Robot Driver Research Articles

Multibody dynamics modeling of electromagnetic direct-drive vehicle robot driver

Collaborative dynamics modeling of flexible multibody and rigid multibody for an electromagnetic direct-drive vehicle robot driver is proposed in the article. First, spatial dynamic equations of the direct-drive vehicle robot driver are obtained based on multibody system dynamics. Then, the shift manipulator dynamics model and the mechanical leg dynamics model are established on the basis of the multibody dynamics equations. After establishing a rigid multibody dynamics model and conducting finite element mesh and finite element discrete processing, a flexible multibody dynamics modeling of the electromagnetic direct-drive vehicle robot driver is established. The comparison of the simulation results between rigid and flexible multibody is performed. Simulation and experimental results show the effectiveness of the presented model of the electromagnetic direct-drive vehicle robot driver.

Fuzzy-neural-network-based speed control method and experiment verification for electromagnetic direct drive robot driver

Vehicle test has a great significance for the development of new vehicle products. It is necessary for a new type of vehicle stereotypes to conduct a lot of vehicle test. Some vehicle test, such as emission durability test, vehicle performance test, vehicle noise test, high and low temperature environment test, vehicle road test, vehicle bench test, is more suitable for the operation by robot. Robot driver is an intelligent robot that can realize automatic driving under harsh environment in vehicle test instead of a human driver without any modification. Because of the vehicle is not required to be modified, and the vehicle robot driver can be directly installed in the different vehicle cab. The drive way of the vehicle robot driver includes the hydraulic drive, the pneumatic drive and the servo electric drive. The hydraulic drive is steady, but it needs an oil cylinder. It is not easy for the pneumatic drive to accurate positioning and the real-time property. As a driving device of the robot driver, the servo electric drive needs a mechanism of rotary motion into linear motion. Electromagnetic linear motor can solve the shortcomings of three other drive styles. It can improve the transmission efficiency and transmission accuracy, and make the transmission mechanism simple. A control approach of speed in an electromagnetic direct drive robot driver based on fuzzy neural network is proposed in this paper, in order to realize the accurate speed tracking of different driving test cycle conditions. The electromagnetic direct drive robot driver adopts an electromagnetic linear actuator as the driving device in this paper. The throttle mechanical leg, the brake mechanical leg, the clutch mechanical leg and the shift manipulator are directly driven through the electromagnetic linear actuator. The control system structure and the coordinated motion control model of the electromagnetic direct drive robot driver are given. On the basis of this, the speed control model based on fuzzy neural network of the electromagnetic direct drive robot driver is designed. The shift manipulator displacement, the throttle mechanical leg displacement, the clutch mechanical leg displacement and the brake mechanical leg are the input variables of the fuzzy neural network model, and the vehicle speed of the test vehicle is the output variable of the fuzzy neural network model. The number of the input variable membership functions is three, and the type of the input variable membership functions is gbellmf. The fuzzy neural network training algorithm adapts the hybrid learning algorithm combing with back propagation algorithm and least square method. The proposed control method of the electromagnetic direct drive robot driver is experimentally proved and compared with other control methods and with human driver performances. Actual vehicle test results and error comparison analysis show that the vehicle speed tracking accuracy of robot driver using the proposed approach is higher than that of robot driver using PID control method and human driver. Besides, the proposed method has good adaptability under all kinds of driving test cycle, which can ensure the accuracy and effectiveness of vehicle test.

Dynamic modeling and simulation analysis of robot driver's mechanical legs

Dynamic modeling and simulation analysis of robot driver's mechanical legs

ロボットドライバーのための力制御系と制御特性

ロボットドライバーのための力制御系と制御特性

Closed loop control of a robot assisted smart flexible needle for percutaneous intervention.

This paper presents the experimental evaluation of a coordinated control system for a robot and robot-driven shape memory alloy (SMA) actuated smart flexible needle capable of following a curved path for percutaneous intervention. The robot driving the needle is considered the outer loop and the non-linear SMA actuated flexible needle system comprises the inner loop. The two feedback control loops are coordinated in such a way that the robot drives the needle while monitoring the needle's actual deflection against a preplanned ideal trajectory, so that the needle tip reaches the target location within an acceptable accuracy. In air and in water experimental results are presented to validate the ability of the proposed coordinated controller to track the overall desired trajectory which includes the combined trajectory of the robot driver and the needle.

Vehicle Robot Driver Research and Development Based on Servo Motor Control

Vehicle robot drivers are widely used in automotive tests especial for some tests on automotive chassis dynamometer. However, China has less technology accumulations than west developed countries in the field of research and development of robot driver. In order to promote the developments of the domestic robot driver technology, a vehicle robot driver based on servo motor control was developed for automobile chassis dynamometer test, its system composition, functional features, and key technologies in developing process were expounded specifically in this paper.

Speed Tracking Control of a Vehicle Robot Driver System Using Multiple Sliding Surface Control Schemes

To overcome the drawbacks of using a traditional proportional-integral-derivative (PID) control method for a robot driver system, such as requiring preliminary offline learning, big overshoot and large speed fluctuation, a new method for speed tracking of a robot driver system based on sliding mode control is proposed in this paper. Firstly, the coordinated control model of multiple manipulators for the robot driver is built, which achieved coordinated control of the throttle mechanical leg, clutch mechanical leg, brake mechanical leg and shift mechanical arm for the robot driver. On the basis of this, a speed tracking sliding mode controller for a vehicle robot driver is designed using the method of multiple sliding surfaces design, and the variable structure control laws of throttle and brake are designed respectively, which realize the speed tracking of the given driving test cycle. Experimental results demonstrate that compared with the PID control method, the proposed method can obviously reduce the overshoot of vehicle speed tracking control and greatly improve the accuracy of vehicle speed tracking. The vehicle speed tracking accuracy stays within a tolerance band of ±2 km/h, which meets the requirements of national vehicle test standards. Furthermore, the action of the speed tracking control in the same driving test cycle using the proposed method is consistent, so that the robot driver has good repeatability. Therefore, it can ensure the effectiveness of the vehicle emission test.

Dynamic performance simulation of a continuously variable transmission motorcycle for fuzzy autopilot design

This paper describes the modelling technique of a continuously variable transmission (CVT) motorcycle for control strategy design of an autopilot on a driving cycle test bench. The simulation is carried out on a code which is the combination of an engine cycle simulation synthesis, a dynamic CVT driveline algorithm, a chassis dynamometer model and a fuzzy autopilot controller. The autopilot, which is a robot driver, includes a throttle actuator and a brake handle controller. Fundamental fuzzy theories were employed to simulate the control strategy of an experienced driver, also to form a feedback control loop for the simulation. The whole simulation package is capable of predicting the motorcycle performance under dynamic road conditions, such as ECE R40 and USA FTP75 driving patterns. Simulation results of a baseline motorcycle, equipped with a 125 cm3 four-stroke engine plus a rubber belt CVT system, are demonstrated in this paper. Parametric studies are used to design the control strategy of the autopilot as well as to improve the design of the powertrain system of the motorcycle.