Min–max MPC using a tractable QP problem

Min–max model predictive controllers (MMMPC) suffer from a great computational burden that is often circumvented by using approximate solutions or upper bounds of the worst possible case of a performance index. This paper proposes a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min–max problem is computed using a quadratic programming problem. The overall computational burden is much lower than that of the min–max problem and the resulting control is shown to have a guaranteed stability. A simulation example is given in the paper

A bounded positive nonlinear PI controller for double-pipe heat exchangers

"In this work, temperature regulation of double-pipe heat exchangers is considered. The positive (unidirectional) and bounded features of the flow rate input variable are taken into account in the analysis. As a result, a bounded positive PI-type control scheme, which achieves the regulation objective avoiding input saturation, is proposed. It turns out to be a simple algorithm that does not need to feed back the whole state vector, that does not depend on the exact value of the system parameters, and whose stabilization character is global in the closed-loop system state-space domain. Moreover, it may be applied to both flow configuration cases, i.e., countercurrent and parallel-flow heat exchangers. The analytical developments are corroborated through experimental and simulation results.