원문정보
초록
영어
In this paper, we take two-wheeled self-balancing robot as the experimental object, designing an advanced controller which not only solves the defect of the over-reliance on precise mathematical model for modern control theory, but also solves the problem of complex handling issues of the fuzzy controller under multi input and multi output situation. The designing idea is: firstly, a fusion function is gained through the feedback matrix by LQR method; the fusion function is adopted to reduce the input dimensions of the fuzzy controller, thus to control the system. Secondly, due to the quantization factor and proportional factor of fuzzy control are mainly obtained by experience, we use particle swarm algorithm to optimize the quantization factor and scaling factor of fuzzy controller in order to get an ideal control effect. Thirdly, constructing two fusion functions to fuse the six dimensional input variables from the robot, which solve the existence error between the designed decoupler from theoretical calculation and the actual situation of the system, also solve the difficulties through decouper designing. Finally, proving the validity of the control strategy through the simulation and real-time control, it shares a good theoretical and practical significance.
목차
1. Introduction
2. System Description and Modeling of the System
2.1 Mathematical Model of the System
2.2 The Dynamic Model Based On Newtonian Mechanics
3. The Design of LQR-Fuzzy Controller
3.1 The Design of Controller
3.2 Robot Real-Time Control Test
4. Particle Swarm Optimization LQR-Fuzzy Controller
4.1 The Selection of Optimal Objective Function
4.2 The Realization of LQR-Fuzzy Control Parameter Optimization
5. Dual Fusion of LQR-Fuzzy Controller Designing
5.1 The Construction of Fusion Function
5.2 The Confirmation of Fuzzy Rules
5.3 Robot Real-Time Control Test
6. Conclusion
References