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Iterative learning model predictive control (ILMPC), endowed with the merits of iterative learning control and model predictive control, has excellent abilities of disturbance rejection and constraints handling. It has been widely applied in regulation of industrial batch processes for its remarkable reference tracking performance. In practice, the presence of strong system nonlinearity fundamentally challenges the model mismatch cyclewise invariance assumption that underlies numerous synthesis of many ILMPC methods. It may further induce additional conservatism and thus ultimately leads to performance degradation. To circumvent this issue, in this paper a novel ILMPC scheme relying upon past error compensation from multiple time instances is presented, in which the associated weights are adaptively optimized, thereby resulting in considerable enhancement of robustness. These superior performances are confirmed by numerical experiments on injection molding process, a typical batch process. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) has been successfully applied to numerous batch processes over the past decades. Monotonic convergence of tracking error is a desired characteristic that attracts much attention in academia. Many factors arising in industrial practice, such as strong nonlinearity and parameter uncertainty, have challenged the most existing monotonically convergent ILC approaches. This motivates the development of the nonlinear monotonically convergent ILC (NMC-ILC) in this paper. The proposed NMC-ILC is an optimization-based control strategy, in which the original nonlinear process is linearly approximated to reduce the complexity of the optimization problem accompanied. An upper bound of the tracking error is derived by considering the effects of model mismatches that consist of two components: structural mismatch induced by the linearization and parametric mismatch arising from the indetermination of process parameters. The NMC-ILC is then devised by minimizing this upper bound. Numerical experiments are conducted on an ILC-testing benchmark and the velocity control in an injection molding process. [ABSTRACT FROM PUBLISHER]

Online system identification provides a powerful tool to process control engineers for controller synthesis, process dynamics monitoring, real-time optimization and other purposes at a low computational cost. Instead of processing data in time, this paper intends to propose processing data in a different “direction” – in iteration/batch to improve the estimates tracking performance. However, directly changing the data processing direction gives rise to severe fluctuations on parameter estimates within a batch. To overcome this problem, two online identification methods with simple implementations are devised based on average. One method is applying average in the dual space, while the other in the primal space. The convergence of both approaches has been analyzed. An adaptive average strategy based on moving-window is also developed to track inter-batch dynamics drift. Finally, the simulation results on injection molding, two-tank system and continuous stirred tank reactor (CSTR) testify the improved performance of the methods proposed in this paper. [ABSTRACT FROM AUTHOR]

It is of great importance to develop an online modeling method for chemical processes operated in closed loop for better understanding, monitoring the process or other purposes without endangering the system. This paper intends to devise an online system identification method, particularly for the batch process, by fully exploiting its intrinsic repetitiveness. It properly uses the information from the time direction and the batch direction, thus leading to a gradual performance enhancement. In addition, the identification method formulates the priori controller knowledge such as closed-loop stability as optimization constraints to refine the parameter estimates. A trust region method is employed to overcome the significant computation burden of directly handling these constraints such as solving Lyapunov inequalities. An adaptive filter is introduced to further smooth the parameter estimates. Finally, the effectiveness of the approach is verified by three numerical examples including a two-tank system. [ABSTRACT FROM AUTHOR]

This paper studies an online identification algorithm for batch processes incorporating priori process knowledge of pole and zero positions. The knowledge is available to control engineers and can be exploited to improve the accuracy of the identified process model. To reduce the computation burden of directly invoking Lyapunov inequality, a bound on the identified parameters is imposed to enforce the match between the priori knowledge and identified model. The bound is recursively calculated according to the newly obtained model. The proposed identification method uses the information not only from the time direction but also along the batch direction to improve the identification performance from batch to batch. A filter is introduced to suppress the variation on the identified parameters. Finally, numerical simulations verify the performance and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a constrained two dimensional recursive least square system identification method. [•] Incorporate a penalty term into cost function to smooth identified parameters. [•] Study the weights selection on the penalty term. [ABSTRACT FROM AUTHOR]