Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system

Tao, Chin‐Wang; Taur, J.S.; Chen, Y. C.

doi:10.1016/j.fss.2009.12.007

Cited by 101 publications

(34 citation statements)

References 14 publications

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“…5, statistical performance of GSA. Our research shows the combination of elevation and azimuth i.e., vertical and horizontal which is better than the errors reported in previous researches [15][16][17], where the error values are reported separately (Vertical or Horizontal). Hence from the results it is observed that by optimizing the α and δ, the results are better than in previous researches [12,13] reported without optimizing α and δ. PSO converges faster in the least optimal parameter in all the cases.…”

Section: Resultscontrasting

confidence: 77%

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Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

2018

Teh. vjesn.

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Abstract:In this paper, the Twin rotor MIMO system (TRMS) is tuned by Nonlinear PID controller using Evolutionary Computation methods. The proposed Nonlinear PID controller, used to tune TRMS, improves the system performance with additional degrees of freedom. Evolutionary Computation methods such as Differential Search Algorithm (DSA), real coded Genetic Algorithm (RGA) with simulated binary crossover (SBX) and Particle Swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are used to determine the optimal parameters of the proposed controller by minimizing Integral Square Error (ISE) for rotor response of TRMS. SIMULINK MATLAB software is used for simulating the system. The statistical performance of the controller is analysed among twenty independent trials by taking best, worst, mean and standard deviations of ISE. Simulation results reveal that TRMS system tuned by nonlinear PID controller using Particle Swarm optimization (PSO) is better than the other methods

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Section: Resultscontrasting

confidence: 77%

“…Tao et al proposed a fuzzy controller for TRMS [17] and in his work the minimization of error is not reported. Taskin [18] has proposed a fuzzy and LQR model with PID control.…”

Section: Introductionmentioning

confidence: 99%

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

2018

Teh. vjesn.

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“…Moreover, minimizing the quadratic cost function which integrates both states and inputs of the system via penalty matrices, LQR offers an optimal response between speed of response and amount of control input. In the last few decades, several results on LQR: hybrid LQR [3], fuzzy LQR [4], switched LQR [5], to name a few, have been reported in the literature. Moreover, LQR has been successfully applied for a large number of complex systems namely, a double inverted pendulum [6], fuel cell systems [6], and aircraft [7].…”

Section: Introductionmentioning

confidence: 99%

A new algebraic LQR weight selection algorithm for tracking control of 2 DoF torsion system

Elumalai

Subramanian

2017

Archives of Electrical Engineering

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This paper proposes a novel linear quadratic regulator (LQR) weight selection algorithm by synthesizing the algebraic Riccati equation (ARE) with the Lagrange multiplier method for command following applications of a 2 degree of freedom (DoF) torsion system. The optimal performance of LQR greatly depends on the elements of weighting matrices Q and R. However, normally these weighting matrices are chosen by a trial and error approach which is not only time consuming but cumbersome. Hence, to address this issue, blending the design criteria in time domain with the ARE, we put forward an algebraic weight selection algorithm, which makes the LQR design both simple and modular. Moreover, to estimate the velocity of a servo angle, a high gain observer (HGO) is designed and integrated with the LQR control scheme. The efficacy of the proposed control scheme is tested on a benchmark 2 DoF torsion system for a trajectory tracking application. Both the steady state and dynamic characteristics of the proposed controller are assessed. The experimental results accentuate that the proposed HGO based LQR scheme can guarantee the system to attain the design requirements with minimal vibrations and tracking errors.

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“…Due to their inherent robustness and stability properties, such as a gain margin of (!6, ∞) dB and a phase margin of (!60E, 60E), LQR finds its application in many engineering and scientific domains [1][2][3]. In the last two decades several investigations have been reported on LQR, namely, self-adjusting LQR [4], switched LQR [5], hybrid LQR [6] and fuzzy LQR [7]. In addition, LQR techniques have been successfully implemented for a large number of complex systems such as the double inverted pendulum [8], fuel cell systems [9], vibration control system [10], electric vehicles [11], and aircraft [12].…”

Section: Introductionmentioning

confidence: 99%

Algebraic Riccati equation based Q and R matrices selection algorithm for optimal LQR applied to tracking control of 3rd order magnetic levitation system

Kumare¹,

Jerome²

2016

Archives of Electrical Engineering

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This paper presents an analytical approach for solving the weighting matrices selection problem of a linear quadratic regulator (LQR) for the trajectory tracking application of a magnetic levitation system. One of the challenging problems in the design of LQR for tracking applications is the choice of Q and R matrices. Conventionally, the weights of a LQR controller are chosen based on a trial and error approach to determine the optimum state feedback controller gains. However, it is often time consuming and tedious to tune the controller gains via a trial and error method. To address this problem, by utilizing the relation between the algebraic Riccati equation (ARE) and the Lagrangian optimization principle, an analytical methodology for selecting the elements of Q and R matrices has been formulated. The novelty of the methodology is the emphasis on the synthesis of time domain design specifications for the formulation of the cost function of LQR, which directly translates the system requirement into a cost function so that the optimal performance can be obtained via a systematic approach. The efficacy of the proposed methodology is tested on the benchmark Quanser magnetic levitation system and a detailed simulation and experimental results are presented. Experimental results prove that the proposed methodology not only provides a systematic way of selecting the weighting matrices but also significantly improves the tracking performance of the system.

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Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system

Cited by 101 publications

References 14 publications

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

A new algebraic LQR weight selection algorithm for tracking control of 2 DoF torsion system

Algebraic Riccati equation based Q and R matrices selection algorithm for optimal LQR applied to tracking control of 3rd order magnetic levitation system

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