Review on Sliding Mode Controller and Its Modified Types for Rehabilitation Robots

Ahmed, Syed Faiz; Raza, Yarooq; Mahdi, Hussain Falih; Muhamad, Wan Mansor Wan; Joyo, M. Kamran; Shah, Asadullah; Koondhar, M. Y.

doi:10.1109/icetas48360.2019.9117390

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…The second part is the design of a switching function that ensures the system slides in the desired sliding surface. The theory of SMC is reviewed in details in [52][53][54].…”

Section: Sliding Mode Controllermentioning

confidence: 99%

Lateral control for autonomous wheeled vehicles: A technical review

Kebbati

Ait-Oufroukh

Ichalal

et al. 2022

Asian Journal of Control

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Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision-making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments.

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“…The second part is the design of a switching function that ensures the system slides in the desired sliding surface. The theory of SMC is reviewed in details in [52][53][54].…”

Section: Sliding Mode Controllermentioning

confidence: 99%

Lateral control for autonomous wheeled vehicles: A technical review

Kebbati

Ait-Oufroukh

Ichalal

et al. 2022

Asian Journal of Control

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“…The sliding mode control is a nonlinear control that derives the state into the sliding surface defined only with states without uncertainties. Thus, it provides a very robust control performance to uncertainties such as disturbance, delay, and system model error [37]. The SMC has been shown to provide robust performance for the consensus of MAS with disturbance [38], actuator faults [39], and delay [40].…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay

Yang

2022

Electronics

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Despite the enormous progress in consensus control of a multi-agents system (MAS), amodel-based consensus control is valid only when the assumption on the system environment and on the model is valid. To overcome this limitation, several deep learning (DL) based consensus controls directly learn how to generate a control signal from the model-based control. Depending on the exploitation of knowledge from the model-based control structure, four different deep learning models were considered. Numerical simulations of MAS with unknown time-varying delays and disturbance verify that, while providing comparable performance to the model-based control for many different system configurations, the DL-based controls with explicit knowledge of the control signal structure are preferred to that with implicit knowledge of the control signal or no knowledge, which shows the promising potential of DL-based control with supervised learning.

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“…Controlling rehabilitation robots effectively is crucial for their success in patient therapy (Ahmed et al, 2019;Li et al, 2023). Model predictive control (MPC) emerges as a promising candidate due to its forward-looking nature and ability to handle multivariable systems with constraints (Drgoňa et al, 2020;Jahanshahi et al, 2021;Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Kriging-based Model Predictive Control for Lower-limb Rehabilitation Robots

Alotaibi,

Alsubaie

2024

Journal of Disability Research

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Model predictive control (MPC) has emerged as a predominant method in the realm of control systems; yet, it faces distinct challenges. First, MPC often hinges on the availability of a precise and accurate system model, where even minor deviations can drastically affect the control performance. Second, it entails a high computational load due to the need to solve complex optimization problems in real time. This study introduces an innovative method that harnesses the probabilistic nature of Gaussian processes (GPs), offering a solution that is robust, adaptive, and computationally efficient for optimal control. Our methodology commences with the collection of data to learn optimal control policies. We then proceed with offline training of GPs on these data, which enables these processes to accurately grasp system dynamics, establish input–output relationships, and, crucially, identify uncertainties, thereby informing the MPC framework. Utilizing the mean and uncertainty estimates derived from GPs, we have crafted a controller that is capable of adapting to system deviations and maintaining consistent performance, even in the face of unforeseen disturbances or model inaccuracies. The convergence of the closed-loop system is assured through the application of the Lyapunov stability theorem. In our numerical experiments, the exemplary performance of our approach is demonstrated, notably in its capacity to adeptly handle the complexities of dynamic systems, even with limited training data, underlining a significant leap forward in MPC strategies.

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Review on Sliding Mode Controller and Its Modified Types for Rehabilitation Robots

Cited by 11 publications

References 13 publications

Lateral control for autonomous wheeled vehicles: A technical review

Lateral control for autonomous wheeled vehicles: A technical review

Deep Learning-Based Consensus Control of a Multi-Agents System with Unknown Time-Varying Delay

Kriging-based Model Predictive Control for Lower-limb Rehabilitation Robots

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