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.

Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework

Model Predictive Control — Status and Challenges

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.

Network-Based Predictive Control for Constrained Nonlinear Systems With Two-Channel Packet Dropouts

Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation

Iterative nonlinear model predictive control. Stability, robustness and applications

Predictive controller with estimation of measurable disturbances. Application to an olive oil mill

Iterative Nonlinear Model Predictive Control

Application of iterative nonlinear model predictive control to a batch pilot reactor

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

/pdf/iterative-nonlinear-control-of-a-semibatch-reactor-stability-5a3iliqc8c.pdf

J.R. Cueli

Papers

Iterative nonlinear model predictive control. Stability, robustness and applications

Predictive controller with estimation of measurable disturbances. Application to an olive oil mill

Iterative Nonlinear Model Predictive Control

Application of iterative nonlinear model predictive control to a batch pilot reactor

Iterative Nonlinear Control of a Semibatch Reactor. Stability Analysis