Adaptive Fuzzy-PI Control for Active Front Steering System of Armoured Vehicles: Outer Loop Control Design for Firing On The Move System

Kadir, Zulkiffli Abd; Mazlan, Saiful Amri; Zamzuri, Hairi; Hudha, Khisbullah; Amer, Noor Hafizah

doi:10.5545/sv-jme.2014.2210

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…The previous work from the authors has demonstrated the derivations and verifications of this model [12,15]. Also, several previous studies have been evaluating its controller's performance on the same model [11,14,16]. Figure 1 also shows the symbols and vehicle parameters used in developing the model.…”

Section: Modelling Of a 7dof Heavy Vehiclementioning

confidence: 97%

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Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Amer¹,

Hudha²,

Zamzuri³

et al. 2021

Automation and Control

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This chapter discusses the development of an adaptive path tracking controller equipped with a knowledge-based supervisory algorithm for an autonomous heavy vehicle. The controller was developed based on a geometric/kinematic controller, the Stanley controller. One of the mostly known issues with any geometric/kinematic controller is that a properly tuned controller may not be valid in a different operating region than the one it was being tuned/optimised on. Therefore, this study proposes an adaptive algorithm to automatically choose an optimal controller parameter depending on the manoeuvring and vehicle conditions. An optimal knowledge database is developed for an adaptive algorithm to automatically obtain the parameter values based on the vehicle speed, v, and heading error, ϕ. Several simulations are carried out with different trajectories and speeds to evaluate the effectiveness of the controller against its predecessors, namely, Stanley and the nonadaptive modified Stanley (Mod St) controllers. The simulated steering actions are then compared against human driver's experimental data along the predefined paths. It was shown that the proposed adaptive algorithm managed to guide the heavy vehicle successfully and adapt to various trajectories with different vehicle speeds while recording lateral error improvement of up to 82% compared to the original Stanley controller.

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Section: Modelling Of a 7dof Heavy Vehiclementioning

confidence: 97%

“…The model, developed based on a heavy vehicle, namely, a High Mobility Multipurpose Wheeled Vehicle (HMMWV), is closely related to an armoured vehicle as shown in Figure 1. It consists of several Heavy vehicle model [11][12][13].…”

Section: Modelling Of a 7dof Heavy Vehiclementioning

confidence: 99%

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Amer¹,

Hudha²,

Zamzuri³

et al. 2021

Automation and Control

Self Cite

View full text Add to dashboard Cite

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“…The role of PID control in industrial automation has been presented in [9], which formulates a nonlinear PID control law to ensure global asymptotic stability (GAS). Classical approaches, when combined with modern control strategies, improve transient response in uncertain scenarios as highlighted in [10]. Combining VSC with PID and adaptive control strategies, Jingmei et al have improved precision in trajectory tracking of a robotic manipulator [11].…”

Section: Towards Sophisticated Control Of Robotic Manipulators: An Exmentioning

confidence: 99%

Towards Sophisticated Control of Robotic Manipulators: An Experimental Study on a Pseudo-Industrial Arm

Iqbal¹,

Ullah²,

Khan³

et al. 2015

SV-JME

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Robotic manipulators have reshaped industrial processes. The scientific community has witnessed an ever increasing trend in robots deployed to accomplish various tasks in industry. The complex nature and constrained requirements of robots may demand non-trivial control approaches. This paper deals with the design, simulation and hardware realization of two sophisticated control strategies: computed torque control (CTC) and variable structure control (VSC) on a pseudo-industrial manipulator with six degrees of freedom (DOF). Based on the derived kinematic and dynamic models of the robot, control laws have been formulated, which are then subjected to various test inputs in a simulation to characterize the tracking performance. The simulation results were then validated by implementing control laws on a custom-developed pseudo-industrial autonomous articulated robotic educational platform (AUTAREP). The experimental results show the effectiveness of the control strategies to track a desired trajectory. Highlights• This paper presents advanced strategies to control a highly non-linear system like a multiple Degree Of Freedom (DOF) robotic arm. • The strategies include computed torque control (CTC) and variable structure control (VSC). • Design parameters using both strategies have been investigated in a simulation. • The strategies were carried out on a custom-developed autonomous articulated robotic educational platform (AUTAREP). • Trajectory tracking results showed that the derived laws can effectively track the desired reference input for both strategies.

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“…10 An AFS system based on adaptive fuzzy-PI controller is used to guarantee stability of armored vehicles when shooting. 11 An AFS system is used for yaw stability control, which is based on the feedback of both yaw rate and front steering angle, and the decoupling of the lateral and yaw motion of a vehicle and damping for vehicle's yaw are achieved simultaneously. 12 The trade-off between the vehicle's yaw damping and decoupling of lateral and yaw motion are discussed.…”

Section: Introductionmentioning

confidence: 99%

A model free controller based on reinforcement learning for active steering system with uncertainties

Zhao

Cheng

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Vehicle steering control is crucial to autonomous vehicles. However, unknown parameters and uncertainties of vehicle steering systems bring a great challenge to its control performance, which needs to be tackled urgently. Therefore, this paper proposes a novel model free controller based on reinforcement learning for active steering system with unknown parameters. The model of the active steering system and the Brushless Direct Current (BLDC) motor is built to construct a virtual object in simulations. The agent based on Deep Deterministic Policy Gradient (DDPG) algorithm is built, including actor network and critic network. The rewards from environment are designed to improve the effectiveness of agent. Simulations and testbench experiments are implemented to train the agent and verify the effectiveness of the controller. Results show that the proposed algorithm can acquire the network parameters and achieve effective control performance without any prior knowledges or models. The proposed agent can adapt to different vehicles or active steering systems easily and effectively with only retraining of the network parameters.

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Adaptive Fuzzy-PI Control for Active Front Steering System of Armoured Vehicles: Outer Loop Control Design for Firing On The Move System

Cited by 14 publications

References 11 publications

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Knowledge-Based Controller Optimised with Particle Swarm Optimisation for Adaptive Path Tracking Control of an Autonomous Heavy Vehicle

Towards Sophisticated Control of Robotic Manipulators: An Experimental Study on a Pseudo-Industrial Arm

A model free controller based on reinforcement learning for active steering system with uncertainties

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