Initialized Model Reference Adaptive Control for Lower Limb Exoskeleton

Amiri, Mohammad Soleimani; Ramli, Rizauddin; Ibrahim, Mohd Faisal

doi:10.1109/access.2019.2954110

Cited by 30 publications

(27 citation statements)

References 37 publications

(37 reference statements)

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“…The identical initial condition (4) is satisfied. The control law is designed as (5) and (6). The following conclusions are satisfied.…”

Section: Iiiconvergence Analysis Of Model Reference Adaptive Iteratimentioning

confidence: 81%

“…Where, ˆ( , ) a k i and ˆ( , ) b k i are adjustable parameters in the kth cycle. It is updated by the parameter iterative learning adaptive law shown in (6).…”

Section: Model Reference Adaptive Iterative Learning Control Algomentioning

confidence: 99%

“…Parameter iterative learning adaptive law (6) Γ takes the positive definite diagonal matrix in the form of (25). According to (5) and (6), the value of matrix Γ determines the adaptive adjustment rate of controller parameters â and b . The larger the value of Γ, the larger the parameter adjustment amount.…”

Section: ( ) ( ) ( ) ( ) ( ) U K I a K I Y K I B K I Y K Imentioning

confidence: 99%

“…With the increase of the number of iterations, the change rate of the control quantity is accelerated, so that the step response speed is accelerated. According to (5) and (6), it can be seen that the acceleration of the change rate of the control quantity must be related to the change of the coefficients a and b. The corresponding changing curve of a and b are shown in Fig.…”

Section: ( ) ( ) ( ) ( ) ( ) U K I a K I Y K I B K I Y K Imentioning

confidence: 99%

“…Since the model reference adaptive control (MRAC) strategy was proposed, it has been widely used in various practical control systems due to its adaptive ability to changing controlled objects and the stability guaranteed by the design process based on the Lyapunov function [1]- [6]. In particular, MIT control strategy with the simplest form [7]- [8] and MRAC strategy based on input and output variables [9] are widely used.…”

Section: Introductionmentioning

confidence: 99%

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Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

Jingzhuo

Huang

2020

IEEE Access

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In the design process of the controller, the adaptive gain of model reference adaptive control (MRAC) often requires a tradeoff between the adaptive ability, robustness and stability of the control system. The tradeoff of adaptive gain leads to poor control performance and increase design difficulty. Aiming at this problem, the iterative learning idea is introduced into the model reference adaptive control strategy. The control parameter adaptive law based on the parameters of the previous control process is designed. For scalar systems, a new control strategy is constructed, which is the combination of MRAC and iterative learning control (ILC). The adaptive ability of the model reference adaptive controller is improved by using learning ability of ILC. An appropriate composite energy function is designed to prove the uniform convergence of the proposed control strategy and the boundedness of the control quantity. The proposed control strategy is applied to the ultrasonic motor. The effectiveness of the proposed control strategy is verified by experiments and simulations. The controller is designed by using the first-order model that is large different from the actual object. It verifies that the control strategy has strong robustness to model deviation and online time-varying characteristics.

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“…The identical initial condition (4) is satisfied. The control law is designed as (5) and (6). The following conclusions are satisfied.…”

Section: Iiiconvergence Analysis Of Model Reference Adaptive Iteratimentioning

confidence: 81%

“…Where, ˆ( , ) a k i and ˆ( , ) b k i are adjustable parameters in the kth cycle. It is updated by the parameter iterative learning adaptive law shown in (6).…”

Section: Model Reference Adaptive Iterative Learning Control Algomentioning

confidence: 99%

Section: ( ) ( ) ( ) ( ) ( ) U K I a K I Y K I B K I Y K Imentioning

confidence: 99%

Section: ( ) ( ) ( ) ( ) ( ) U K I a K I Y K I B K I Y K Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

Jingzhuo

Huang

2020

IEEE Access

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Hybrid Design of Model Reference Adaptive Controller and PID Controller for Lower Limb Exoskeleton Application

Aliman

Ramli

Haris

2021

Lecture Notes in Mechanical Engineering

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Optimal robust control with cooperative game theory for lower limb exoskeleton robot

et al. 2022

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For achieving trajectory tracking issue of the lower limb exoskeleton robot, a novel optimal robust control with cooperative game theory is proposed. The uncertainties are considered (possible time-varying, bounded and fast) and the fuzzy set theory is creatively adopted to describe the boundary. From the view of analytical mechanics, the trajectory tracking is treated as the constraints control problem, including holonomic and nonholonomic constraints. A robust control is designed with two adjustable parameters to guarantee the uniform boundedness and uniform ultimate boundedness. For obtaining the optimal selection of two adjustable parameters, a novel optimal control employed cooperative game theory is proposed. Combining the robust control and optimal design, optimal robust control is formulated. The Pareto optimal solution is obtained to guarantee the minimum control cost. In the simulation, the adaptive robust control of Sun is chosen as a comparison. The existence of Pareto optimality and the effectiveness of optimal robust control have been verified via simulation results.

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Initialized Model Reference Adaptive Control for Lower Limb Exoskeleton

Cited by 30 publications

References 37 publications

Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

Hybrid Design of Model Reference Adaptive Controller and PID Controller for Lower Limb Exoskeleton Application

Optimal robust control with cooperative game theory for lower limb exoskeleton robot

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