MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system

Ni, Haoqi; Zhou, Weifeng; Pardalos, Pãnos M.; Fang, Jiating; Fei, Minrui

doi:10.1016/j.engappai.2014.07.023

Cited by 46 publications

(13 citation statements)

References 24 publications

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“…Formulating the control performance of double inverted pendulum as a multiobjective fitness function, Wang et al [10] put forward a multiobjective binary probability optimization algorithm for the selection of optimal weighting matrices. ey adopted a binary-coding scheme to reduce the computational complexity and utilized integral absolute error (IAE) and maximum deviation (MD) as the fitness functions to improve the dynamic performance and stability of the system.…”

Section: Introductionmentioning

confidence: 99%

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Reddipogu

Elumalai

2020

Journal of Control Science and Engineering

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This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objectives of ASS including the ride comfort, road handling, and suspension travel, the state feedback controller design for ASS is formulated as an optimization problem and an improved PSO is employed for finding the optimal weights of the linear-quadratic regulator (LQR). Specifically, for solving the premature convergence of the particles and imbalance between exploration and exploitation capabilities of PSO, an adaptive inertia weight that updates the velocity of the particles based on the success rate is used. The efficacy of the AIWPSO-tuned LQR is experimentally tested on a quarter-car ASS plant using the hardware in loop (HIL) testing for an uneven road surface. Experimental results highlight that, compared to conventional PSO-tuned LQR, the proposed scheme can significantly minimize the vehicle body acceleration due to irregular road profile while guaranteeing the minimum tire friction for passenger safety. The ISO 2361-1 standards adopted to evaluate the ride and health criteria substantiate that the proposed scheme reduces the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration assessed in both low- and high-frequency regions manifests the significant improvement in the ride comfort.

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Section: Introductionmentioning

confidence: 99%

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Reddipogu

Elumalai

2020

Journal of Control Science and Engineering

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“…10 For this purpose, a linear dynamical model of the system is derived around predefined equilibrium points. For a given linearized model, it is straightforward to apply established methods such as pole placement 11 and LQR 12,13 to maintain the stability of the system. Various studies also show comparisons among different linear control schemes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

Önkol

Kasnakoğlu

2018

Measurement and Control

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This paper presents the adaptive model predictive control approach for a two-wheeled robot manipulator with varying mass. The mass variation corresponds to the robot picking and placing objects or loads from one place to another. A linear parameter varying model of the system is derived consisting of local linear models of the system at different values of the varying parameter. An adaptive model predictive control controller is designed to control the fast-varying center of gravity angle in the inner loop. The reference for the inner loop is generated by a slower outer loop controlling the linear position using a linear quadratic Gaussian regulator. This adaptive model predictive control/linear quadratic Gaussian control system is simulated on the nonlinear model of the robot, and the closed-loop performance of the proposed scheme is compared with a system having inner/outer loop controllers as proportional integral derivative/ proportional integral derivative, feedback linearization/linear quadratic Gaussian, and linear quadratic Gaussian/linear quadratic Gaussian. It is seen that adaptive model predictive control shows mostly superior and otherwise very good performance when compared to these benchmarks in terms of reference tracking and robustness to mass parameter variations.

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“…It can be observed that the expression (42) yields a simple relation to select the elements of penalty matrices according to the design criteria given in time domain. Hence, from the design perspective, knowing the desired damping ratio, natural frequency of the system and the state space matrices of the plant, one can determine the elements of weighting matrices and in turn the optimal controller gain (K) can be obtained using (6) by solving the ARE. In the next section, the validation of the proposed algebraic weight selection algorithm on a 2 DoF torsion system for tracking application is presented.…”

Section: Vol 66 (2017)mentioning

confidence: 99%

“…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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MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system

Cited by 46 publications

References 24 publications

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

Adaptive Model Predictive Control of a Two-wheeled Robot Manipulator with Varying Mass

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

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