Constrained Control and Estimation

Goodwin, Graham C.; Doná, José A. De; Serón, María M.

doi:10.1007/b138145

Cited by 330 publications

(244 citation statements)

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“…Generic Predictive control problems [16,17] are based on solving finitehorizon constrained optimisation problems. Let us discuss how the predictive control problem can be formally stated in a TS framework.…”

Section: Fuzzy Predictive Control: Problem Statementmentioning

confidence: 99%

Shape-independent model predictive control for Takagi–Sugeno fuzzy systems

Ariño

Querol

Sala³

2017

Engineering Applications of Artificial Intelligence

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Predictive control of TS fuzzy systems has been addressed in prior literature with some simplifying assumptions or heuristic approaches. This paper presents a rigorous formulation of the model predictive control of TS systems, so that results are valid for any membership value (shape-independent) with a suitable account of causality (control can depend on current and past memberships and state). As in most fuzzy control results, a family of progressively better controllers can be obtained by increasing Polya-related complexity parameters, generalising over prior proposals.

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Section: Fuzzy Predictive Control: Problem Statementmentioning

confidence: 99%

Shape-independent model predictive control for Takagi–Sugeno fuzzy systems

Ariño

Querol

Sala³

2017

Engineering Applications of Artificial Intelligence

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“…Receding horizon optimisation has become a very successful strategy in real time control problems [11,19,8,23,18]. The basic idea is that one solves a fixed horizon optimisation problem to compute a sequence of predicted inputs over some prediction horizon (say T time steps) but one only implements (or stores) the first step.…”

Section: Application Of Receding Horizon Control To Mine Planningmentioning

confidence: 99%

“…Note that various simplifications are known to work well in the process control case [11,19,8,23,18]. For example,…”

Section: Application Of Receding Horizon Control To Mine Planningmentioning

confidence: 99%

“…Specifically, we define the mine state as a collection of pit depths at a certain number of surface locations and we represent the evolution of this state via a dynamic model that uses mining action as control input. We then use receding horizon optimal control [11,19,8,23,18] to solve the mine planning problem under constraints. This leads to several advantages, some of which are explained in the main body of the paper.…”

Section: Introductionmentioning

confidence: 99%

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Receding horizon control applied to optimal mine planning

et al. 2006

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In this paper we show that the problem of optimal mine planning can be cast in the framework of receding horizon control. Traditional formulations of this problem have cast it in the framework of mixed integer linear programming. In this paper, we present an alternative formulation of the mine planning problem using the "language" of control engineering. We show that this alternative formulation gives rise to new insights which have the potential to lead to improved computational procedures. The advantages are illustrated by an example incorporating many practical features of an actual mine planning problem.

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“…A possible way to deal with constraints is to use unconstrained design methods combined with a "cautious" approach of operating far from the constraints (e.g., imposing large distances between the vehicles in the formations). But, such procedures would, in general, reduce the performance that would be achievable by operating closer to the limits [12]. Another possible approach is to push the trajectory away from the constraints by penalizing a vehicle close to an obstacle or other vehicle (e.g., potential field approaches, or other optimization-based approaches that do not impose explicitly the constraints), but these methods do not guarantee that the constraints will be satisfied.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Vehicle Formations

Fontes

Caldeira

2009

Optimization and Cooperative Control Strategies

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Abstract. We propose a two-layer scheme to control a set of vehicles moving in a formation.The first layer, the trajectory controller, is a nonlinear controller since most vehicles are nonholonomic systems and require a nonlinear, even discontinuous, feedback to stabilize them. The trajectory controller, a model predictive controller, computes centrally a bang-bang control law and only a small set of parameters need to be transmitted to each vehicle at each iteration.The second layer, the formation controller, aims to compensate for small changes around a nominal trajectory maintaining the relative positions between vehicles. We argue that the formation control can be, in most cases, adequately carried out by a linear model predictive controller accommodating input and state constraints. This has the advantage that the control laws for each vehicle are simple piecewise affine feedback laws that can be pre-computed off-line and implemented in a distributed way in each vehicle.Although several optimization problems have to be solved, the control strategy proposed results in a simple and efficient implementation where no optimization problem needs to be solved in real-time at each vehicle.

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Constrained Control and Estimation

Cited by 330 publications

References 0 publications

Shape-independent model predictive control for Takagi–Sugeno fuzzy systems

Shape-independent model predictive control for Takagi–Sugeno fuzzy systems

Receding horizon control applied to optimal mine planning

Model Predictive Control of Vehicle Formations

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