Adaptive control of Upper Limb Exoskeleton Robot Based on a New Modified Function Approximation Technique (FAT)

2018 ◽  
Author(s):  
Brahim Brahmi ◽  
Maarouf Saad ◽  
Cristobal Ochoa-Luna ◽  
Mohammad H. Rahman ◽  
Stefano Di Gennaro
Keyword(s):  
Adaptive Control ◽  
Upper Limb ◽  
Function Approximation ◽  
Approximation Technique ◽  
Upper Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Function Approximation Technique

Novel Adaptive Reaching Law for Sliding Mode Control of an Upper Limb Exoskeleton Robot*

2020 ◽  
Author(s):  
Brahim Brahmi ◽  
Khaled El-Monajjed ◽  
Mohammad Habibur Rahman ◽  
Tanvir Ahmed ◽  
Claude El-Bayeh ◽  
...  
Keyword(s):  
Sliding Mode Control ◽  
Upper Limb ◽  
Sliding Mode ◽  
Reaching Law ◽  
Mode Control ◽  
Upper Limb Exoskeleton ◽  
Exoskeleton Robot

Position and Attitude Control of Underactuated Drones Using the Adaptive Function Approximation Technique

2020 ◽  
Author(s):  
Azin Shamshirgaran ◽  
Donald Ebeigbe ◽  
Dan Simon
Keyword(s):  
Disturbance Rejection ◽  
Function Approximation ◽  
Control Method ◽  
System Stability ◽  
Linear Feedback ◽  
Approximation Technique ◽  
Adaptive Function ◽  
Edge Tracking ◽  
Highly Nonlinear ◽  
Function Approximation Technique

Abstract Despite the popularity of drones and their relatively simple operation, the underlying control algorithms can be difficult to design due to the drones’ underactuation and highly nonlinear properties. This paper focuses on position and orientation control of drones to address challenges such as path and edge tracking, and disturbance rejection. The adaptive function approximation technique control method is used to control an underactuated and nonlinear drone. The controller utilizes reference attitude signals, that are derived from a proportional derivative (PD) linear feedback control methodology. To avoid analytic expressions for the reference attitude velocities, we employ a continuous-time Kalman filter based on a model of the measurement signal — which is derived by passing the reference attitude position through a low-pass signal differentiator — as a second-order Newtonian system. Stability of the closed loop system is proven using a Lyapunov function. Our design methodology simplifies the control process by requiring only a few tuning variables, while being robust to time-varying and time-invariant uncertainties with unknown variation bounds, and avoids the requirement for the knowledge of the dynamic equation that governs the attitude of the drone. Three different scenarios are simulated and our control method shows better accuracy than the proportional-derivative controller in terms of edge tracking and disturbance rejection.

Sign in / Sign up

Export Citation Format

Share Document