Optimal Control of Non-Holonomic Robotic Systems Based on Type-3 Fuzzy Model

Wu, Lili; Huang, Haiyan; Wang, Meng; Alattas, Khalid A.; Mohammadzadeh, Ardashir; Ghaderpour, Ebrahim

doi:10.1109/access.2023.3330244

Cited by 8 publications

(1 citation statement)

References 39 publications

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“…This expansion spans a diverse range of sectors including industry, agriculture, forestry, mining, medicine, surgery, rehabilitation, healthcare, search and rescue, domestic use, operation in hazardous locations, remote area deployment, and entertainment. Wheeled MRs, which interact with surfaces through their wheels, exemplify these systems and are subject to non-holonomic constraints [33]. These constraints arise because the wheels are designed to roll forward without slipping, which limits their movement.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Fuzzy Control for Robotic Systems

Xue,

Zhou,

Chen

et al. 2024

IEEE Access

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The use of wheeled mobile robots (MRs) with symmetrical structure in engineering is rapidly increasing, with applications in various fields, such as industry, agriculture, forestry, healthcare, mining, rehabilitation, search and rescue, household tasks, remote locations, and entertainment. As MRs become more common, researchers are focusing on developing better ways to model and control these robots to improve their performance and adaptability. The main challenges in this area include uncertain dynamics, non-holonomic constraints, and various perturbations, which complicate the design of the control system. This paper presents a new predictive control scheme for MRs that is independent of the dynamics and the robot's working environment. A Type-3 fuzzy logic system is developed to identify the MR dynamics online. The designed predictive scheme improves accuracy and speeds up convergence, while also addressing uncertainties and considering constraints on control input. Additionally, a chaotic-based system is proposed for secure path planning, generating a complex and unpredictable reference trajectory that is useful for patrol MR applications. The effectiveness of the suggested controller is demonstrated through simulations and experiments.

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Section: Introductionmentioning

confidence: 99%

A Constrained Fuzzy Control for Robotic Systems

Xue,

Zhou,

Chen

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

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Trajectory tracking of a wheeled mobile robot based on the predefined‐time sliding mode control scheme

Cong,

Wang,

et al. 2024

Asian Journal of Control

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In this paper, a double‐loop control method is proposed to improve the trajectory tracking performance of a wheeled mobile robot (WMR) with slippage properties and external disturbances. Considering the strong robustness and the ability to enable the system to converge in a short time, the predefined‐time sliding mode control (PTSMC) strategy is applied to the design of a double‐loop controller. Furthermore, a nonlinear disturbance observer (NDO) is adopted to comply with the feedforward compensation of the external disturbances. In turn, the controller only needs to deal with the internal disturbance of the system under the premise of using the NDO, thereby reducing the burden of the sliding mode controller. Based on Lyapunov methods, the stability of the double‐loop controller and the NDO is analyzed. Finally, the feasibility of the double‐loop control strategy is proven by simulations of the WMR operating along circular, sin‐shaped, and eight‐shaped roads.

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Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Cai,

Zhang,

Khadakar

et al. 2024

Complex Intell. Syst.

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Unmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

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Optimal Control of Non-Holonomic Robotic Systems Based on Type-3 Fuzzy Model

Cited by 8 publications

References 39 publications

A Constrained Fuzzy Control for Robotic Systems

A Constrained Fuzzy Control for Robotic Systems

Trajectory tracking of a wheeled mobile robot based on the predefined‐time sliding mode control scheme

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

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