Binggang Cao

Energy-Regenerative Fuzzy Sliding Mode Controller Design for Ultracapacitor-Battery Hybrid Power of Electric Vehicle

In order to deal with two major problems of electric vehicle (EV): the short driving range and the short life of batteries, a hybrid power system was designed and applied to the EV. It was composed of an ultracapacitor with high-specific power and long life, four lead-acid batteries with high-specific energy, and a bi-directional DC/DC converter. To improve the stability and reliability of the system, based on researching energy-regenerative process and fuzzy sliding mode controller (Fuzzy-SMC), the energy-regenerative mathematical model of the system was established, and the energy-regenerative Fuzzy-SMC for the system was designed. The experimental results show that the Fuzzy-SMC is superior to PID controller at response speed, steady-state tracking error and resisting perturbation. Additionally, comparing with the EV which uses batteries as its single power source, the ultracapacitor-battery hybrid power system can recover more energy, lengthen the life of batteries, and increase the driving range by 36.8% with PID controller, and by 42.1% with Fuzzy-SMC.

Position-sensing based a new docking system of RPRS

Docking is an essential function for reconfigurable planetary robot system (RPRS), it supports almost all metamorphic characteristics of the system. This paper presents a space docking method for self-reconfigurable modular robot system. The method adopts a position sensory detector (PSD) to get the space geometrical relation between modular child robots. By using the kinematics and inverse kinematics modeling of the child robot, a docking control algorithm is developed to achieve space dock automatically. To validate the method, simulation experiments were done on a platform based on OPENGL, and the simulating results were given as well.

Neural Network Self-adaptive PID Control for Driving and Regenerative Braking of Electric Vehicle

In order to deal with the main problems of electric vehicle (EV), such as the short driving range, the short life of batteries, the variation of the road state and driving mode and so on, based on constructing the main circuit diagram of the EVs control system and researching driving and regenerative braking process, the mathematical model of the system was established, driving and regenerative braking controller was designed for the EV. To improve the stability and reliability of the system, neural network (NN) self-adaptive PID control algorithm was researched and applied to the system. The controller comprises a back propagation (BP) NN and a radial basis function (RBF) NN. The former is used to adaptively adjust the parameters of the PID controller on line. The later is used to establish nonlinear prediction model and perform parameter prediction. The experimental results show that the NN self-adaptive PID controller is superior to traditional PID controller at response speed, steady-state tracking error and resisting perturbation. Additionally, it can recover more energy, lengthen batteries life, and increase the driving range than PID controller by 5.3%.

Development of an OpenGL based multi-robot simulating platform

Simulating platform plays a crucial role in multi-robot research, as a tool to quickly and efficiently test new concepts, strategies, and algorithms. In this paper, OpenGL and Visual C++ based modeling method has been discussed in detail to establish a platform for the 3D simulating of multi-link mobile robots and their collaboration. The cooperation strategies, the control methods and the communication mechanisms of multi-robot system can be explored and verified by this platform. A planetary robot system is used to verify the validity of the developed platform. The effectiveness and the practicability of the platform are successfully demonstrated by a four-robot motion coordination simulating results.

Neural network control of electric vehicle based on position-sensorless brushless DC motor

Based on analyzing the principle of position- sensorless control for brushless DC motor (BLDCM), a control system employing back-EMF method was designed for the position-sensorless electric vehicle (EV). In order to eliminate the influence on back-EMF detection circuit from motor neutral point and RC filter, the system disconnected the reference point of detection circuit from battery cathode, and did the phase- shifting compensation of back-EMF. To improve the stability and reliability of the system, neural network PID (NNPID) control algorithm was researched and applied to the system. The controller comprises a back propagation (BP) NN and a radial basis function (RBF) NN. The former is used to adaptively adjust the parameters of the PED controller on-line. The later is used to establish nonlinear prediction model and perform parameter prediction. The experimental results show that the control system of position-sensorless EV can overcome the disturbance of phase shifting, successfully achieve position-sensorless commutation control and replace Hall sensors. In addition, when using NNPID controller, the control system is superior to that using traditional PID controller at response speed, steady-state tracking error and resisting perturbation in the driving process.

Communication Mechanism Study of a Multi-Robot Planetary Exploration System

Communication mechanism is a key problem for multi-robot research. The communication characteristics of a reconfigurable planetary multi-robot system are analyzed. According to the function requirement of the multi-robot system, enlightened by the principle of TDMA (Time Division Multiple Address) and rotational mechanism, a novel multi-robot wireless communication mechanism is proposed. The mechanism and the communication protocol are also described in detail. Simulation and experimentation both prove the feasibility and practicability of the proposed mechanism.

Improved EKF for SOC of the storage battery

Aiming at the electric automobile in the running state of the complicated working condition, an innovative battery SOC estimation method is presented. Based on a new type of on-line measurement in storage battery parameters, improved EKF algorithm is used to estimate the remaining battery capacity. By isolating single cells and acquainting parameters, the unit cells SOC is estimated through the Kalman algorithm, and we can calculate assembled battery SOC by integrating unit cells SOC. This algorithm overcomes the changes of electric vehicle battery parameters which are complicated and the traditional estimation algorithm has defects of low accuracy of SOC. The technology put forward in this paper overcomes the flaw. And the internal resistance of the battery can be estimated. The research has an important significance on SOH. Analysis of the test shows that, using this method for on-line estimation of battery SOC, the estimation accuracy is relatively high can reflect the real residual capacity of battery better.

BP networks based trajectory planning and inverse kinematics of a reconfigurable mars rover

The inverse kinematics of series manipulators presents an inherent complexity due to their various structures and kinematics constraints. To a novel reconfigurable Mars rovers arm, the inverse kinematics had been solved by numerical method combined with space geometry relations, but the complex calculating process can not be utilized in real-time control. In this paper, some actions in common use are programmed in the child rover, and the BP neural network was used to solve the inverse kinematics and trajectory planning. To a desired trajectory, some solutions by direct kinematics algorithms and geometry relations corresponding to the trajectory were used as the BP networks training data set, and the non-linear mapping from joint-variable space to operation-variable space was obtained with iterative training and learning method. The results show the output of training is consistent with the desired path, and the obtained reference trajectory is smooth.

Real-time time-optimal control of manipulators based on gravity center motion

Fast motion is a central problem in high-performance robotics. Finding the minimum time control strategies for point to point operation of a robotic manipulator is algorithmically difficult and computationally very intensive. As a result, the practical applicability of currently available methods is very limited. In this study, we present an approximate solution to the time-optimal trajectory planning problem, from a point of view of the gravity center motion. While not producing the exact minimum-time solution, the algorithm approximated the minimum-time solution can be easily utilized online. The algorithm running on a typical PC computer only takes about 1.03 seconds, while the algorithm for the exact time optimal solution takes about 2.54 seconds. It is thus possible for real-time control.