Research on Optimal Control for the Vehicle Suspension Based on the Simulated Annealing Algorithm

Meng, Jie; Chen, Qingzhang; Ren, He

doi:10.1155/2014/420719

Cited by 14 publications

(10 citation statements)

References 4 publications

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“…Simulation results showed the better performance of designed HFPIDCR controller in achieving high performance for passenger ride comfort and safety. Recently, various control strategies such as interval fuzzy controller [23], optimal law [24], robust sampled data H∞ control [25], finite frequency H∞ control [26], composite nonlinear feedback control [27], fuzzy sliding mode control [28], active force control [29], LQR control [30], multi-objective control [31] and continuous and discrete sliding mode control [32] etc. have been used and compared for suppression of road induced vibrations in active quarter car suspension system.…”

Section: Related Workmentioning

confidence: 99%

Passenger Body Vibration Control in Active Quarter Car Model using Hybrid ANFIS PID Controller

Faridabad¹,

Haryana²

2018

IJISA

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Abstract-The objective of this paper is to improve the passenger ride comfort and safety in an active quarter car model. For this purpose, a quarter car model with passenger body and seat is considered to capture the dynamic behaviour of a real complete car system. To achieve the desired target, two different controllers such as Adaptive Neuro Fuzzy (ANFIS) controller and Hybrid ANFIS PID controller (HANFISPID) are designed. The controllers selection and design was aimed to achieve good passenger ride comfort and health, taking passenger body acceleration and displacement response under random road excitations. The performance of designed controllers are evaluated using simulation work in time and frequency domain. Simulation results show that the proposed HANFISPID control scheme can succesfully achieve the desired ride comfort and safety of passenger compared to passive and ANFIS controlled cases in an active quarter car model.

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Section: Related Workmentioning

confidence: 99%

Passenger Body Vibration Control in Active Quarter Car Model using Hybrid ANFIS PID Controller

Faridabad¹,

Haryana²

2018

IJISA

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“…During simulation the vehicle was travelling over a class B road at 20 kmph speed. A sprung mass acceleration of 0.3326 m/ s 2 and suspension space travel of 0.354843m was observed using SA optimized LQR control (Meng et al, 2014). A current GA based optimization technique incorporates multi-objective optimization.…”

Section: Multi Objective Optimizationmentioning

confidence: 99%

Optimization of the linear quadratic regulator (LQR) control quarter car suspension system using genetic algorithm

Nagarkar¹,

Patil²

2016

ing.inv.

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In this paper, a genetic algorithm (GA) based in an optimization approach is presented in order to search the optimum weighting matrix parameters of a linear quadratic regulator (LQR). A Macpherson strut quarter car suspension system is implemented for ride control application. Initially, the GA is implemented with the objective of minimizing root mean square (RMS) controller force. For single objective optimization, RMS controller force is reduced by 20.42 % with slight increase in RMS sprung mass acceleration. Trade-off is observed between controller force and sprung mass acceleration. Further, an analysis is extended to multi-objective optimization with objectives such as minimization of RMS controller force and RMS sprung mass acceleration and minimization of RMS controller force, RMS sprung mass acceleration and suspension space deflection. For multi-objective optimization, Pareto-front gives flexibility in order to choose the optimum solution as per designer's need.Keywords: Genetic algorithm (GA), MacPherson strut, quarter car, linear quadratic regulator (LQR), optimization. RESUMENEn este artículo se presenta un algoritmo genético (GA) basado en un enfoque de optimización con el fin de encontrar los parámetros de la matriz de ponderación del regulador lineal cuadrático (LQR). Se implementa un sistema de suspensión Macpherson para la aplicación del control de amortiguación. Inicialmente el GA se implementa con el objetivo de minimizar la raíz cuadrada media (RMS) del controlador de fuerza. Para la optimización de un único objetivo, el controlador RMS de la fuerza se reduce en un 20,42 % con un ligero aumento en la aceleración RMS de la masa suspendida. Se observa equilibrio entre el control de fuerza y la aceleración de la masa suspendida. Además, el análisis se extiende a la optimización multiobjetivo con objetivos como la minimización del control RMS de la fuerza y de la aceleración debida a la masa suspendida y la minimización del control RMS de la fuerza, RMS surgida por la aceleración de la masa y la deflexión del espacio de la suspensión. Para la optimización multiobjetivo el Pareto-frontal facilita elegir la solución óptima según la necesidad del diseñador.Palabras clave: Algoritmo genético (AG), suspensión MacPherson, cuarto de coche, regulador lineal cuadrático (LQR), optimización.

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“…LQR control technique has been derived and implemented for the present work. Meng 4 , et al, describes a method to optimise the weight matrix of the LQR controller by using the simulated annealing algorithm which utilises the random searching characteristics of the algorithm to optimise the weight matrices with the target function of suspension performance indexes. This method improves the design efficiency and control performance of the LQR control, and solves the problem of the LQR controller when defining the weight matrices.…”

Section: Introductionmentioning

confidence: 99%

Development of Mathematical Models, Simulating Vibration Control of Tracked Vehicle Weapon Dynamics

Jakati¹,

Banerjee²,

Jebaraj³

2017

Def. Sc. Jl.

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Focuses upon development of the mathematical model, simulating the tracked vehicle weapon dynamics, integrated over a half car platform. Governing differential equations have been formulated for the weapon system using state space approach, simulating the elevation dynamics over a half vehicle chassis, and coded using Matlab. The elevation model of the weapon comprises 3 degrees of freedom, arising from the rotational dynamics of the drive, breech and muzzle, which has sequentially been coupled to the half car model. Thereafter, the backstepping, LQR and PID control techniques have been derived and incorporated into the state space matrix for the coupled dynamics model, in which the control parameters have been arrived at through various iterations. Comparative weapon dynamics response studies have been carried out between that obtained from the above control strategies and the passive model, over standard terrain conditions at specified speeds. The above study would form a very useful framework for implementation of alternate control strategies for weapon stabilisation in the full tracked vehicle.

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Research on Optimal Control for the Vehicle Suspension Based on the Simulated Annealing Algorithm

Cited by 14 publications

References 4 publications

Passenger Body Vibration Control in Active Quarter Car Model using Hybrid ANFIS PID Controller

Passenger Body Vibration Control in Active Quarter Car Model using Hybrid ANFIS PID Controller

Optimization of the linear quadratic regulator (LQR) control quarter car suspension system using genetic algorithm

Development of Mathematical Models, Simulating Vibration Control of Tracked Vehicle Weapon Dynamics

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