Sun Fuchun

Disturbance Observer-Based Robust Control of Free-Floating Space Manipulators

A disturbance observer-based control scheme is proposed for free-floating space manipulator with nonlinear dynamics derived using the virtual manipulator approach. The derived dynamic equation uses only link angles as generalized coordinates, which is suitable for the controller design in joint space. Since joint coupling, model uncertainties in robot dynamics are treated as lumped disturbances, a disturbance observer is developed at each joint of degree-of-freedom space manipulator to decouple and simplify the controller design. Simulation results of a six-link space manipulator show that the proposed scheme achieves superior performance, especially when large external disturbances are present.A disturbance observer-based control scheme is proposed for free-floating space manipulator with nonlinear dynamics derived using the virtual manipulator approach. The derived dynamic equation uses only link angles as generalized coordinates, which is suitable for the controller design in joint space. Since joint coupling, model uncertainties in robot dynamics are treated as lumped disturbances, a disturbance observer is developed at each joint of degree-of-freedom space manipulator to decouple and simplify the controller design. Simulation results of a six-link space manipulator show that the proposed scheme achieves superior performance, especially when large external disturbances are present.

Observer based adaptive controller design of flexible manipulators using time-delay neuro-fuzzy networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.A dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For perfect tracking control of the robot, an output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with rule representation of a Takagi-Sugeno-Kang type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.

An Overview on Coordination Control Problem of Multi-agent System

This paper presents an overview on the state-of-the-art of the coordination control problem of multi-agent systems.The fundamental problems in the coordination control field are introduced,and the research hotspots and frontiers are analyzed and summarized.Furthermore,some of the latest research achievements on coordination control of networked Euler-Lagrange systems are also presented.Finally,the problems in this area and the prospect of future research are summarized.

Dynamic modeling, control and simulation of flexible dual-arm space robot

Flexible Multi-Arm Space Robot is a highly nonlinear and coupled dynamic system. Using the assumed mode method to describe the elastic deformation, the dynamic model of flexible dual-arm space robot is build by the Lagrange approach. The inversion dynamic control method is performed to solve the tracking problem. Then a serial of simulations of tracking control of two kinds of typical trajectory have done. Good tracking control results obtained at the simulations of the flexible dual-arm space robot.

Analysis and synthesis of fuzzy stochastic systems via LMI approach

This paper studied a general class of nonlinear stochastic systems, which can be approximated by T-S fuzzy model. The problem both the control and the state entering into the diffusion term is discussed here. A PDC state feedback controller is proposed to guarantee the mean-square stability of the closed-loop system. All the results are represented by the linear matrix inequalities.

Stable adaptive control for robot trajectory tracking using neural networks

Existing stable adaptive control approaches using neural networks have been developed mostly in continuous time systems for robot trajectory tracking. This paper investigates the discrete time case. A novel scheme for integrating a neural network (NN) approach with an adaptive implementation of the sliding mode control with the sector is developed. The sliding mode control with the sector serves two purposes, one is to provide the global stability of the closed loop system, the other is to improve the tracking performance. The whole system stability and tracking error convergence are proved by Lyapunov techniques which yield a NN weight tuning algorithm.

Design and development of a ground experiment system with free-flying space robot

Ground experiment system with free-flying space robot (FFSR) is essential to support space tasks during tests on Earth. In this paper, a ground experiment system combining air-bearing table and hardware-in-loop simulation is presented. Elementary experiments based on physical and semi-physical simulation platform are reported.

A Method for Rapid Transfer Alignment Based On UKF

Using quaternions is an effective approach for solving stiff numerical problem in the transfer initial alignment using Eulerian angle attitude algorithm when the missile is in vertical state. However, due to the strong nonlinearity, the usual Kalman filter can not be applied. Therefore, we use UKF to implement the estimation in nonlinear model. Based on UT transformation and adopting Kalman filters frame, UKF makes sampling strategy approach nonlinear distribution and the specific sampling form is determinate sampling. The number of sampled particles (usually named Sigma particles) of UKF is very small, which is determined by the selected sampling strategy. The most typical strategy chooses 2n + 1 (n is the extended state dimension) symmetrical Sigma particles. The amount of calculation of UKF is approximately equal to that of EKF, but, its performance is better than EKFs performance, and the determinate sampling makes UKF avoid PFs particle degeneration. This method based on UKF can implement initial alignment when the missile is in any attitude state. This paper presents the nonlinear model of initial alignment and uses UKF to implement the estimation. The simulation shows, this method can obtain high precision in short time

Deep vision networks for real-time robotic grasp detection:

Grasping has always been a great challenge for robots due to its lack of the ability to well understand the perceived sensing data. In this work, we propose an end-to-end deep vision network model to predict possible good grasps from real-world images in real time. In order to accelerate the speed of the grasp detection, reference rectangles are designed to suggest potential grasp locations and then refined to indicate robotic grasps in the image. With the proposed model, the graspable scores for each location in the image and the corresponding predicted grasp rectangles can be obtained in real time at a rate of 80 frames per second on a graphic processing unit. The model is evaluated on a real robot-collected data set and different reference rectangle settings are compared to yield the best detection performance. The experimental results demonstrate that the proposed approach can assist the robot to learn the graspable part of the object from the image in a fast manner.

Getting a Suitable Terminal Cost and Maximizing the Terminal Region for MPC

The model predictive control (MPC) subject to control and state constraint is studied. Given a terminal cost, a terminal region is obtained through iterative estimation by using support vector machine (SVM). It is proved that the obtained terminal region is the largest terminal region when the terminal cost is given. The relationships between terminal cost and terminal region and between terminal cost and total cost are discussed, respectively. Based on these relationships, a simple method to get a suitable terminal cost is proposed and it can be adjusted according to our need. Finally, some experiment results are presented.