PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

Duong, Hung Quoc; Quang, Nguyen Hong; Nguyen, Doan Trang; Nguyen, Lanh Van

doi:10.28991/esj-2022-06-02-01

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…Furthermore, in this study, the uncertain element originates from the joint stiffness, specifically the coefficient k s in the system model Eqs. ( 6) and (7), with a parametric variation of ±20%.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…A PID control technique was used to control flexible manipulators. Trajectory tracking results and reducing undesirable vibrations witnessed the effectiveness of the developed control strategy in the studies of Alam et al [6] and Duong et al [7]. SMC techniques for the tracking trajectory problem of an FJR arm and the altitude of quadrotor Unmanned Aerial Vehicle (UAV) with feedback linearization or disturbance observer have been proposed in the literature [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

Mohammad,

AL-Samarraie,

Jaber

2024

International Journal on Smart Sensing and Intelligent Systems

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This paper proposes and evaluates a novel control approach for trajectory tracking, stability enhancement, and vibration reduction of a flexible joint robot (FJR). The FJR is a 2-degree-of-freedom underactuated nonlinear system that is challenging to control due to vibration, underactuation, uncertainties, and external disturbances. The control objectives are high trajectory tracking performance together with suppressing vibration. The proposed control approach uses integral sliding mode control (ISMC) combined with a barrier function based on back-stepping. This ensures robust and smooth performance by eliminating the reaching phase where sliding mode control (SMC) is typically not robust. Robustness is thus guaranteed from the start. The FJR is modeled as a 4th-order system using Lagrangian mechanics and decomposed into two 2nd-order subsystems for control design. Sliding variables are defined for each subsystem. Using these variables, the proposed control achieves robust trajectory tracking, stability, and vibration reduction. Numerical simulations in MATLAB validate the superior performance of the proposed ISMC-barrier function control compared to conventional ISMC for the FJR. This novel control approach addresses the challenges of controlling this FJR.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

Mohammad,

AL-Samarraie,

Jaber

2024

International Journal on Smart Sensing and Intelligent Systems

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show abstract

“…In supervised learning, the correct output data for a set of input data is known in advance. This data is provided semi-automatically or by a supervisor [31].…”

Section: -Artificial Neural Networkmentioning

confidence: 99%

Trajectory Tracking Control of a Mobile Robot using Neural Networks

Trujillo,

Morales,

Chávez

et al. 2023

Emerg Sci J

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This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots through the application of neural networks. The goal of this approach is to enhance the accuracy and overall performance of trajectory tracking without the need for manual gain recalibration, which is a tedious and time-consuming task for the designer when setting up the robot. This improvement is achieved by creating a kinematic controller based on neural networks, which are constructed using the kinematic model of the robot. In the initial phase, the controller requires gains defined by the designer. Subsequently, during the application phase, the backpropagation algorithm is used to dynamically adjust the gains of the neural network, aiming to minimize the closed-loop error. One of the key innovations introduced by this controller is the potential for automatic online gain tuning, thereby eliminating the need for a pre-learning phase, typically required by traditional neural controllers. To validate the effectiveness of this approach, the results are systematically analyzed and compared against those obtained using a conventional kinematic controller. Performance metrics reveal the improved precision in trajectory tracking achieved by the controller, with reduced effort, highlighting the performance enhancements in different trajectories. Doi: 10.28991/ESJ-2023-07-06-01 Full Text: PDF

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“…In advance, Particle Swarm Optimization (APSO) inertia weight is updated with constriction factor to avoid the local optimal problem. The accuracy of the proposed algorithm was also checked with the different numbers of a neuron [49]. The proposed algorithms perform faster convergence than backpropagation neural networks.…”

Section: Related Workmentioning

confidence: 99%

An Improved Particle Swarm Optimization Algorithm for Data Classification

Bangyal¹,

Nisar

Soomro

et al. 2022

Applied Sciences

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Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real world. The main problem PSO faces is premature convergence due to lack of diversity, and it is usually stuck in local minima when dealing with complex real-world problems. In meta-heuristic algorithms, population initialisation is an important factor affecting population diversity and convergence speed. In this study, we propose an improved PSO algorithm variant that enhances convergence speed and population diversity by applying pseudo-random sequences and opposite rank inertia weights instead of using random distributions for initialisation. This paper also presents a novel initialisation population method using a quasi-random sequence (Faure) to create the initialisation of the swarm, and through the opposition-based method, an opposite swarm is generated. We proposed an opposition rank-based inertia weight approach to adjust the inertia weights of particles to increase the performance of the standard PSO. The proposed algorithm (ORIW-PSO-F) has been tested to optimise the weight of the feed-forward neural network for fifteen data sets taken from UCI. The proposed techniques’ experiment result depicts much better performance than other existing techniques.

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PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

Cited by 12 publications

References 19 publications

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

Barrier Function-Based Integral Sliding Mode Controller Design for a Single-Link Rotary Flexible Joint Robot

Trajectory Tracking Control of a Mobile Robot using Neural Networks

An Improved Particle Swarm Optimization Algorithm for Data Classification

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