Adaptive predator–prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system

Das, Rashmi Ranjan; Elumalai, Vinodh Kumar; Subramanian, Raaja Ganapathy; Kumar, Kadiyam Venkata Ashok

doi:10.1016/j.asoc.2018.06.044

Cited by 28 publications

(8 citation statements)

References 20 publications

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“…This paper evaluates control performances to packet loss by comparing between the H 1 controller and an LQ controller which does not care packet loss [8]. Tuning parameters in both controller designs are set to be the same step response for fair comparison of control performances.…”

Section: Numerical Examplementioning

confidence: 99%

Influence of packet loss on wireless feedback control systems as a disturbance

et al. 2019

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The knowledge of characteristics of the influence of packet loss is important to design controllers and observers which can reduce this influence on wireless feedback control systems. This paper clarifies the characteristics of the influence of packet loss as a disturbance that causes a difference between transmitted information and used information at the received side. Through the analyses of the influence of packet loss on both controller and controlled object sides, we clarify the power spectrum density of the influence on the controller side is not constant. Numerical results show an well-known approach -H ∞ controller, which can reduce the influence of white disturbances, can not reduce the influence of packet loss. Our analyses will be useful for good modeling of the influence of packet loss to design controllers and observers. Wireless feedback control systemThis paper deals with a wireless feedback control system that is discretized at a sampling interval T s . At time t ¼ kT s (k ¼ 0; 1; 2; Á Á Á), each of the state information and the control information is expressed as x½k and u½k. The plant is assumed to be linear time invariant system and is modeled by state equation as follows: Fig. 3. Power spectrum density of w u ½k (S w u ðfÞ) (p ¼ 0:2).

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Section: Numerical Examplementioning

confidence: 99%

Influence of packet loss on wireless feedback control systems as a disturbance

et al. 2019

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“…Therefore, Shu Kai et al 25 proposed a linear decline of inertia weight particle swarm optimization algorithm, Its inertia weight linear decline of dynamic as the number of iterations increases to improve the optimization effect of the algorithm. Later, scholars also improved the particle swarm optimization algorithm from the optimization result and convergence speed, [26][27][28][29] but most can't consider both the convergence speed and optimization effect. In the light of the characteristics of LQR control of elevator car horizontal vibration, to find the weighting coefficient matrix Q of LQR controller more quickly and effectively, this paper proposes a Jumping inertia Weight Particle Swarm Optimization (JWPSO) algorithm which can adjust the inertia weight of PSO adaptively according to the objective function value and optimizes the weighting matrix Q of LQR control by using JWPSO to improve further the LQR control effect of elevator car horizontal vibration primary control and then simulates the elevator car horizontal vibration by MATLAB/Simulink to analyze the effect of active control.…”

Section: Introductionmentioning

confidence: 99%

Study on the LQR Control of High-speed Elevator Car Horizontal Vibration Based on the Jumping Inertia Weight Particle Swarm Optimization

He¹,

Li²,

Zhang³

et al. 2022

The International Journal of Acoustics and Vibration

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To reduce the horizontal vibration of a high-speed elevator car caused by the excitation of a guide rail more effectively, a differential magnetic suspension active guide shoe based on the principle of the differential magnetic suspension actuator is designed. Then the dynamic model of the active vibration damping system of the elevator car is established and an LQR controller is designed to reduce the horizontal vibration of the car. To search the weighting coefficient matrix of the LQR controller more efficiently, an algorithm named Jumping inertia Weight Particle Swarm Optimization (JWPSO) algorithm is proposed, and the frequently used fitness function verifies the optimization effect of the JWPSO algorithm. The weighting coefficient matrix of the LQR controller is optimized using the JWPSO algorithm. Finally, the impact of the JWPSO algorithm-optimized LQR controller is verified by MATLAB. The simulation result shows that the designed active vibration controller can effectively attenuate the horizontal vibration of the elevator car, and the control effect is significantly better than the LQR controller optimized by the GA algorithm and the H-inf controller optimized by LMI. This paper provided a new method for horizontal vibration reduction of the high-speed elevator car.

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“…The literature contains several models for the simulation of the dynamic behavior of vehicles. These models can be categorized into three groups of quarter, 14–16 half 17–20 and full-vehicle models. 21–25 Naturally, if the vehicle model in simulation is more complete, the dynamic response obtained from the model will be more reliable.…”

Section: Introductionmentioning

confidence: 99%

Parameters uncertainty in pareto optimization of nonlinear inerter-based suspension system under nonstationary random road excitation

Seifi

Hassannejad

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Ignoring the possible impacts of uncertainties in vehicle components during the design phase can undermine the safety of passengers and the vehicle performance. The main function of a suspension system is to provide satisfactory ride comfort and road-holding with a sufficiently low probability of rollover. Despite many studies on the design of new suspension systems with inerters, the effect of uncertainties in vehicle weight and tire stiffness on the design of suspension with inerters has not received much attentions. This paper presents a new type of suspension with inerters and asymmetric dampers and investigates the dynamic behavior of a vehicle under variable vehicle speed. Moreover, the effect of uncertainties on the choice of acceptable values of inerters is evaluated. For this investigation, the authors developed a 9-DOF full vehicle model with roll and yaw motions under non-stationary random road excitations in the time and frequency domains and studied its dynamic response with different suspension models. The optimal design was performed using a multi-objective optimization algorithm called MOEA/D. The best model was then used to determine the effect of uncertainties on the choice of inerters. The optimization results show that using the optimized suspension with inerters and nonlinear dampers instead of conventional design improves the ride comfort by 0.16%, the vehicle road-holding by 3.54%, and the rollover probability by 44.73%. In the proposed model, by changing the values of vehicle parameters with uncertainty, the choice of inerters to have an acceptable performance would be variable.

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Adaptive predator–prey optimization for tuning of infinite horizon LQR applied to vehicle suspension system

Cited by 28 publications

References 20 publications

Influence of packet loss on wireless feedback control systems as a disturbance

Influence of packet loss on wireless feedback control systems as a disturbance

Study on the LQR Control of High-speed Elevator Car Horizontal Vibration Based on the Jumping Inertia Weight Particle Swarm Optimization

Parameters uncertainty in pareto optimization of nonlinear inerter-based suspension system under nonstationary random road excitation

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