Single-Wheeled Self-Balancing Robot Modelling And Control

Unicycle riding is a kind of senior motor skills of human beings or other intelligent animals after training. Single-wheeled Self-Balancing Robot (SWSBR, SWR) is a kind of intelligent mimetic systems imitating human behavior of riding an unicycle.Different from other mobile robot with multiple wheels, the unicycle riding robot system or SWSBR has only one wheel to touch the ground. And it is a statically instable, coupled and highly nonlinear plant in three dimensions. Modeling and control of flexible self-balancing robot are important issues in the fields of control science and robotic engineering. This dissertation studies and designs a Single-Wheeled Self-Balancing Robot (SWSBR), develops the kinematics model and dynamic model of the robot, and on the basis of system characters analyzing, the research on the robot's motion and balancing control is carried out. The main contributions are as follows:(1) Single-Wheeled Self-Balancing Robot SystemThis dissertation illustrates the design of a single-wheeled self-balancing robot system (SWSBR System), whose the most important structure character imitates the overall structure of the unicycle: the vertical flywheel to tune the roll DOF and the walk wheel to adjust the pitch DOF, with robot frame for all modules fixed on. There is only one walk wheel contacting with the ground. Its dynamic feature is complex, and its modeling and control are difficult. The electronic system of the robot is hierarchical architecture: the organization layer has an embedded PC as the center, supplemented by a variety of human-machine interface. It is responsible for monitoring, acquiring information, decision-making and movement control instructions issued; the coordination layer has a DSP as the core, supplemented by state sensors, is mainly responsible for balance and motion control; the execution layer is the motor servo system, and responsible for controlling the torques of wheels. The control system structure of the studied robot constitutes a bionic sensory-motor system.(2) Dynamic Modeling and Analysis of SWSBRIn this dissertation, the dynamic model of SWSBR is derived by applying the Lagrange method. Based on the proposed model, firstly, the dynamic characters of SWSBR is analyzed, zero input response and zero states response simulation are carried out, the outcome is in compliance with the physical fact, which examines the validity of the model. Secondly, the system characters of SWSBR is analyzed, it is proved that SWSBR is not stable and locally controllable on its upright equilibrium point. Thirdly, the analysis of the SWSBR design parameters is carried out: quality, center of gravity, inertia flywheel and so on. Finally, the three-dimensional model of SWSBR consistent with the dynamic model and physical fact is built by virtual prototyping technology. The model and the analysis described in this dissertation provide some theoretical basis for the modeling and control of SWSBR.(3)Motion Balancing Control for SWSBR Based on Nonlinear Control MethodThis dissertation proposes a three-loop control method based on nonlinear PD for the posture balancing and motion control of SWSBR. The nonlinear PD three closed-loop control method includes driven inner loop, posture balancing control mid loop and motion control outer loop. The input of the motor controllers is the output torques of posture balancing controller. The input of posture balancing controller is the output of motion controller. In the posture control mid loop, the stable equilibrium of SWSBR is controlled by 2 nonlinear PD controllers, in which tan (θ) is the nonlinear aspect ratio, used as the nonlinear differential link. The inputs of motion controller are desired position. The simulation and real system experiments in static balancing control and motion control are carried out. The results prove that the control method proposed by this dissertation is effective for single-wheeled self-balancing robot. The simulation results are compared with linear PD or LQR controller. They show that with the same parameters, PDNLB dynamic performance and robustness are much better than linear PD and LQR controllers.(4) Dynamic Inversion Control Method for SWSBRFor nonlinear control problems of single-wheeled self-balancing robot, inverse system method is proposed and pseudo-dynamic inversion controller is designed. Single-wheeled self-balancing robot system is a minimum phase system, inverse system does not exist. To constitute single-wheeled self-balancing robot dynamic pseudo-inverse control system, the time scale separation method is used in building the system pseudo-inverse. But this method has such problems as: accurate model for dynamic inverse system is difficult to obtain, construction of dynamic inversion control system is inconvenient, performance of pseudo-dynamic inversion control is difficult to maintain. And the neural network has capability of the approximate any continuous input-output mapping. So the dynamic inversion control based on neural network is proposed. The method uses BP neural network to approach the dynamic inverse model, constitutes a reference model of neural network dynamic inversion control system. The results show that the method can take advantage of BP neural network to approximate the inverse system, effectively solve problems that the exact analytical model is difficult to obtain, and achieve posture control; but the overshoot, settling time and other control performance are not satisfactory, and the robustness is poor. (6) Iterative Learning Control Method for SWSBRIn this dissertation, an iterative learning control method of neural network backstepping control method for SWSBR's posture and position control is proposed. The iterative learning control is introduced based on the energy function of tracking error to overcome the uncertainty caused by the robot system parameters and disturbances. The neural network backstepping control method is the tool for iterative learning. It is based on the linear control and approximation of ideal control law by the neural network. The iterative learning control method not only has a simple and clear form, but also can compensate uncertainty, nonlinearity, coupling, modeling error and other factors through the online learning process. Simulation results show that the iterative learning control of neural network backstepping method for SWSBR's posture and position control are effective within a certain range and have better performance than linear control. Comparative results of experiments show that with the same parameters, the performance of iterative learning control of neural network backstepping method, adaptive neural network dynamic inversion control are the best; nonlinear PD three closed-loop control and dynamic inversion control are the second; linear control method, neural network dynamic inversion control and dynamic inversion control are the worst.This subject is supported by the National Natural Foundation (60774077) and the High Technology Development Plan (863) (2007AA04Z226). The research results have significance for optimizing the system structure of flexible robots, the analysis of the Single-Wheeled Self-Balancing Robot's motion pattern and identity, and the study of motion and balancing control problem. Several patents have been granted to the proposed robot, which has application value in the fields of research and education for robotic technology and control science, and service/entertainment robot development.