Direct adaptive neural control of a quarter-car active suspension system

Pedro, Jimoh O.; Dahunsi, Olurotimi Akintunde; Baloyi, Nyiko

doi:10.1109/afrcon.2011.6071981

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…Intelligent controllers provide new alternatives with better prospects but they inherit the setbacks of the nonlinear controllers since they are implemented in most cases in combination with the nonlinear control techniques [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions

Pedro¹,

Dangor²,

Dahunsi³

et al. 2013

Mathematical Problems in Engineering

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This paper presents a differential-evolution- (DE-) optimized, independent multiloop proportional-integral-derivative (PID) controller design for full-car nonlinear, electrohydraulic suspension systems. The multiloop PID control stabilises the actuator via force feedback and also improves the system performance. Controller gains are computed using manual tuning and through DE optimization to minimise a performance index, which addresses suspension travel, road holding, vehicle handling, ride comfort, and power consumption constraints. Simulation results showed superior performance of the DE-optimized PID-controlled active vehicle suspension system (AVSS) over the manually tuned PID-controlled AVSS and the passive vehicle suspension system (PVSS).

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Section: Introductionmentioning

confidence: 99%

Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions

Pedro¹,

Dangor²,

Dahunsi³

et al. 2013

Mathematical Problems in Engineering

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“…Neural networks control has been proposed for quarter-car active suspension systems, for example, an adaptive neural network control scheme in which the neural networks approximate the unknown car’s mass, 15 a cascade neural network predictive controller for a quarter-car active suspension non-linear system, 16 a direct adaptive neural network based feedback linearization controller. 17…”

Section: Related Studiesmentioning

confidence: 99%

Design and comparisons of adaptive harmonic control for a quarter-car active suspension

Nichiţelea

Unguritu

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Car suspensions have the job to keep the tires in contact with the road surface as much as possible, to deliver steering stability with good handling and to guarantee passenger comfort. Most modern vehicles have independent front suspension and many vehicles also have independent rear suspension. Independent suspensions are preferred instead of dependent suspensions for their better ride handling, stability, steering and comfort but they provide less overall strength and a complex design which increases the cost and maintenance expenses for such a suspension. For this reason, automotive engineers struggle to discover new suspension components or advanced control solutions. Taking a step forward in this direction, the paper presents in the beginning one of the well-known mathematical models of a quarter-car active suspension. The obtained model is then implemented in a MATLAB/Simulink simulation which compares multiple control solutions. The only feedback considered for each control algorithm is the measurement of the body acceleration. Among these investigated control algorithms is the adaptive harmonic control solution proposed by this paper. The controller generates a harmonic control signal with variable amplitude and frequency based on the body acceleration feedback. The comparison analysis shows that the proposed control solution demonstrates quite good potential, generating in some cases better results than the other control algorithms.

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“…It is desired to obtain a FBL control law u that will perform the output tracking in such a way that the quadrotor follows the desired output y d with an acceptable level of accuracy, while the states remain bounded [17], [18], [19]. Figure 2 shows the multiple input, multiple output structure of the RBFNN.…”

Section: Controller Designmentioning

confidence: 99%

“…The weight update rules associated with the network may be easily derived through Lyapunov stability analyses [17], [18]. Direct adaptive control using RBFNN has been successfully implemented for other research platforms which exhibit similar characteristics as the quadrotor; e.g., the control of a quarter-car active suspension system and a two-link manipulator [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Direct adaptive neural control of a quadrotor unmanned aerial vehicle

Pedro

Crouse

2015

2015 10th Asian Control Conference (ASCC)

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This paper presents the design of a direct adaptive neural network (DANN)-based feedback linearization (FBL) controller for a multi-input multi output, unstable, nonlinear and underactuated quadrotor UAV. A full system identification was performed on the quadrotor using radial basis function neural networks (RBFNN). The DANN controller was benchmarked against a conventional PD controller which was tuned using Ziegler-Nichols tuning method. The designed controller was found to be capable of accurately tracking the desired output variables simultaneously (altitude, roll, pitch, and yaw angles respectively) and also was robust to parameter variations and disturbance input.

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Direct adaptive neural control of a quarter-car active suspension system

Cited by 8 publications

References 27 publications

Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions

Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions

Design and comparisons of adaptive harmonic control for a quarter-car active suspension

Direct adaptive neural control of a quadrotor unmanned aerial vehicle

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