Multi Objective PSO Tuned Fractional Order PID Control of Robotic Manipulator

Chhabra, Himanshu; Mohan, V.; Rani, Asha; Singh, Vijander

doi:10.1007/978-3-319-47952-1_45

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…Researchers have been developing effective control of manipulators using conventional and intelligent control methods. Conventional control methods are PID, FOPID (Chhabra et al, 2016;Mohan et el., 2018), SMC, and adaptive control (Zhang and Wei, 2017). With the advancements in artificial intelligence techniques like FLC, ANN, and metaheuristic algorithms, improved and effective control can be achieved by incorporating conventional control with such techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The authors provided a range of controller gains to design specifications for hardware implementation. Chhabra et al (2016) used multi-objective PSO for the design of PID and FOPID for the trajectory tracking problem of a two-link robotic manipulator. FOPID showed superior performance as compared to the conventional PID.…”

Section: Introductionmentioning

confidence: 99%

“…Particle swarm optimization (PSO) Fractional PID, Fuzzy PID (Chhabra et al, 2016;Lopez-Franco et al, 2020;Yadav et al, 2022;Mohan et al, 2019;Liu et al, 2022)…”

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confidence: 99%

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Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Rawat

Gupta

Sharma

2023

Int. j. math. eng. manag. sci.

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Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Rawat

Gupta

Sharma

2023

Int. j. math. eng. manag. sci.

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“…The primary move is to use SVM as a classifier; it is eligible and valid for application [50][51][52][53][54][55]. The sample of expertise obtained from the PSCAD simulation, the data used for coaching tasks, the seventieth of the details, and the half-hour info checking [40][41][42][43]. For the most part, SVM is used for the fault designation of transmission lines that are serially salaried.…”

Section: Introductionmentioning

confidence: 99%

Smart Technology Based Empirical Mode Decomposition (EMD) Approach for Autonomous Transmission Line Fault Detection Protection

Al-Zubaydi

2022

EAI Endorsed Trans Energy Web

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Many novel technologies of property energy and cell, solar power, batteries, and high-efficient combustion are widely investigated to conserve energy and reduce emissions. Transmission lines (TLs) play a serious role in transmitting generated electricity to different distribution units in facility engineering. The transmission lines function as a link between shoppers and a Power Station. Faults usually occur within the transmission when positioned in an open field. Quick identification and sick line faults square measures required for the conventional operation of the plant. A way like distinct moving ridge rework (DWT) and (EMD) is used to locate and identify faults to resolve this disruption. DWT is used to break down fault transients, as a result of which the info can be collected at the same time in each time and frequency domain. EMD decomposes the TLs voltage into Intrinsic Mode operation (IMFs). Four varieties of fault signals are square measurements produced by the grid-connected facility. Line faults square measure induced MATLAB/Simulink mistreatment.

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“…In literature, various control strategies are utilized for the robotic manipulator such as the proportional integral derivative (PID) controller, [3][4][5][6] the fractional-order PID controller, [7][8][9] the fuzzy logic controller, 10,11 and the neural network. 12 Among these controllers, the model predictive control (MPC) is approved as an effective control technique for linear and nonlinear systems in complicated and simple industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

Elsisi

2020

Int J Intell Syst

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The controller design for the robotic manipulator faces different challenges such as the system's nonlinearities and the uncertainties of the parameters. Furthermore, the tracking of different linear and nonlinear trajectories represents a vital role by the manipulator. This paper suggests an optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA). The proposed modification of the MTOA is carried out based on opposition‐based learning (OBL) and quasi OBL approaches. This modification improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum. The proposed method is applied on the robotic manipulator to track different linear and nonlinear trajectories. The NLMPC parameters are tuned by the MMTOA rather than the trial and error method of the designer. The proposed NLMPC based on MMTOA is compared with the original MTOA, genetic algorithm, and cuckoo search algorithm in literature. The superiority and effectiveness of the proposed controller are confirmed to track different linear and nonlinear trajectories. Furthermore, the robustness of the proposed method is emphasized against the uncertainties of the parameters.

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Multi Objective PSO Tuned Fractional Order PID Control of Robotic Manipulator

Cited by 12 publications

References 7 publications

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Smart Technology Based Empirical Mode Decomposition (EMD) Approach for Autonomous Transmission Line Fault Detection Protection

Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm

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