An Adaptive Electric Differential for Electric Vehicles Motion Stabilization

International audienceThis paper proposes a traction drive system for electric vehicles (EVs) with two separate induction motor drive-based wheels. In this case, an electric differential (ED) is developed. To handle EV stability while cornering or under slippery road condition, the proposed traction drive uses direct torque control and an adaptive-flux-and-speed-observer-based algorithm. EV-specific experimental tests on a digital signal processor TMS320LF2407 are carried out to show the effectiveness of the proposed adaptive ED in terms of robustness and stability

Intelligent Adaptive Motion Control for Ground Wheeled Vehicles

In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunov’s stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies

Optimization of a low weight electronic differential for LEVs

It is presented a performance analysis of an Electronic Differential (ED) system designed for Light Electric Vehicles (LEVs). We have developed a test tricycle vehicle with one front steering wheel and two rear fixed units is a same axis with a brushless DC integrated in each of them. Each motor has an independent controller unit and a common Arduino electronic CPU based that can plan specific speeds for each wheels as curves are being traced. Different implementations of sensors (input current/torque, steering angle and speed of the wheels) are discussed related to hardware complexity, and performance obtained based on speed level requirements and slipping on the traction wheels.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Analysis and stabilization of chaos in electric-vehicle steering system

published_or_final_versio

Active power steering system in electronic differential of electric vehicle with individual drive of two front wheels

Розроблено систему керування індивідуальними приводами передніх ведучих коліс електромобіля (ЕМ), яка забезпечує потрібне тягове зусилля, а також виконує функцію електронного диференціала. Остання полягає в заданні в поворотах ЕМ крутних моментів ведучих коліс відповідно до геометрії рульового керування Акермана. Крім того, відповідне активне керування моментами коліс забезпечує задане підсилення керма, а також демпфування пружних коливань рульового механізму. Робота системи ілюструється результатами комп’ютерного симулювання.In an electric vehicle (EV) with individual drives of the front driving wheels, an electronic differential can successfully perform the mechanical differential function. An electronic differential is a system for controllingthe torques of the driving wheels in the turns of the EV according to Ackermann's steering geometry. This ensures such a difference in the angular velocities of the outer and inner wheels in relation to the direction of turn, which minimizes their slip. The control system of individual drives of thefront driving wheels of EV is developed, which provides the required traction effort and performs the specified function of the electronic differential. In addition, the system carries out such additional active control of the wheels torques to provide a given steering wheel reinforcement in the steering column, as well as damping the elastic oscillations of the steering mechanism. The computer model of the conversion EV based on Audi A2 with an electronic differential and developed active steering system is created in the en-vironment of Matlab/Simulink. Electric drives of the front wheels are realized based on brushless DC motors. The results of computer simulation, which confirm the effectiveness of the proposed solutions, are presented

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft