Author
J.R. Cueli
Bio: J.R. Cueli is an academic researcher from University of Seville. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 4, co-authored 8 publications receiving 137 citations.
Papers
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TL;DR: In this article, the authors present a new methodology applicable to batch processes called iterative nonlinear model predictive control (INMPC), which incorporates the ability of learning from past batches (known as iterative learning control or ILC) to an underlying nonlinear controller.
85 citations
TL;DR: In this article, the authors describe the application of a predictive controller that deals with measurable disturbances in the extraction process in an olive oil mill, where the raw material is prepared for the mechanical separation.
24 citations
07 Mar 2013
12 citations
TL;DR: In this paper, an Iterative Model Predictive Controller (inmpc) is proposed to control chemical batch reactors, which combines the good qualities of MPC with the possibility of learning from past batches, that is the base of Iterative Control.
9 citations
12 Dec 2005
TL;DR: In this paper, the authors presented the application of Iterative Nonlinear Model Predictive Control (INMPC) to a semibatch chemical reactor using a nonlinear model and a quadratic objective function that is optimized to obtain the control law.
Abstract: This paper presents the application of Iterative Nonlinear Model Predictive Control, INMPC, to a semibatch chemical reactor The proposed control approach is derived from a model-based predictive control formulation which takes advantage of the repetitive nature of batch processes The proposed controller combines the good qualities of Model Predictive Control (MPC) with the possibility of learning from past batches, that is the base of Iterative Control It uses a nonlinear model and a quadratic objective function that is optimized in order to obtain the control law A stability proof with unitary control horizon is given for nonlinear plants that are affine in control and have linear output map The controller shows capabilities to learn the optimal trajectory after a few iterations, giving a better fit than a linear non-iterative MPC controller The controller has applications in repetitive disturbance rejection, because they do not modify the model for control purposes In this application, some experiments with a disturbance in inlet water temperature has been performed, getting good results
4 citations
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TL;DR: A learning model predictive controller for iterative tasks is presented in this article, where a safe set and a terminal cost function are used in order to guarantee recursive feasibility and non-decreasing performance at each iteration.
Abstract: A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.
261 citations
TL;DR: In this article, the development of MPC theory and industrial applications are briefly reviewed and the limitations of current model predictive control theory and technology are analyzed, and the necessity to strengthen the MPC research with respect to enhancing its effectiveness, scientificness, and usability is pointed out.
254 citations
TL;DR: A novel Lyapunov function is proposed, based on which the conditions for ensuring the regional input-to-state practical stability are developed, and the proposed control strategy is applied to a spring-and-cart system to demonstrate the applicability and effectiveness.
Abstract: This paper investigates the predictive control scheme and the associated stability issue for the constrained nonlinear networked control systems (NCSs), where both the sensor-to-controller packet dropout and the controller-to-actuator packet dropout are considered simultaneously. The model predictive control based framework is proposed to compensate for the two-channel packet dropouts. This framework consists of two main aspects: 1) to design the control packets by solving a constrained optimization problem and 2) to synthesize an efficient packet transmission and compensation mechanism based on the Transmission Control Protocol. To study the stability of the resultant nonlinear NCS, we propose a novel Lyapunov function, based on which the conditions for ensuring the regional input-to-state practical stability are developed. Finally, the proposed control strategy is applied to a spring-and-cart system to demonstrate the applicability and effectiveness.
135 citations
TL;DR: In this article, a neural network-model predictive control (NN-MPC) algorithm was implemented to control the temperature of a polystyrene (PS) batch reactors and the controller set-point tracking and load rejection performance was investigated.
117 citations
TL;DR: In this article, the authors present a new methodology applicable to batch processes called iterative nonlinear model predictive control (INMPC), which incorporates the ability of learning from past batches (known as iterative learning control or ILC) to an underlying nonlinear controller.
85 citations