Adaptive robust motion/force control of constrained mobile manipulators using RBF neural network

This paper presents a method for trajectory tracking of constrained mobile manipulators under both holonomic and nonholonomic constraints. An adaptive robust motion/force controller with a Radial Basis Function (RBF) neural network is proposed to improve the tracking control based on the dynamic mod...

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Bibliographic Details
Published in:International journal of dynamics and control 2024, Vol.12 (9), p.3379-3391
Main Authors: Rani, Shivani, Kumar, Amit, Kumar, Naveen, Singh, Harendra Pal
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive systems
Closed loops
Compensators
Complexity
Constraints
Control
Control and Systems Theory
Control stability
Controllers
Dynamic models
Dynamical Systems
Engineering
Feedback control
Manipulators
Neural networks
Parameter estimation
Parameter uncertainty
Radial basis function
Robot arms
Robust control
Stability
Tracking control
Tracking errors
Trajectory analysis
Trajectory control
Vibration
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Summary:This paper presents a method for trajectory tracking of constrained mobile manipulators under both holonomic and nonholonomic constraints. An adaptive robust motion/force controller with a Radial Basis Function (RBF) neural network is proposed to improve the tracking control based on the dynamic model with external disturbances and parameter uncertainties. In this proposed control strategy, the model-based robust compensator is associated with an adaptive controller, which is based on model-free RBF network; it can effectively enhance the control performance. With the proposed method, the trajectory and constraint force can be converged asymptotically to their respective values to reach the desired levels. Neural network estimation errors, as well as boundedness on parameter uncertainties, are all strictly controlled by an updated law of adaptive control. Lyapunov stability theory and the stability of a closed-loop system are ensured by the adaptive robust controller. Finally, simulation results of the proposed approach in a comparative manner with model-based and model-free controllers are performed to demonstrate the adequate performance of adaptive robust control in tracking errors and have a faster reduction rate.
ISSN:2195-268X
2195-2698
DOI:10.1007/s40435-024-01418-3