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Summary: In this paper, a heuristic historian data‐based predictive control strategy is presented and used to control a water distribution system simulated using the EPANET software, in particular, the Richmond water distribution system. The control actions are computed based on past historian data. The historian stores closed‐loop operation data of the process with different controllers used in the past, which may not provide sufficient information for a precise system nor controller identification. The proposed predictive controller computes the current control actions as a weighted sum of past control actions so that an estimation of the performance cost over a prediction horizon is minimized. Only a subset of the past control actions in the historian close to the current state of the process is considered in the current control computations to carry out a local linearization. This predictive strategy is well suited to control applications of large and complex processes for which it is difficult to carry out identification experiments such as water distribution systems. In the application example, the trajectories of a set of relay controllers are used through the proposed approach to take into account pressure constraints and periodic references. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: This brief shows how a min–max MPC with bounded additive uncertainties and a quadratic cost function results in a piecewise affine and continuous control law. Proofs based on properties of the cost function and the optimization problem are given. The boundaries of the regions in which the state space can be partitioned are also treated. The results are illustrated by an example. [Copyright &y& Elsevier]