Chaotic Whale Optimized Fractional Order PID Controller Design for Desalination Process

Kavin, F.; Senthilkumar, R.

doi:10.32604/cmc.2022.021577

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…In power systems, using a fractional-order PID controller to control grid frequency and voltage ensures the stable operation of the power system [115]. In industrial water treatment equipment, using a fractional-order PID controller to control water quality and flow can improve water treatment efficiency [116]. In petrochemical production, using a fractional-order PID controller to control temperature, pressure, and flow during the production process can improve product quality [117].…”

Section: Comparison With the Existing Literaturementioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

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The suspension system is a crucial part of an electric vehicle, which directly affects its handling performance, driving comfort, and driving safety. The dynamics of the 8-DoF full-vehicle suspension with seat active control are established based on rigid-body dynamics, and the time-domain stochastic excitation model of four tires is constructed by the filtered white noise method. The suspension dynamics model and road surface model are constructed on the Matlab/Simulink simulation software platform, and the simulation study of the dynamic characteristics of active suspension based on the fractional-order PIλDµ control strategy is carried out. The three performance indicators of acceleration, suspension dynamic deflection, and tire dynamic displacement are selected to construct the fitness function of the genetic algorithm, and the structural parameters of the fractional-order PIlDm controller are optimized using the genetic algorithm. The control effect of the optimized fractional-order PIlDm controller based on the genetic algorithm is analyzed by comparing the integer-order PID control suspension and passive suspension. The simulation results show that for optimized fractional-order PID control suspension, compared with passive suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions reaches over 25%, the average optimization of suspension dynamic deflection exceeds 30%, and the average optimization of tire dynamic displacement is 5%. However, compared to the integer-order PID control suspension, the average optimization of the root mean square (RMS) of acceleration under random road conditions decreased by 5%, the average optimization of suspension dynamic deflection increased by 3%, and the average optimization of tire dynamic displacement increased by 2%.

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Section: Comparison With the Existing Literaturementioning

confidence: 99%

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Yin,

Wang,

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…The fractional-order PID controller has a higher degree of freedom and matching than the conventional PID controller. Kavin et al implemented a fractional-order PID controller in a reverse-osmosis desalination system and optimised the controller using the chaotic whale method [20]. Chiranjeevi proposed and implemented a fractional-order proportional-integral-derivative (PID) controller that used a pollination algorithm to control the motor speed, effectively improving the performance of the system [21].…”

Section: Introductionmentioning

confidence: 99%

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

Zhang,

Xiong

et al. 2024

Fractal Fract

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The vibration of piping systems is one of the most important causes of accelerated equipment wear and reduced work efficiency and safety. In this study, an active vibration control method based on a fractional-order proportional–integral–derivative (PID) controller was proposed to suppress pipeline vibration and reduce pipeline damage. First, a mathematical model of the distributed piping system was established using the finite element analysis method, and the characteristics of the distributed piping system were studied effectively. Further, the time-frequency domain parameter identification method was used to realise the system identification of the cross-point vibration transfer function between the brake and sensor, and the particle swarm optimisation algorithm was utilised to further optimise the transfer function parameters to improve the system identification accuracy. Therefore, a fractional-order PID controller was designed using the D-decomposition method, and the optimal controller parameters were obtained. The experimental and numerical simulation results show that the improved system identification algorithm can significantly improve modelling accuracy. In addition, the designed fractional-order PID controller can effectively reduce the system’s overshoot, oscillation time, and adjustment time, thereby reducing the vibration response of piping systems.

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“…The Dual-loop PID control algorithm [4] is often used in the control of balancing robots [5], while it is difficult to tune the PID parameters simply by manual. Many studies [6][7][8][9][10][11][12][13][14][15][16][17][18] have proposed different methods to optimize the PID algorithm, which enable the PID algorithm to have better performance and to be applied to various scenarios. The merit of the PID parameters determines the effectiveness of the control algorithm as well as the accuracy and stability of balancing robot control.…”

Section: Introductionmentioning

confidence: 99%

“…In [17], the Whale Optimization Algorithm (WOA) is applied to the PID con-troller which achieved the effective trajectory tracking control of the robot manipulator. In [18], the chaotic whale algorithm (CWOA) is proposed to optimize the parameter tuning of the FOPID control algorithm. The CWOA-FOPID control algorithm obtains better ITAE and ISE values during the optimization search process, and the time-domain metrics of the output response are better than those obtained by other existing controllers in the reverse osmosis desalination process.…”

Section: Introductionmentioning

confidence: 99%

CFHBA-PID Algorithm: Dual-Loop PID Balancing Robot Attitude Control Algorithm Based on Complementary Factor and Honey Badger Algorithm

Lin

Zheng

Zhang

et al. 2022

Sensors

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The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot.

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Chaotic Whale Optimized Fractional Order PID Controller Design for Desalination Process

Cited by 4 publications

References 24 publications

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

Fractional-Order PIλDμ Control to Enhance the Driving Smoothness of Active Vehicle Suspension in Electric Vehicles

System Identification and Fractional-Order Proportional–Integral–Derivative Control of a Distributed Piping System

CFHBA-PID Algorithm: Dual-Loop PID Balancing Robot Attitude Control Algorithm Based on Complementary Factor and Honey Badger Algorithm

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