The South China University of Technology

Zhijun Zhang

Lu'nan Zheng

Yuanqing Li

Qi Guo

Yongqian Huang

Ziyi Yan

Dongyu Ji

Disclosed is a method for controlling stable flight of an unmanned aircraft, comprising the following steps: acquiring real-time flight operation data of the aircraft itself by means of an attitude sensor, a position sensor and an altitude sensor mounted to the unmanned aircraft, performing corresponding analysis on a kinematic problem of the aircraft by a processor mounted thereto, and establishing a dynamics model of the aircraft (S1); designing a controller of the unmanned aircraft according to a multi-layer zeroing neurodynamic method (S2); solving output control quantities of motors of the aircraft by the designed multi-layer zeroing neural network controller using the acquired real-time operation data of the aircraft and target attitude data (S3); and transferring solution results to a motor governor of the aircraft, and controlling powers of the motors according to a relationship between the control quantities solved by the controller and the powers of the motors of the multi-rotor unmanned aircraft, so as to control the motion of the unmanned aircraft (S4). Based on the multi-layer zeroing neurodynamic method, a correct solution to the problem can be approached rapidly, accurately and in real time, and a time-varying problem can be significantly solved.

Method for controlling steady flight of unmanned aircraft

Disclosed are a brain-computer interface based robotic arm self-assisting system and method. The system comprises a sensing layer, a decision-making layer and an execution layer. The sensing layer comprises an electroencephalogram acquisition and detection module and a visual identification and positioning module and is used for analyzing and identifying the intent of a user and identifying and locating positions of a corresponding cup and the user's mouth based on the user intent. The execution layer comprises a robotic arm control module that performs trajectory planning and control for a robotic arm based on an execution instruction received from a decision-making module. The decision-making layer comprises the decision-making module that is connected to the electroencephalogram acquisition and detection module, the visual identification and positioning module and the robotic arm control module to implement the acquisition and transmission of data of an electroencephalogram signal, a located position and a robotic arm status and the sending of the execution instruction for the robotic arm. The system combines the visual identification and positioning technology, a brain-computer interface and a robotic arm to facilitate paralyzed patients to drink water by themselves, improving the quality of life of the paralyzed patients.

Brain-computer interface based robotic arm self-assisting system and method

A method is presented for programming a repeating motion of a redundant robotic arm on the basis of a variable parameter convergence differential neural network. The method may include establishing an inverse kinematics equation, creating an inverse kinematics problem, introducing a repeating motion indicator, converting a time-varying convex quadratic programming problem into a time-varying matrix equation, and integrating an optimal solution to obtain an optimal solution of a joint angle. The use of the variable parameter convergence differential neural network to solve the repeating redundant mechanical motion has the advantages of high computational efficiency, high real-time performance, and enhanced robot arm robustness.

Method for programming repeating motion of redundant robotic arm

Provided is a stable flight control method for a multi-rotor unmanned aerial vehicle based on finite-time neurodynamics, comprising the following implementation process: 1) acquiring real-time flight orientation and attitude data through airborne sensors, and analyzing and processing kinematic problems of the aerial vehicle through an airborne processor to establish a dynamics model of the aerial vehicle; 2) designing a finite-time varying-parameter convergence differential neural network solver according to a finite-time varying-parameter convergence differential neurodynamics design method; 3) solving output control parameters of motors of the aerial vehicle through the finite-time varying-parameter convergence differential neural network solver using the acquired real-time orientation and attitude data; and 4) transmitting results to speed regulators of the motors of the aerial vehicle to control the motion of the unmanned aerial vehicle. Based on the finite-time varying-parameter convergence differential neurodynamics method, the invention can approximate the correct solution of the problem in a quick, accurate and real-time way, and can well solve a variety of time-varying problems such as matrix, vector, algebra and optimization.

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Guangzhou, China

Zhijun Zhang