Experimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems

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dc.contributor.authorYanyong, Sarucha
dc.contributor.authorKaitwanidvilai, Somyot
dc.date.accessioned2026-08-06T10:51:27Z
dc.date.available2026-08-06T10:51:27Z
dc.date.issued2025-06-01
dc.description.abstractThis paper presents a practical solution for an adaptive impedance force controller with online learning capabilities, designed to mitigate the effects of inaccuracies in system identification models. The proposed hybrid algorithm addresses the challenges associated with online learning in real-world machines. Additionally, the system demonstrates the ability to adapt to environmental changes, maintaining high-quality performance despite variations. A sliding surface guarantees system stability, while Particle Swarm Optimization (PSO) optimizes impedance parameters, reducing the risk of local minima. The hybrid algorithm also reduces overshoot and undershoot, resulting in faster system responses. Simulation and experimental results demonstrate that the proposed technique outperforms conventional force control systems in terms of learning ability and overall performance.
dc.identifier.citationResults in Control and Optimization, 19, 2025
dc.identifier.doi10.1016/j.rico.2025.100548
dc.identifier.issn26667207
dc.identifier.other2-s2.0-105000522592
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17062
dc.sourceResults in Control and Optimization
dc.subjectAdaptive force control
dc.subjectHybrid controller
dc.subjectOnline learning control
dc.subjectPSO based learning
dc.subjectRobust sliding mode force control
dc.titleExperimental realization of PSO-based hybrid adaptive sliding mode control for force impedance control systems
dc.typeArticle

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