to construct fictitious reference signal and switch function in order to handle different process conditions. In addition, the plant data are also used to build the iterative learning control law. Here the learning rate in a different error level is adjusted to ensure the convergent speed and stability, rather than keeping constant in traditional iterative learning control. Furthermore, two important problems in iterative learning control, i.e., the initial control law and convergence analysis, are discussed in detail. The initial input of first iteration is arranged according to a mechanism model, which can assure a good produce quality in the first iteration and a fast convergence speed of tracking error. The convergence condition is given which is obviously relaxed compared with the tradition iterative learning control. Simulation results show that the proposed control algorithm is effective for the Chylla-Haase problem with good performance in both convergent speed and stability.