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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.