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The work deals with composite iterative learning model predictive control (CILMPC) for uncertain batch processes via a two dimensional Fornasini–Marchesini (2D-FM) model. A novel equivalent error system is first presented which is composed of state error and tracking error. Then an iterative learning predictive updating law is constructed by 2D state feedback control and the 'worst' case linear quadratic function is also designed. Besides, the update controller considering the input and output constraint will be optimized using the worst-case objective function along the infinite moving horizon. The solvable conditions that can be optimized online in real time are constructed using linear matrix inequalities (LMIs). The stability of the proposed control scheme can be achieved with the feasibility of the optimization problem. Compared with robust traditional MPC using one-dimensional models, the presented control approach can guarantee more degrees of tuning to achieve faster convergence of tracking error, which is of more significance since uncertainties exist inevitably in industrial batch processes. Finally, an injection molding process and a three-tank are introduced as two cases to demonstrate the feasibility and superiority of the proposed MPC strategy. • A 2D iterative learning model predictive control (MPILC) scheme is introduced. • The process state and tracking dynamics are both regulated. • Improved control performance is obtained under both model/plant match and mismatch cases. [ABSTRACT FROM AUTHOR]

To achieve improved control performance of batch processes under uncertainty, a novel two-dimensional model predictive iterative learning control (2D-MPILC) scheme is proposed. First, a new two-dimensional (2-D) extended nonminimal state space model is formulated where more degrees of freedom are offered for further controller design; second, a new error compensation strategy is introduced in the controller design to improve the ensemble control performance. The two merits are combined together to form a new control strategy where the model predictive control and iterative learning control are united based on the 2-D framework. By employing the novel model formulation and the system error compensation approaches, the effects caused by uncertainty in the batch processes can be eliminated gradually from cycle-to-cycle such that the desired control performance will be obtained finally. The effectiveness of the proposed 2D-MPILC is tested on the packing pressure in the injection molding batch process. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new two‐dimensional (2D) fuzzy composite iterative learning fault‐tolerant control strategy using a 2D Takagi‐Sugeno fuzzy model is proposed for batch processes with time delay and actuator faults. Firstly, based on the local‐sector nonlinearity method, a 2D Takagi‐Sugeno fuzzy model representing the nonlinear batch process with actuator faults is constructed with a series of linear models and nonlinear membership functions. Then, a 2D fuzzy feedback control–based iterative learning fault‐tolerant control strategy is proposed under the constructed model. Using the 2D Lyapunov stability theory, sufficient conditions for system asymptotic stability are given. The fault‐tolerant control law is then designed, guaranteeing system asymptotic stability along time and batches even if the system fails. Finally, the traditional control algorithm, the pure iterative learning control algorithm, and the feedback control–based iterative learning control algorithm proposed in this paper are compared on the level control of the three‐tank system, which proves the effectiveness of the proposed method. [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]

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process. [ABSTRACT FROM AUTHOR]

A robust design of a hybrid iterative learning fault-tolerant guaranteed cost control scheme is proposed for a class of multi-phase batch processes under faults and disturbances. Firstly, based on an equivalent two-dimensional Fornasini-Marchesini (2D-FM) switched system with actuator faults varying within an allowable range, a 2D robustly hybrid controller that includes a robust hybrid extended feedback control to ensure performance over time and a hybrid iterative learning control to improve the tracking performance from cycle to cycle is formulated to guarantee the closed-loop convergence and the H∞ performance level with a cost function bearing the upper bounds for all admissible uncertainty and actuator failures. Secondly, 2D system theory and the average dwell time strategy are adopted to derive conditions for guaranteeing exponential stability of the corresponding system in terms of linear matrix inequalities (LMIs), where the suboptimal hybrid guaranteed cost controller, which minimizes the quadratic performance index and rejects external disturbances, is designed using a convex optimization under LMI constraints. Finally, the proposed method is further verified by simulation on an injection molding process in comparison with traditional methods. [ABSTRACT FROM AUTHOR]

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC. [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]

This paper presents a new state space model design of linear quadratic (LQ) tracking control scheme using genetic algorithm (GA) optimization for batch processes under partial actuator faults. To develop the LQ control, the process model is first transformed into a new state space formulation that includes both the process state and the output tracking error dynamics. Then the subsequent LQ control is formulated. As analytical tuning rules of the elements in the weighting matrices are not known, a GA is introduced to optimize these elements to achieve the desired closed-loop process responses. Simulation on several processes demonstrates that the proposed LQ scheme obtains the design objective well with improved control performance. [ABSTRACT FROM AUTHOR]

Highlights: [•] A state space model predictive fault-tolerant control for batch processes with partial actuator faults is proposed. [•] The design is applied to injection velocity control. [•] Improved closed-loop control performance is achieved with unknown disturbances and actuator faults. [ABSTRACT FROM AUTHOR]

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]

• A two-dimensional switched model is developed. • A switching controller with greater robustness and lower conservatism is developed. • Sufficient conditions for system stability are provided based on the robust positive invariant set and terminal constraint set. To guarantee the control performance of batch processes under time delays and actuator failures, a novel iterative learning model-based predictive fault-tolerant control approach is developed. First, a two-dimensional switched model was developed by linearizing the system at multiple operating points so as to describe the actual process with and without actuator faults. Second, a switching controller with greater robustness and diminished conservativeness was developed based on this switched model. Compared to conventional fault-tolerant control methods, the switching controller has stronger fault-tolerant ability, less conservativeness, and higher energy efficiency. Third, by integrating the Lyapunov-Razumikhin function (LRF) method, sufficient conditions for system stability could be provided based on the robust positive invariant and terminal constraint sets. Solving the stability conditions could then allow us to obtain the control law gains under faulty or normal cases. Compared to the existing Lyapunov-Krasovskii function method (LKF) method, the developed stability conditions were simpler, less complex, and less conservative. Finally, the effectiveness of the proposed method is tested on a continuous stirred reactor (CSTR). [ABSTRACT FROM AUTHOR]

Although conventional two-dimensional model predictive iterative learning control (2D-MPILC) based on an extended non-minimum state space (ENMSS) model can avoid designing an observer, it only relies on feedback to passively deal with time delay. This passive treatment for time delay hinders the further improvement of control performance. To address this shortcoming, a two-dimensional model predictive iterative learning control strategy based on the set point learning (2D-SPL-MPILC) is proposed. Firstly, a set point learning strategy is developed to improve the ability to deal with time delay. Based on the error of the previous batch, the set point learning strategy perceives the system dynamics in advance, and then outputs this advance perception in the form of the predictive set point. Secondly, a novel ENMSS model is constructed on the basis of the predictive tracking error between the predictive set point and the actual output. Since the predictive tracking error integrates the predictive set point, this novel ENMSS model has certain predictive ability. Finally, based on this novel ENMSS model, a novel two-dimensional model predictive iterative learning control (2D-MPILC) method, which is called 2D-SPL-MPILC method, is designed. Because this controller contains the perception of future process dynamics, it can reduce the impact of time delay and improve the control performance. The case studies on the packing pressure control in injection molding process and batch reactors demonstrate the effectiveness of the presented 2D-SPL-MPILC strategy. • a set point learning strategy is developed. • a novel extended non-minimum state space (ENMSS) model is developed. • a two-dimensional model predictive iterative learning control (2D-MPILC) method is designed. [ABSTRACT FROM AUTHOR]