A multivariable multiobjective predictive controller

This paper is devoted to the cooperative tracking control of multiple unmanned aerial vehicles with unknown faults. Considering the practical case that the unmanned aerial vehicles suffer from strong nonlinearities, external disturbances, actuator, and sensor faults, this brief investigates the distributed adaptive fault tolerant scheme for multi-unmanned aerial vehicle system within model predictive control framework. Firstly, for preparation to the fault occurrence case, the baseline model predictive control scheme is designed under fault-free case. Then considering the fault occurrence, a fault detection strategy is proposed by means of the moving horizon estimation and linearly parameterized approximation for actuator and sensor faults, respectively.Thereby with the characterized fault information, the fault tolerant model predictive control scheme is constructed by using an adaptive updating mechanism to compensate for actuator and sensor faults simultaneously. Finally, simulations well demonstrate the effectiveness of proposed control scheme.

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“…Taking inspiration from Aicha et al [33], Junior et al [34], and Storn and Price [35], an adaptive DE algorithm is utilized for

{\overset{false¯}{J}}_{i}^{'} ((), k)

. The details on adaptive DE algorithm can be found in our previous work [38].…”

Section: Baseline Mpc Design Under Fault‐free Casementioning

confidence: 99%

Distributed fault tolerant model predictive control for multi‐unmanned aerial vehicle system

Zhang

Sun

Deng

2021

Asian Journal of Control

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“…Moreover, postulate 2 contradicts it because fuel consumption minimization means that most effective compressors should be fully loaded all the time. Problems of similar nature have been studied recently in [12]: the authors consider there a multicritreria model predictive control, comparing two methods of picking up a unique solution out of the Pareto front.…”

Section: A Multiple Optimality Criteriamentioning

confidence: 99%

Simulation-based optimization of transportation costs in high pressure gas grid

Кamola¹,

Plamowski²

2013

Preprint

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Design, architecture and deployment details of a decision support system engineered to minimize operating costs of compressor stations in a gas network are presented. The system employs standard simulation software for pipelines, combined with well known optimization routine for finding optimal station control profiles in a repetitive way. A list of custom improvements is presented that make the system capable and robust enough to perform the optimization tasks. Implementation process is described in detail, covering the case of handling extra optimality criteria postulated by the user. Benefits from using the system and lessons learned are presented in the conclusions section.

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“…Generally, MOEAs can be categorized into three groups, namely, dominance-based methods, scalarization-based methods, and performance indicator-based approaches (Cheng et al, 2015;Denysiuk et al, 2015). Among these, dominance-based approaches such as SPEA2 (Zitzler et al, 2001), NSGA-II (Deb et al, 2002;Ben Aicha et al, 2013), NSGA-III (Deb and Jain, 2014), and NSLS (Chen et al, 2015) have been probably the most commonly used approaches, which calculate an individual's fitness on the basis of the Pareto dominance relation.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

Zuo

Zhao-fu

et al. 2017

International Journal of Applied Mathematics and Computer Science

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The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

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A multivariable multiobjective predictive controller

Cited by 15 publications

References 23 publications

Distributed fault tolerant model predictive control for multi‐unmanned aerial vehicle system

Distributed fault tolerant model predictive control for multi‐unmanned aerial vehicle system

Simulation-based optimization of transportation costs in high pressure gas grid

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

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