A stochastic strategy integrating wind compensation for trajectory tracking in aircraft motion control

2016 ◽  
Author(s):  
Luca Deori ◽  
Simone Garatti ◽  
Maria Prandini
Keyword(s):  
Motion Control ◽  
Trajectory Tracking ◽  
Aircraft Motion

Robust motion control for multi-split transmission line four-wheel driven mobile operation robot in extreme power environment

2020 ◽  
Vol 47 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Hong Jun Li ◽  
Wei Jiang ◽  
Dehua Zou ◽  
Yu Yan ◽  
An Zhang ◽  
...  
Keyword(s):  
Motion Control ◽  
Trajectory Tracking ◽  
Control Method ◽  
Sliding Mode ◽  
Variable Structure ◽  
Control Model ◽  
Joint Motion ◽  
Content Type ◽  
Structure Control ◽  
Sliding Mode Variable Structure

Purpose In the multi-splitting transmission lines extreme power environment of ultra-high voltage and strong electromagnetic interference, to improve the trajectory tracking and stability control performance of the robot manipulator when conduct electric power operation, and effectively reduce the influence of disturbance factors on the robot motion control, this paper aims to presents a robust trajectory tracking motion control method for power cable robot manipulators based on sliding mode variable structure control theory. Design/methodology/approach Through the layering of aerial-online-ground robot three-dimensional control architecture, the robot joint motion dynamic model has been built, and the motion control model of the N-degrees of freedom robot system has also been obtained. On this basis, the state space expression of joint motion control under disturbance and uncertainty has been also derived, and the manipulator sliding mode variable structure trajectory tracking control model has also been established. The influence of the perturbation control parameters on the robot motion control can be compensated by the back propagation neural network learning, the stability of the controller has been analyzed by using Lyapunov theory. Findings The robot has been tested on a analog line in the lab, the effectiveness of sliding mode variable structure control is verified by trajectory tracking simulation experiments of different typical signals with different methods. The field operation experiment further verifies the engineering practicability of the control method. At the same time, the control method has the remarkable characteristics of sound versatility, strong adaptability and easy expansion. Originality/value Three-dimensional control architecture of underground-online-aerial robots has been proposed for industrial field applications in the ubiquitous power internet of things environment (UPIOT). Starting from the robot joint motion, the dynamic equation of the robot joint motion and the state space expression of the robot control system have been established. Based on this, a robot closed-loop trajectory tracking control system has been designed. A robust trajectory tracking motion control method for robots based on sliding mode variable structure theory has been proposed, and a sliding mode control model for the robot has been constructed. The uncertain parameters in the control model have been compensated by the neural network in real-time, and the sliding mode robust control law of the robot manipulator has been solved and obtained. A suitable Lyapunov function has been selected to prove the stability of the system. This method enhances the expansibility of the robot control system and shortens the development cycle of the controller. The trajectory tracking simulation experiment of the robot manipulator proves that the sliding mode variable structure control can effectively restrain the influence of disturbance and uncertainty on the robot motion stability, and meet the design requirements of the control system with fast response, high tracking accuracy and sound stability. Finally, the engineering practicability and superiority of sliding mode variable structure control have been further verified by field operation experiments.

VISION BASED MOTION CONTROL AND TRAJECTORY TRACKING FOR MICROASSEMBLY ROBOTS

2007 ◽  
Vol 04 (03) ◽  
pp. 237-249
Author(s):  
MIN WANG ◽  
XIADONG LV ◽  
XINHAN HUANG
Keyword(s):  
Motion Control ◽  
Trajectory Tracking ◽  
Visual Servoing ◽  
Control Method ◽  
Focal Depth ◽  
Least Square ◽  
Energy Curve ◽  
Least Square Estimator ◽  
Robotic Motion ◽  
Optimizing Control

This paper presents a vision based motion control and trajectory tracking strategies for microassembly robots including a self-optimizing visual servoing depth motion control method and a novel trajectory snake tracking strategy. To measure micromanipulator depth motion, a normalized gray-variance focus measure operator is developed using depth from focus techniques. The extracted defocus features are theoretically distributed with one peak point which can be applied to locate the microscopic focal depth via self-optimizing control. Tracking differentiators are developed to suppress noises and track the features and their differential values without oscillation. Based on the differential defocus signals a coarse-to-fine self-optimizing controller is presented for micromanipulator to precisely locate focus depth. As well as a novel trajectory snake energy function of robotic motion is defined involving kinematic energy, curve potential and image potential energy. The motion trajectory can be located through searching the converged energy distribution of the snake function. Energy weights in the function are real-time adjusted to avoid local minima during convergence. To improve snake searching efficiency, quadratic-trajectory least square estimator is employed to predict manipulator motion position before tracking. Experimental results in a microassembly robotic system demonstrate that the proposed strategies are successful and effective.

An energy-time optimal autonomous motion control framework for overhead cranes in the presence of obstacles

2020 ◽  
pp. 095440622095450
Author(s):  
Xinwei Wang ◽  
Jie Liu ◽  
Xianzhou Dong ◽  
Haijun Peng ◽  
Chongwei Li
Keyword(s):  
Motion Control ◽  
Trajectory Tracking ◽  
Overhead Cranes ◽  
Control Framework ◽  
Weighted Matrix ◽  
Autonomous Motion ◽  
Avoidance Condition ◽  
Time Optimal ◽  
Predictive Controller ◽  
Time And Energy

This paper focuses on the autonomous motion control of 3-D underactuated overhead cranes in the presence of obstacles, and an “offline motion planning + online trajectory tracking” framework is developed. In the motion planner, to meet the balance between transfer time and energy consumption, the transfer mission is formulated as an energy-time hybrid optimal control problem. And a simple and conservative collision-avoidance condition is derived. To achieve fast and robust calculations, an iterative procedure that determines optimal terminal time based on the secant method is developed. Finally, to realize the high-precision trajectory tracking and fast residual sway suppression, a model predictive controller with a piecewise weighted matrix is designed. Numerical simulation demonstrates that the discussed framework is effective.

Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators

2013 ◽  
Vol 25 (4) ◽  
pp. 737-747 ◽  
Author(s):  
Munadi ◽  
◽  
Tomohide Naniwa ◽  
Keyword(s):  
Adaptive Control ◽  
Motion Control ◽  
Trajectory Tracking ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Control Input ◽  
Dominant Type ◽  
Dynamical Parameters ◽  
Small Robot

This paper presents an experimental study to verify an adaptive dominant type hybrid adaptive and learning controller for acquiring an accurate trajectory tracking of periodic desired trajectory of robot manipulators. The proposed controller is developed based on combining the model-based adaptive control (MBAC), repetitive learning control (RLC) and proportionalderivative (PD) control in which the MBAC input becomes dominant than other inputs. Dominance of adaptive control input gives the advantage that the proposed controller could adjust the feed-forward motion control input immediately after changing the desired motion or load of the manipulator. In motion control law, the proposed controller uses only one vector to estimate the unknown dynamical parameters. It makes the proposed controller as a simpler hybrid adaptive and learning controller which does not need much computational power and also is easily be implemented for real applications of robot manipulators. The proposed controller is verified through experiments on a four-link small robot manipulator as representation of a scale robot manipulator to ensure this controller can be applied in the real applications of robot manipulators. The experimental results show the effectiveness of the proposed controller by indicating the position tracking error approaches to zero.

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