Multi-objective Cuckoo Search Algorithm-Based 2-DOF FOPD Controller for Robotic Manipulator

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

doi:10.1007/978-981-13-2553-3_33

Cited by 7 publications

(16 citation statements)

References 7 publications

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“…• The proposed controller can track the linear trajectory as shown in Figure 6 and Table 2 with minimum overshoot and settling time compared with the original MTOA, 38 the PID controller based on the MTOA, the PID controller based on the GA, 6 and the PID controller based on the CSA. 7 • As clear in Table 3, the proposed controller has the best performance indices compared with other methods. • The proposed NLMPC can track the nonlinear trajectory with negligible error as shown in Figure 8.…”

Section: Scenario 2: the Tracking Of Nonlinear Trajectoriesmentioning

confidence: 86%

“…As clear in this table, the suggested MMTOA-based NLMPC has the minimum value of the performance index "FOD" compared with the original MTOA, 38 the PID controller based on the MTOA, the PID controller based on the GA, 6 and the PID controller based on the CSA. 7…”

Section: Stopmentioning

confidence: 99%

“…where θ˙d f i , is the desired final velocity of each link, θ df i , is the desired final acceleration of each link, and t f is the final time. The desired final positions of the robotic manipulator links are selected as "θ = 0. rad" at the final time "t f = 4 s," the initial positions and velocities are equal to zero as References [6,7] for a fair comparison. The constants " , and c = −0.125…”

Section: Scenario 2: the Tracking Of Nonlinear Trajectoriesmentioning

confidence: 99%

“…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%

“…The FOD ensures the minimization of the settling time and the maximum overshoot simultaneously. The performance of the proposed NLMPC based on the MMTOA is compared with the PID controller based on the GA, 6 the PID controller based on the cuckoo search algorithm (CSA), 7 and the original MTOA. 38 The superiority of the proposed controller is confirmed to track different trajectories.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Scenario 2: the Tracking Of Nonlinear Trajectoriesmentioning

confidence: 86%

Section: Stopmentioning

confidence: 99%

Section: Scenario 2: the Tracking Of Nonlinear Trajectoriesmentioning

confidence: 99%