Trajectory tracking performance in task space of robot manipulators: an adaptive neural controller design

Author(s):  
N.A. Martins ◽  
M.F. Figueiredo ◽  
P.C. Goncalves ◽  
M. de Alencar ◽  
F.A.R. de Alencar
2012 ◽  
Vol 26 (8) ◽  
pp. 2313-2323 ◽  
Author(s):  
Jungmin Kim ◽  
Naveen Kumar ◽  
Vikas Panwar ◽  
Jin-Hwan Borm ◽  
Jangbom Chai

2011 ◽  
Vol 110-116 ◽  
pp. 3176-3183 ◽  
Author(s):  
Mao Hsiung Chiang ◽  
Hao Ting Lin

This study aims to develop a leveling position control of an active PWM-controlled pneumatic isolation table system. A novel concept using parallel dual-on/off valves with PWM control signals is implemented to realize active control and to improve the conventional pneumatic isolation table that supported by four pneumatic cushion isolators. In this study, the cushion isolators are not only passive vibration isolation devices, but also pneumatic actuators in active position control. Four independent closed-loop position feedback control system are designed and implemented for the four axial isolators. In this study, on/off valves are used, and PWM is realized by software. Therefore, additional hardware circuit is not required to implement PWM and not only cost down but also reach control precision of demand. In the controller design, the Fourier series-based adaptive sliding-mode controller with H∞ tracking performance is used to deal with the uncertainty and time-varying problems of pneumatic system. Finally, the experiments on the pneumatic isolation table system for synchronous position and trajectory tracking control, including no-load and loading conditions, and synchronous position control with master-slave method, are implemented in order to verify that the controller for each cushion isolator can realize good position and trajectory tracking performance.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiutai Liu ◽  
Xiucheng Dong ◽  
Yong Yang ◽  
Hongyu Chen

This paper aims at the trajectory tracking problem of robot manipulators performing repetitive tasks in task space. Two control schemes are presented to conduct trajectory tracking tasks under uncertain conditions including unmodeled dynamics of robot and additional disturbances. The first controller, pure adaptive iterative learning control (AILC), is based upon the use of a proportional-derivative-like (PD-like) feedback structure, and its design seems very simple in the sense that the only requirement on the learning gain and control parameters is the positive definiteness condition. The second controller is designed with a combination of AILC and neural networks (NNs) where the AILC is adopted to learn the periodic uncertainties that attribute to the repetitive motion of robot manipulators while the add-on NNs are used to approximate and compensate all nonperiodic ones. Moreover, a combined error factor (CEF), which is composed of the weighted sum of tracking error and its derivative, is designed for network updating law to improve the learning speed as well as tracking accuracy of the system. Stabilities of the controllers and convergence are proved rigorously by a Lyapunov-like composite energy function. The simulations performed on two-link manipulator are provided to verify the effectiveness of the proposed controllers. The results of compared simulations illustrate that our proposed control schemes can significantly conduct trajectory tracking tasks.


Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 248
Author(s):  
Useok Jung ◽  
Moon-Gyeang Cho ◽  
Ji-Won Woo ◽  
Chang-Joo Kim

This paper treats a robust adaptive trajectory-tracking control design for a rotorcraft using a high-fidelity math model subject to model uncertainties. In order to control the nonlinear rotorcraft model which shows strong inter-axis coupling and high nonlinearity, incremental backstepping approach with state-dependent control effectiveness matrix is utilized. Since the incremental backstepping control suffers from performance degradation in the presence of control matrix uncertainties due to change of flight conditions, control system robustness is improved by combining the least squares parameter estimator to estimate time varying uncertainties contained in the control effectiveness matrix. Also, by selecting a suitable gain set by investigating the error dynamics, a uniform trajectory-tracking performance over operational flight envelope of the rotorcraft is ensured without resorting to the conventional gain scheduling method. To evaluate the proposed controller, comparative results between IBSC and Adaptive IBSC are provided in this paper with sequential maneuvers from the ADS-33E-PRF. The proposed method shows improved tracking performance under variations in control effective matrix in the flight simulation. Robust and stable parameter estimation is also guaranteed due to the implementation of the DF-RLS algorithm for the least squares estimator.


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