Abstract
In order to derive the feasible control law of the constrained model predictive control scheme, quadratic programming has been introduced as an effective method. It is known that the typical performance index for model predictive control strategies under various constraints can be converted into a standard quadratic programming problem; however, there may be no feasible solutions for the corresponding quadratic programming problem when the working conditions are too bad or constraints are too rigorous, the real-time control law cannot be updated and the system performance may be deteriorated. To cope with such problems, an improved quadratic programming problem in which relaxations are employed to increase the possibility of successful solutions is proposed for the constrained dynamic matrix control approach in this paper. By adopting the introduced relaxations, more degrees of relaxations are provided for the optimization process under the case of over-constrained, such that the control law is easier to yield. Case study on the temperature regulation of the coke furnace demonstrates the validity of the improved quadratic programming structure–based dynamic matrix control strategy. Simulation results show that the proposed scheme yields improved control performance.
Highlights
Constraints exist widely due to the actual working conditions, the limitation of the actuators and so on in practice, and considering the relevant constraints during the controller design is necessary and meaningful.[1,2,3,4] When the process is over-constrained, there may be no feasible solutions for the corresponding controller
Model predictive control (MPC) strategies have been applied to various industries successfully, and constraints handling are the common problems during the MPC design.[6,7]
In order to avoid the situation that the quadratic programming (QP) problem may be unsolvable, we introduce the relaxations to reconstruct the QP problem
Summary
Constraints exist widely due to the actual working conditions, the limitation of the actuators and so on in practice, and considering the relevant constraints during the controller design is necessary and meaningful.[1,2,3,4] When the process is over-constrained, there may be no feasible solutions for the corresponding controller. Model predictive control (MPC) strategies have been applied to various industries successfully, and constraints handling are the common problems during the MPC design.[6,7] To obtain the optimal control law of the MPC approaches, many effective methods are studied.[8,9,10,11] By using the novel QP algorithm in which relaxations are employed to reconstruct the relevant cost function and constraints, the possibility of successful solving for real-time optimal control laws for the constrained MPC approaches is increased.
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