Point-to-point iterative learning model predictive control

Oh, Se-Kyu; Park, Byung Jun; Lee, Jong Min

doi:10.1016/j.automatica.2017.11.010

Cited by 42 publications

(7 citation statements)

References 20 publications

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“…For LEMPC, a predictive plant is employed to predict future system outputs [29], [30]. In this paper, the single-track vehicle model is introduced to build the predictive plant.…”

Section: Predictive Plant and Dynamic Model A Predictive Plant Fmentioning

confidence: 99%

Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

et al. 2019

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Model predictive control (MPC) is advantageous for designing an electrical vehicle path-tracking controller, but the high computational complexity, mathematical problem, and parameterization challenge adversely affect the control performance. Hence, based on a fully actuated-by-wire electrical vehicle (FAW-EV), a novel path-tracking controller based on improved MPC with a Laguerre function and exponential weight (LEMPC) is designed. The massive optimization control parameters of MPC with a long control horizon are reduced by introducing a fitting orthogonal sequence consisting of Laguerre functions, thereby substantially reducing the computational complexity without sacrificing the tracking accuracy. An exponential weight with decreasing characteristic is introduced to MPC to solve the mathematical problem, thereby improving the robustness of the path tracking controller. In addition, the parameterization access for online adjusting path tracking control performance can be provided by the proposed method. The path tracking motion realization for FAW-EV is subsequently illustrated. Finally, several simulations are implemented to verify the advantages of the proposed method. INDEX TERMS Path track, electrical vehicle, model predictive control (MPC), Laguerre function, exponential weight.

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“…For LEMPC, a predictive plant is employed to predict future system outputs [29], [30]. In this paper, the single-track vehicle model is introduced to build the predictive plant.…”

Section: Predictive Plant and Dynamic Model A Predictive Plant Fmentioning

confidence: 99%

Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

et al. 2019

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“…By contrast, an explicit solution is generally impossible to have if some other physical constraints are imposed, but a numerical one may still be obtainable with affordable computation. 40 Although discussion thus far has focused on tracking problems, ILMPC can also be tailored to cater for other problems, such as point-to-point tracking problems 34 and economic optimization problems. 41 3.2.…”

Section: Learning On Control Inputmentioning

confidence: 99%

“…Indeed, the functional J can be tailored to serve different customized demands in reality. For instance, it can be (i) some metrics quantifying the discrepancy between a given reference r and process output y over a prediction horizon or a subset of time points thereof when perfect tracking may not be attainable, 34,35 (ii) some indicators of economic interest to be maximized, 2,36,37 (iii) a Dirac function on the terminal process output y k,N|t focusing on final product quality, (iv) reaction time requiring minimization, 38 or (v) distributional metrics in order to find the most desirable shape of crystal size distribution. 15 Learning can be intuitively interpreted as improvement from past trials, enabling us to define it mathematically.…”

mentioning

confidence: 99%

110th Anniversary: An Overview on Learning-Based Model Predictive Control for Batch Processes

Cao

Zhao

et al. 2019

Ind. Eng. Chem. Res.

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Batch processes repeatedly execute a given set of tasks over a finite duration, whose versatility and ability to adapt to rapidly changing markets make it prevalent in a multitude of industrial fields, particularly in the era of “smart manufacturing”. Nevertheless, frequent switching and wide-ranging facility operations incur significant nonlinearity and time variability in process dynamics, both of which constitute remarkable challenges toward the regulation of batch processes. Among the various regulatory schemes, the integration of model predictive control and iterative learning schemes stands out, because of its inheritance of the merits of both: (i) ease of handling physical constraints and (ii) utilizing the repetitive operation pattern to adjust control input, process variables, and reference to improve control performance, consequently enhancing product quality. This review intends to account the recent technical advancements during the past two decades, from the perspective of the three different levels of learning mechanisms: control input, model parameter, and tracking reference. We conclude by providing insights into future research.

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“…In practice, most existing ILC strategies cannot handle real-time disturbances within batches. − To ensure system performance along both the batch and time axes, ILC should be combined with a real-time feedback control. − Model predictive control (MPC) is a popular feedback optimization control technique. Kwon et al initially developed a new run-to-run model predictive controller (R2R-based MPC) for batch crystallization processes .…”

Section: Introductionmentioning

confidence: 99%

Improved Two-Dimensional Design of Iterative Learning Predictive Functional Control for Batch Processes

Liu,

Ma,

Yang

2024

Ind. Eng. Chem. Res.

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The conventional iterative learning predictive functional control (ILPFC) scheme has been widely investigated for controlling batch processes. However, these schemes fail to effectively integrate key components, such as the prediction model, reference trajectory, error compensation, basis function, and objective function, resulting in suboptimal control performance. To improve the deficiencies of conventional schemes, this paper proposes an improved two-dimensional ILPFC control scheme. By the introduction of a new reference trajectory, a dual-incremental two dimensional Fornasini−Marchesini (2D-FM) model is constructed, and a corresponding objective function is proposed based on the improved 2D model. Additionally, a multipoint error linear combination compensation approach is introduced for the compensation of uncertainties within the 2D framework. Comparison tests conducted on the injection molding process demonstrate that the improved scheme achieves synergistic integration of the above components. Compared with the conventional scheme, the improved scheme has smoother responses, faster convergence, and smaller tracking errors.

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Point-to-point iterative learning model predictive control

Cited by 42 publications

References 20 publications

Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

Electrical Vehicle Path Tracking Based Model Predictive Control With a Laguerre Function and Exponential Weight

110th Anniversary: An Overview on Learning-Based Model Predictive Control for Batch Processes

Improved Two-Dimensional Design of Iterative Learning Predictive Functional Control for Batch Processes

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