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Dianwei Qian,Shiwen Tong,Chengdong Li 한국전자통신연구원 2016 ETRI Journal Vol.38 No.5
This paper presents a control scheme for the leader-following formation of multiple robots. The control scheme combines the sliding mode control (SMC) method with the nonlinear disturbance observer (NDOB) technique. The formation dynamics suffer from uncertainties because the individual robots are uncertain. Concerning such formation uncertainties, the leader-following formation dynamics are modeled. Assuming that the formation uncertainties have an unknown boundary, an NDOB-based observer was designed to estimate the formation uncertainties. A sliding surface containing the observer outputs has been defined. Regarding the sliding surface, an SMC-based controller was investigated to form uncertain robots. A sufficient condition in the sense of the Lyapunov theory was proven such that the formation system is asymptotically stable. Herein, some comparison results between the sole SMC method and the second-order SMC method are presented to demonstrate the effectiveness and feasibility of the control scheme for multiple robots in the presence of uncertainties.
Comments on “Sliding-Mode Formation Control for Cooperative Autonomous Mobile Robots”
Dianwei Qian,Shiwen Tong,Jinrong Guo,Suk Gyu Lee Institute of Electrical and Electronics Engineers 2015 IEEE transactions on industrial electronics Vol. No.
<P>One of the formation schemes in multirobot systems is named as the leader-follower scheme. Dynamic equations based on the scheme are formulated for multirobot systems with uncertainties by Defoort et al. ( IEEE Trans. Ind. Electron. vol. 55, no. 11, pp. 3944-3953, Nov. 2008). This paper focuses on the equations, clarifies their drawbacks, and revisits the modeling of the leader-follower formation scheme for multirobot systems with uncertainties.</P>
Adaptive Tracking Design of NCS with Time-varying Signals Using Fuzzy Inverse Model
Shiwen Tong,Dianwei Qian,Na Huang,Guo-ping Liu,Jiancheng Zhang,Guang Cheng 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.11
Tracking control of time-varying signal is a very challenging problem for the network environment applications. An adaptive control strategy based on the inverse of fuzzy singleton model is proposed in the paper. The fuzzy singleton model is a designed equivalent system instead of the fuzzy clustering model of the controlled process. Following an invertibility condition, a collection of predicted control actions are derived from the iterated inverse fuzzy singleton model. Thus, the data dropout and time delays in the network are compensated by means of these predicted values. To enhance control performance, the adaptive control strategy is adopted. Since the method is started from the inputs and outputs of the process, it is actually a data-based solution which is very suitable to the processes with blurred mechanism. Compared with other two control algorithms, the proposed control algorithm exhibits good accuracy, high efficiency, and fast tracking features. Simulations in the data dropout and time-delay cases have verified the effectiveness of the method.
Multi-Robot Avoidance Control Based on Omni-Directional Visual SLAM with a Fisheye Lens Camera
최윤원,최정원,임성규,Dianwei Qian,이석규 한국정밀공학회 2018 International Journal of Precision Engineering and Vol.19 No.10
This paper proposes a noble avoidance control algorithm based on omni-directional visual simultaneous localization and mapping (OVSLAM) with a fisheye lens camera. In addition, a robot avoids colliding with an obstacle regardless of the obstacle's state by analyzing the information of the object obtained from an OVSLAM approach. OVSLAM has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. We therefore proposed an improved avoidance and formation control to configure a multi-robot system optimized for OVSLAM. This system creates a global map based on vector information and position information of objects obtained from a local map, and determines the avoidance method according to the type of object, which is classified by analyzing the odometry and vector and position information. We carried out a formation control experiment in an environment with static obstacles and a dynamic robot, and a formation control experiment in an environment with dynamic obstacles and a robot. The reliability of the proposed formation algorithm was verified through a comparison of maps based on the proposed algorithm and real maps while maintaining the formation by applying a real robot.