A robust integrated predictive iterative learning control based on updating reference for point-to-point tracking

A novel combination of iterative learning control (ILC) and model predictive control (MPC), referred to here as model predictive iterative learning control (MPILC), is proposed for glycemic control in type 1 diabetes mellitus. MPILC exploits two key factors: frequent glucose readings made possible by continuous glucose monitoring technology; and the repetitive nature of glucose-meal-insulin dynamics with a 24-h cycle. The proposed algorithm can learn from an individual's lifestyle, allowing the control performance to be improved from day to day. After less than 10 days, the blood glucose concentrations can be kept within a range of 90-170 mg/dL. Generally, control performance under MPILC is better than that under MPC. The proposed methodology is robust to random variations in meal timings within +/-60 min or meal amounts within +/-75% of the nominal value, which validates MPILC's superior robustness compared to run-to-run control. Moreover, to further improve the algorithm's robustness, an automatic scheme for setpoint update that ensures safe convergence is proposed. Furthermore, the proposed method does not require user intervention; hence, the algorithm should be of particular interest for glycemic control in children and adolescents.

Iterative learning control (ILC) is often used to eliminate repetitive disturbances and improve the tracking performance in the industrial processes. Due to its disability of reacting against real-time disturbances, the integration of model predictive control (MPC) and ILC constitutes the model predictive iterative learning control (MPILC) to improve its ability of rejecting non-repetitive disturbances. MPILC is suitable for linear models which has poor performance for highly nonlinear systems. A nonlinear MPILC (NMPILC) global controller based on T-S model is put forward in this paper. The global controller is composed of a set of local MPILC controllers designed applying the local linear models of the T-S model. The convergence property has been demonstrated. A PWR nuclear power plant is adopted to illustrate the performance of the NMPILC.

Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Control engineering approaches may be applied to irrigation management to make better use of available irrigation water. These methods of irrigation decision-making are being developed to deal with spatial and temporal variability in field properties, data availability and hardware constraints. One type of control system is advanced process control which, in an irrigation context, refers to the incorporation of multiple aspects of optimisation and control. Hence, advanced process control is particularly suited to the management of site-specific irrigation. This paper reviews applications of advanced process control in irrigation: mathematical programming, linear quadratic control, artificial intelligence, iterative learning control and model predictive control. From the literature review, it is argued that model-based control strategies are more realistic in the soil–plant–atmosphere system using process simulation models rather than using ‘black-box’ crop production models. It is also argued that model-based control strategies can aim for specific end of season characteristics and hence may achieve optimality. Three control systems are identified that are robust to data gaps and deficiencies and account for spatial and temporal variability in field characteristics, namely iterative learning control, iterative hill climbing control and model predictive control: from consideration of these three systems it is concluded that the most appropriate control strategy depends on factors including sensor data availability and grower’s specific performance requirements. It is further argued that control strategy development will be driven by the available sensor technology and irrigation hardware, but also that control strategy options should also drive future plant and soil moisture sensor development.

2D MODEL PREDICTIVE ITERATIVE LEARNING CONTROL SCHEMES FOR BATCH PROCESSES

A novel optimal energy management strategy (EMS) for plug-in hybrid electric vehicle (PHEV) is proposed in this paper, which takes the battery health into consideration for prolonging its service life. The integrated control framework combines batch-wise iterative learning control (ILC) and time-wise model predictive control (MPC), referred to as 2D-MPILC. The major advantages of the proposed method are shown with better performance as well as faster convergence speed by taking into account the time-wise feedback control within the current batch. Then, the MPILC method is applied for the PHEV with the ability to make continuous period-to-period improvements. Its performances will approach dynamic programming (DP)-based method after a learning process with satisfying real-time processing capacity. The results in real-world city bus routines verify the effectiveness of the proposed EMS for greatly improving the performance of the PHEV.

In this paper, an integrated tube robust iterative learning model predictive control (Tube‐RILMPC) strategy based on the dynamic partial least squares (DyPLS) identified algorithm is proposed. The problems of large amount of online data calculation and input and output variable dimensions disaster in the original variable space are solved. This integrated Tube‐RILMPC strategy enhances the tracking performance and robustness of two‐dimensional (2D) control system. The output trajectories of system are located in the tube the nominal trajectory, which reduces the modelling error caused by the uncertain model. Based on the worst‐case performance index of ellipsoidal uncertainty and polytopic uncertainty, a robust iterative learning control (ILC) strategy is designed. Finally, the superiority of the proposed control algorithm is verified by comparative simulation.

Latent variable iterative learning model predictive control for multivariable control of batch processes

Model predictive control (MPC) is a powerful technique that can be used for various applications, including trajectory tracking. However, MPC requires an accurate model of the system and a long enough prediction horizon to achieve good performance. Although variations of MPC like stochastic MPC and robust MPC can alleviate the problem to a certain degree, they still more or less rely on the knowledge of system models, disturbance and noise. MPC can also suffer from high computational cost or loss of recursive feasibility, particularly in the case of the existence of model mismatch (actual system model is different from the model used by the controller). Iterative learning control (ILC) is another technique that can be used for improving system performance (reducing tracking error) for repetitive tasks. ILC is known for its capability of learning from previous history (iterations of tasks) and less reliance on the model accuracy. In this paper, both MPC and different ILC algorithms are employed for a trajectory tracking problem with model mismatch. The performances of different algorithms are then analyzed and compared. A criterion on the degree of model mismatch to guarantee error convergence is also discussed for model-based ILC.

This work focuses on the model-free iterative learning control (ILC) design with good transient performance and fast convergence rate that is employed for precision control of a wafter stage. First, the experimental setup, the mathematical model, and the desired reference are introduced. Notice that the desired reference and its internal model (IM) are known in advance. Then, based on the internal model principle (IMP), a new ILC scheme, named as 1M-based ILC, is proposed by incorporating the 1M of the reference. The proposed 1M-based ILC is model-free, that is, independent on the system model of the controlled plant. The 1M -based ILC is carefully constructed to track the reference along the time axis while tracking/rejecting repetitive variations along the iteration axis. Precisely because of the tracking capability along the time axis in every iteration, the 1M-based ILC can achieve satisfactory performance even in the 0th iteration and improve the transient performance and the convergence speed. In addition, the 2-D $H_{\infty}$ theory is used to establish the design criterion for the 1M-based ILC. Finally, in the experiments, the efficiency of the 1M-based ILC is illustrated by comparing with a P-type ILC and a model-based one.

A 2D-FM model-based robust iterative learning model predictive control for batch processes

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

ABSTRACTIn this article, to tackle with the iteration-varying trail lengths and random initial state shifts, an average operator-based PD-type iterative learning control (ILC) law is firstly presented for linear discrete-time multiple-input multiple-output (MIMO) systems with vector relative degree. The proposed PD-type ILC law includes an initial rectifying action against initial state shifts, and pursues the reference trajectory tracking beyond the initial time points. As special cases of the PD-type ILC law, P-type and D-type ILC laws are then introduced. It is proved that for linear discrete-time MIMO systems with vector relative degree, the three proposed ILC laws can drive the varying trail lengths-based ILC tracking errors to zero in mathematical expectation beyond the initial time points. A numerical example is used to illustrate the effectiveness of the proposed ILC laws.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.