Robust Steering Control of Autonomous Underwater Vehicle: based on PID Tuning Evolutionary Optimization Technique

Rathore, Ankush; Kumar, Mahendra

doi:10.5120/20651-3179

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…Then, the control signal into the process can be described by equation (10) and equation (11) after adding feedforward and feedback.…”

Section: Model-following Self Tunermentioning

confidence: 99%

“…The development of adaptive and learning systems has a long history that is presently culminating in a very recent, distinguished lineage in the literature [4,11,12,13,14], already bestowing three major publication awards, validating the contemporary interest in continued developments. Many techniques are available from this long lineage as alternatives benchmarks for comparing newly proposed methods.…”

Section: Introductionmentioning

confidence: 99%

“…Many techniques are available from this long lineage as alternatives benchmarks for comparing newly proposed methods. Rathmore focused on artificial intelligence applying robust steering control based on tuning of PID controller using the so-called genetic algorithm and the harmonic search algorithm [11]. In 2020, socalled deterministic artificial intelligence was proposed for controlling autonomous unmanned underwater vehicles [12].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Enhanced UUV Actuator Control

Wang¹,

Sands²

2023

Preprint

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This manuscript compares deterministic artificial intelligence to model following control applied to DC motor control, including evaluation of the threshold computation rate to let unmanned underwater vehicles correctly follow the challenging discontinuous square wave command signal. The approaches presented in the main text are validated in MATLAB®, where the motor process is discretized at multiple step sizes, which is inversely proportional to the computation rate. The performance is evaluated by the error mean and standard deviation. With a large step size, discrete deterministic artificial intelligence shows a larger error mean than the model-following self-turning regulator approach (benchmark selection). However, the performance gets optimized with the step size decreased. The error mean is close to the continuous deterministic artificial intelligence when the step size is reduced to 0.2 seconds, which means that the computation rate and the sampling period restrict discrete deterministic artificial intelligence. In that case, the continuous deterministic artificial intelligence is the most feasible and reliable selection for future applications on unmanned underwater vehicles since it is superior to all the approaches with multiple computation rates.

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“…Then, the control signal into the process can be described by equation (10) and equation (11) after adding feedforward and feedback.…”

Section: Model-following Self Tunermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence Enhanced UUV Actuator Control

Wang¹,

Sands²

2023

Preprint

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show abstract

“…Pouria et al embedded adaptive control with anti-windup compensators with a PID controller to conquer the challenge of model uncertainty and saturation in actuators [37]. Since tuning of PID parameters is important because these parameters have a great effect on the stability and performance of the control system, Ankush et al [38] proposed a harmonic-search-algorithm (HSA) technique to tune the PID parameters. Similarly, as traditional PID fails to ensure lateral control stability due to the inflexible parameter settings, BP-PID (back-propagation proportion-integration-differentiation) was designed by Han et al [39].…”

Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

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“…The controller was tested in simulation, and the results were verified by testing the strategy in a tank at zero speed. Authors in Rathore and Kumer [11] study proposed a PID controller for an underwater vehicle steering control. The PID controller parameters were optimized based on genetic algorithm and harmonic search method.…”

Section: Introductionmentioning

confidence: 99%

Model Dependent Controller for Underwater Vehicle

Jasim

2017

UHDJST

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Robust Steering Control of Autonomous Underwater Vehicle: based on PID Tuning Evolutionary Optimization Technique

Cited by 12 publications

References 10 publications

Artificial Intelligence Enhanced UUV Actuator Control

Artificial Intelligence Enhanced UUV Actuator Control

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Model Dependent Controller for Underwater Vehicle

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