Novel system identification method and multi-objective-optimal multivariable disturbance observer for electric wheelchair

Saadatzi, Mohammad Nasser; Poshtan, Javad; Saadatzi, Mohammad Sadegh; Tafazzoli, Faezeh

doi:10.1016/j.isatra.2012.06.013

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…It does not give a set of solutions as a trade-off between different contradictory objectives. But in the present study, a new controller design philosophy for NCS applications has been proposed using multi-objective genetic algorithm, similar to that studied in [38], [39]. Due to the discontinuous nature of the objective functions, in the presence of random packet losses, it is not possible to frame it as classical convex optimization problem which again justifies the motivation of applying population based global optimization techniques [10].…”

Section: Few Discussion On the Proposed Networked Predictive Controll...mentioning

confidence: 97%

Multi-objective optimization framework for networked predictive controller design

Das

Pan

2013

ISA Transactions

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Networked Control Systems (NCSs) often suffer from random packet dropouts which deteriorate overall system's stability and performance. To handle the ill effects of random packet losses in feedback control systems, closed over communication network, a state feedback controller with predictive gains has been designed. To achieve improved performance, an optimization based controller design framework has been proposed in this paper with Linear Matrix Inequality (LMI) constraints, to ensure guaranteed stability. Different conflicting objective functions have been optimized with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only gives guaranteed closed loop stability in the sense of Lyapunov, even in the presence of random packet losses, but also gives an optimization trade-off between two conflicting time domain control objectives.

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Section: Few Discussion On the Proposed Networked Predictive Controll...mentioning

confidence: 97%

Multi-objective optimization framework for networked predictive controller design

Das

Pan

2013

ISA Transactions

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“…A disturbance observer in the RIC framework is composed of internal and external loops [34–36]. The design of the internal loop is based on input disturbance attention, where most often appearing disturbances are reaction and load torque, friction, and unmodeled dynamics [20, 34].…”

Section: Ric Disturbance Observer For Positioning Systemsmentioning

confidence: 99%

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

Sarjaš

Svečko

Chowdhury

2014

The Scientific World Journal

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This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution.

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“…An enhanced bacteria foraging optimization technique is considered for the optimal power flow problem with the presence of FACTS devices (Edward et al, 2013). A novel system identification method is considered for electric wheelchair in (Saadatzi et al, 2013). In (Hooshmand & Esfahani, 2011) LMS algorithm based adaptive filter is considered for delay minimization in thyristor controlled reactor compensators.…”

Section: Introductionmentioning

confidence: 99%

Collective Animal Behaviour Based Optimization Algorithm for IIR System Identification Problem

Upadhyay

Kar

Mandal

et al. 2014

International Journal of Swarm Intelligence Research

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In this paper a novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied to the infinite impulse response (IIR) system identification problem. Functionality of CAB is governed by occupying the best position of an animal according to its dominance in the group. Enrichment of CAB with the features of randomness, stochastic and heuristic search nature has made the algorithm a suitable tool for finding the global optimal solution. The proposed CAB has alleviated from the defects of premature convergence and stagnation, shown by real coded genetic algorithm (RGA), particle swarm optimization (PSO) and differential evolution (DE) in the present system identification problem. The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using CAB over RGA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for IIR system models of both the same order and reduced order.

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Novel system identification method and multi-objective-optimal multivariable disturbance observer for electric wheelchair

Cited by 12 publications

References 23 publications

Multi-objective optimization framework for networked predictive controller design

Multi-objective optimization framework for networked predictive controller design

Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

Collective Animal Behaviour Based Optimization Algorithm for IIR System Identification Problem

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