Robust nonlinear fractional order fuzzy PD plus fuzzy I controller applied to robotic manipulator

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

doi:10.1007/s00521-019-04074-3

Cited by 31 publications

(10 citation statements)

References 46 publications

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“…Moreover, the performance of this fractional-order PID is further enhanced using intelligent fuzzy techniques to achieve better servo and regulatory responses. Therefore, various combinations of fractionalorder PID and fuzzy logic are proposed in the literature to form fractional-order fuzzy PID controller for two-link [4,39,43,44,51,62,63,67,79,92,95], three-link manipulators [48,65], SCARA [31,53], PUMA 560 [30], and Stewart platforms [89]. In addition, the authors of [64] have proposed a hybrid two-degree-of-freedom fractional-order fuzzy PID controller by combining two-degree-of-freedom PID, fractional-order concept, and fuzzy logic.…”

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

“…Further, to incorporate the self-tuning of controller parameters rather than designing using precise mathematics, researchers have used several optimization techniques where the non-linear controller gains are updated in real-time using error and fractional rate of error. The optimization techniques used in the literature are artificial bee colony [39,43,53,95], genetic algorithm [30,39,43,64,79], cuckoo search [4,31,48,51,63], backtracking search [44,65], dragonfly [79], ant lion optimizer [79], particle swarm optimization [67,89] and grey wolf optimizer [95]. The robustness testing of these self-tuned fractional-order fuzzy PID controllers has shown superior tracking results in comparison to the conventional counterparts.…”

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

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A Review on Fractional-Order Modelling and Control of Robotic Manipulators

2023

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Robot manipulators are widely used in many fields and play a vital role in the assembly, maintenance, and servicing of future complex in-orbit infrastructures. They are also helpful in areas where it is undesirable for humans to go, for instance, during undersea exploration, in radioactive surroundings, and other hazardous places. Robotic manipulators are highly coupled and non-linear multivariable mechanical systems designed to perform one of these specific tasks. Further, the time-varying constraints and uncertainties of robotic manipulators will adversely affect the characteristics and response of these systems. Therefore, these systems require effective modelling and robust controllers to handle such complexities, which is challenging for control engineers. To solve this problem, many researchers have used the fractional-order concept in the modelling and control of robotic manipulators; yet it remains a challenge. This review paper presents comprehensive and significant research on state-of-the-art fractional-order modelling and control strategies for robotic manipulators. It also aims to provide a control engineering community for better understanding and up-to-date knowledge of fractional-order modelling, control trends, and future directions. The main table summarises around 95 works closely related to the mentioned issue. Key areas focused on include modelling, fractional-order modelling type, model order, fractional-order control, controller parameters, comparison controllers, tuning techniques, objective function, fractional-order definitions and approximation techniques, simulation tools and validation type. Trends for existing research have been broadly studied and depicted graphically. Further, future perspective and research gaps have also been discussed comprehensively.

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Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

Section: Fractional-order Fuzzy Pid Controllersmentioning

confidence: 99%

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

2023

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“…The supremacy of NSGA-II lies in converting multiple objectives into a single measure by generating a set of Pareto fronts, sorted on the basis of non-domination. To solve multiple objectives problems in engineering fields, NSGA-II is implemented because of its efficiency, simplicity, and elitism [9,10,13,47,48]. NSGA-II performs all the computations in this work and finds the controller parameters for its optimum performance.…”

Section: Multiobjective Optimisation and Nsga-iimentioning

confidence: 99%

“…Including fractional order (FO) operators in design adds an extra degree of freedom, providing flexibility and superior performance. Therefore, the application of fractional differ-integral operators in controllers has gained recent research thrust in the field of control of motor [1], renewable energy [2,3], process control [4,5], power system [6,7], robotics [8][9][10][11], tumour growth control [12,13], and automatic voltage regulation [14]. Moreover, it is widely utilised with advanced intelligent techniques for the design and control of real-world systems.…”

Section: Introductionmentioning

confidence: 99%

Self-regulatory Fractional Fuzzy Control for Dynamic Systems: An Analytical Approach

Mohan

Panjwani

Chhabra

et al. 2022

Int. J. Fuzzy Syst.

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This paper presents an analytical design of a fractional order fuzzy proportional integral plus derivative (FOFPI + D) controller. Artificial intelligence is incorporated into the controller with the help of a formula-based fuzzy logic system. The designed scheme combines fractional order fuzzy PI (FOFPI) and fractional order fuzzy D (FOFD) controller, derived from fundamental FOPID control law. The proposed scheme enjoys the linear structure of the FOPID controllers with non-linear gains that provide self-tuning control capability. The sufficient condition for stability of the closed-loop system is also established using the graphical approach. Performance of the proposed FOFPI + D, its integer order variant (FPI + D), and conventional controllers is examined for control of a highly non-linear and uncertain two-link robotic manipulator system. The optimum parameters of controllers are found by minimising aggregated control variation and error objective through non-dominated sorting genetic algorithm-II (NSGA-II). The comparison for trajectory tracking shows that FOFPI + D has the minimum integral absolute error (IAE) compared to other controllers. Further, rigorous performance investigations are performed to verify the robustness of designed controllers against parametric uncertainties, the varying boundary conditions of reference trajectory and disturbance rejection. It is concluded from the results that the proposed FOFPI + D controller exhibits superior performance.

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“…(Ngôn & Nghiệm, 2020). Việc lựa chọn các khâu khuếch đại GE, GCE, GIE và GU cũng cần thỏa mãn quan hệ (4) (Chhabra et al, 2020;Ahmed et al, 2022), với K p là độ lợi tỷ lệ và T i là thời hằng tích phân. Ngoài ra, giá trị khâu khuếch đại GE và GCE cần được chọn lựa sao cho tín hiệu e và de đi qua 2 khâu này phải có biên độ nằm trong 55 giới hạn hoạt động của 2 ngõ vào của bộ điều khiển mờ.…”

Section: Phương Pháp Nghiên Cứuunclassified

Khảo sát ảnh hưởng của các tham số bộ điều khiển fuzzy PD + I trên động cơ không đồng bộ ba pha

Nguyễn¹,

Lâm²,

Nguyen³

2022

CTUJSVN

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Bộ điều khiển vi phân tỷ lệ mờ cộng tích phân điều khiển mô hình động cơ không đồng bộ ba pha được xây dựng trong bài viết. Động cơ không đồng bộ ba pha được mô phỏng trên MATLAB®/Simulink dựa theo phương trình trạng thái trên hệ tọa độ từ thông rotor (hệ tọa độ dq). Mô phỏng với tải khác nhau và có nhiễu tác động vào hệ thống cũng như so sánh với PI và khảo sát ảnh hưởng của các tham số của bộ điều khiển vi phân tỷ lệ mờ cộng tích phân. Kết quả mô phỏng cho thấy giải pháp đề xuất đã đáp ứng tốt hơn so với bộ điều khiển PI thông thường, thời gian xác lập của hệ thống hợp lý, độ vọt lố bị triệt tiêu và sai số ngõ ra thấp. Ngoài ra, bộ điều khiển còn ổn định với nhiễu tác động vào hệ thống.

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Robust nonlinear fractional order fuzzy PD plus fuzzy I controller applied to robotic manipulator

Cited by 31 publications

References 46 publications

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

A Review on Fractional-Order Modelling and Control of Robotic Manipulators

Self-regulatory Fractional Fuzzy Control for Dynamic Systems: An Analytical Approach

Khảo sát ảnh hưởng của các tham số bộ điều khiển fuzzy PD + I trên động cơ không đồng bộ ba pha

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