MPC and PID control based on Multi-Objective Optimization

Gambier, Adrián

doi:10.1109/acc.2008.4587241

Cited by 59 publications

(44 citation statements)

References 22 publications

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“…This set is also called that of non-dominated solutions. When the non-dominated solutions are collectively plotted in the criterion space, they constitute the Pareto front (Gambier, 2008;Veldhuizen and Lamont, 2000). All points of the Pareto front are an equally acceptable solution for the problem.…”

Section: Design Of the Multiobjective Tuning Of Gpc Parametersmentioning

confidence: 99%

“…Unlike in single objective optimization, in optimization with conflicting objectives there is no single optimal solution and it is often necessary to determine a set of points that all fit a predetermined definition for the optimum. Usually, for multiobjective optimality the following definition given by Pareto is accepted (Gambier, 2008).…”

Section: Design Of the Multiobjective Tuning Of Gpc Parametersmentioning

confidence: 99%

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A multivariable multiobjective predictive controller

Aicha¹,

Bouani²,

Ksouri³

2013

International Journal of Applied Mathematics and Computer Science

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Predictive control of MIMO processes is a challenging problem which requires the specification of a large number of tuning parameters (the prediction horizon, the control horizon and the cost weighting factor). In this context, the present paper compares two strategies to design a supervisor of the Multivariable Generalized Predictive Controller (MGPC), based on multiobjective optimization. Thus, the purpose of this work is the automatic adjustment of the MGPC synthesis by simultaneously minimizing a set of closed loop performances (the overshoot and the settling time for each output of the MIMO system). First, we adopt the Weighted Sum Method (WSM), which is an aggregative method combined with a Genetic Algorithm (GA) used to minimize a single criterion generated by the WSM. Second, we use the NonDominated Sorting Genetic Algorithm II (NSGA-II) as a Pareto method and we compare the results of both the methods. The performance of the two strategies in the adjustment of multivariable predictive control is illustrated by a simulation example. The simulation results confirm that a multiobjective, Pareto-based GA search yields a better performance than a single objective GA.

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Section: Design Of the Multiobjective Tuning Of Gpc Parametersmentioning

confidence: 99%

Section: Design Of the Multiobjective Tuning Of Gpc Parametersmentioning

confidence: 99%

A multivariable multiobjective predictive controller

Aicha¹,

Bouani²,

Ksouri³

2013

International Journal of Applied Mathematics and Computer Science

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“…A practical strategy to handle multiple objectives in real-time consists in identifying the utopia point and to operate as close as possible to it [14].…”

Section: Handling Competing Objectivesmentioning

confidence: 99%

Real-Time Optimization Strategies for Building Systems

Zavala

2012

Ind. Eng. Chem. Res.

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We analyze different optimization strategies for real-time energy management in building systems. We have found that exploiting building-wide multivariable interactions between CO 2 , humidity, pressure, occupancy, and temperature leads to significant reductions of energy intensity. Our analysis indicates that it is possible to obtain energy savings of more than 50% compared with traditional control strategies. The exploitation of the thermal comfort zone has been found to be the most relevant factor driving energy savings. We discuss strategies to handle multiple competing objectives as well as economic functions and we analyze financial incentives provided by real-time prices and existing market designs. Numerical experiments are provided to support the claims.

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“…In the evaluation step of NSGAII, a simulation is performed for each controller 20 . Parameters used for simulation of NSGAII given in are: Crossover probability<0.9, Mutation probability>0.914, c = 20, m = 20, tour-size = 2 (size of the tournament selection), pool-size = ceil (size (population, 1)/2): pool-size is the number of individuals selected to form a mating pool after performing tournament selection.…”

Section: Nsgaii Tuning Of Pd Controller Of Puma560mentioning

confidence: 99%

A Multiobjective Genetic Algorithm Applied to Control Optimization

Chouraqui¹,

Benzater²

2015

Science International

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Background: In control system design there are often a number of design objectives to be considered. The objectives are sometimes connecting and no design exists which can be considered best with respect to all objectives. Hence, there is an inevitable tradeoff between design objectives, for example, between and output performance objective and stability robustness. These considerations have led to the study of multi objective optimization methods for control systems. Methods: In this study a multi-objective Non-dominated Sorting Genetic Algorithms NSGA-II is used to tuning of Proportional Derivative (PD) controller of a six freedom arm manipulator PUMA560. The NSGAII algorithm searches for the controller PD gains so that the six Integral Absolute Errors (IAE) in joint space are minimized. Results: Simulation numerical results of multivariable PD control and convergence of the NSGA-II are presented and discussed. Conclusion: The proposed optimization method based on NSGA-II is capable of generating adequate gains for PUMA560 system with minimum errors.

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MPC and PID control based on Multi-Objective Optimization

Cited by 59 publications

References 22 publications

A multivariable multiobjective predictive controller

A multivariable multiobjective predictive controller

Real-Time Optimization Strategies for Building Systems

A Multiobjective Genetic Algorithm Applied to Control Optimization

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