Multi-objective predictive control optimization with varying term objectives : a wind farm case study

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park

Model Predictive Control of Stochastic Linear Systems with Probability Constraints

This paper presents a strategy for computing model predictive control of linear Gaussian noise systems with probability constraints. As usual, constraints are taken on the system state and control input. The novelty relies on setting bounds on the underlying cumulative probability distribution, and showing that the model predictive control can be computed in an efficient manner through these novel bounds— an application confirms this assertion. Indeed real-time experiments were carried out to control a direct current (DC) motor. The corresponding data show the effectiveness and usefulness of the approach

Multiple-lane vehicle platooning based on a multi-agent distributed model predictive control strategy

Vehicle platooning became an interesting topic in the last years, many researchers and practitioners from the academia and industry trying to develop new theories and design appropriate control methods and communication methodologies in order to bring this concept as fast as possible on the roads. Since vehicles drive on multi-lane roads and highways, the subsequent paradigm was to treat vehicles as swarms, i. e., groups of vehicles that travel closely together on different lanes and are electronically connected. A step forward towards this new concept would be the design of multiple-lane platoons. As such, this paper proposes a multi-agent distributed model predictive control strategy for the longitudinal coordination of the vehicles in individual platoons and a classical PI control algorithm for the lateral control of each vehicle in the platoon w. r. t. its neighbors. The simulation results obtained in Matlab/Simulink and the performance analysis prove that the concept is viable

Decentralized predictive formation control for mobile robots without communication

Mobile robot formation control is one of the most important problems in multi-robot systems. A very challenging sub-problem is mobile robot platooning, which means that the mobile robots should follow each other and should maintain a safe distance between them. In order to avoid collisions in the platoon, the controllers have to be designed to ensure string stability, i.e., the spacing errors should not get amplified as they propagate upstream from robot to robot. This paper investigates two different decentralized model predictive control strategies for platoon guidance using only longitudinal changes for the mobile robots. Moreover, the simple solution for mobile robot platooning assumes that there is no communication between them and each mobile robot measures only the distance between itself and the one in front of it. Furthermore, several comparisons are made with classical proportional string stable controllers and a performance analysis is provided

Min-max coalitional model predictive control algorithm

This paper proposes a coalitional model predictive control algorithm with feasibility guarantees for systems with bounded additive uncertainties. This formulation, suitable for sub-systems coupled through the inputs, assumes the coupling variables as disturbances and ensures a robust consensus with minimum information exchange. The simulation results show that the coalitional method has similar behaviour to the fully centralized algorithm and improved performance with respect to the decentralized and the iterative min-max distributed model predictive controllers