The introduction of Electric Power Assisted Steering (EPAS) system is gradually replacing the conventional steering system in modern cars. The main advantage of EPAS system is the ability to minimise energy consumption. This paper discusses the implementation of GA-PID algorithm to realise the potential of energy reduction as compared to conventional PID method. A brushes DC motor is mounted on the steering column to provide an assist torque to the driver. This configuration is known as C-type EPAS system. The results have shown GA-PID controller is bale to minimize energy consumption as compared to PID controller.

Optimal design of electric power assisted steering system(EPAS) using GA-PID method

Purpose – This paper aims to present a linear quadratic regulator (LQR) employed to improve performance of an electrical power steering (EPS) system.Design/methodology/approach – Generally, EPS is a full electric system having an electrical motor which provides the assist torque on the steering mechanism in order to reduce the workload and to enhance the steering feel of the driver during the steering process. Since the torque sensors are considerably expensive, the authors present a control strategy that eliminates the driver torque sensor by introducing a torque estimator. Three main technical areas are described in this paper. First, the principle and structure of EPS are presented including the dynamic model. Second, LQR and Kalman filter techniques are employed to derive an optimal controller for the EPS system. Finally, the simulations and hardware results are depicted.Findings – The combined tools of Matlab/Simulink and dSPACE provide the environment for modelling the controller in software and app...

Controller design for an electric power steering system based on LQR techniques

Sensing the friction coefficient between the tyres of an automobile and the road surface is a key challenge in automotive control. Accurate estimation of this parameter enables higher perfo...

Rapid Road Friction Estimation using Independent Left/Right Steering Torque Measurements

Driver torque and steering reaction force are important information in the designing of EPAS (Electric Power Assisted Steering) system. The driver torque determines the amount of assisted torque and the reaction force can be used to improve the steering feeling and the vehicle stability or introduce a new assist torque control. Since, in practice, they are not usually measureable; a sliding mode observer with unknown inputs is introduced to estimate the driver torque and the road reaction force of an EPAS system by using other measured signals which are normally available. A state space formulation for EPAS system with brushed motor is used for the synthesis of observer with unknown inputs. The proposed observer is applied to state and unknown input estimation of EPAS. Simulation results demonstrate the effectiveness of the proposed observer which improves the EPAS system performance and also reduces system cost and complexity.

Driver torque and road reaction force estimation of an Electric Power Assisted Steering using sliding mode observer with unknown inputs

Hybrid powertrain provides driving torque based upon the vehicle acceleration pedal position. Due to the slow response of the internal combustion (IC) engine, the electric motor(s) (EM) often has to provide additional torque during transient vehicle operations. During cold-start operations, the available battery power is significantly reduced due to cold temperatures, and due to the starting delay of the IC engine, the battery has to provide power at the level beyond its normal operation limit for the electrical motor to meet the torque demand, which would dramatically shorten the battery life. However, if the desired powertrain torque can be predicted, the IC engine could be started early when the predicted future powertrain power is higher than the battery normal operational range, and hence, the duration of battery operated over its normal operational range could be significantly reduced, leading to extended battery life. This makes predicting multistep future torque demand, based upon current and past torque demand data, an important subject. Amid many existing studies, few research studies were focused on this subject. This paper proposes an adaptive recursive prediction algorithm to estimate the future desired powertrain torque and compares the performance of the proposed algorithm with two existing prediction algorithms, i.e., step-by-step and fixed-gain algorithms. These three prediction algorithms are studied under the federal test procedure (FTP) and another four typical driving cycles. The simulation results show that the adaptive recursive prediction algorithm reduces the prediction error significantly with a 4% maximum prediction error, and in addition, the reduced computational load makes it feasible for real-time implementation. The introduced weighting factors for the past and current data are the key to improve prediction accuracy and avoid prediction calculation over- and underflow. In addition, due to the online update of regression gain, the proposed algorithm is also robust to driving behavior variations.

Adaptive Recursive Prediction of the Desired Torque of a Hybrid Powertrain

Currently, all Electric Power Assist Steering (EPAS) systems employ a pinion torque sensor, between the steering column and pinion, to determine the amount of the torque assist to the driver. This torque assist is calculated via a tunable nonlinear boost curve. Then, this signal is used as control command to the electric motor to achieve the desirable level of assist. In this paper, torque estimators are devised to generate accurate estimates of the driver torque input and eliminate the need of the pinion torque sensor. Together with measured signals such as steering wheel rotational position, these estimates provide signals for a robust controller that consists of a feedforward part vs. the boost curve and an H∞ optimal feedback to ensure refined performance, robustness, and disturbance attenuation. Introduction of estimators results in additional feedback loops and ushers in new issues of robust stability and performance. The concept of structural stability is introduced to capture the requirements of robust stability under various system configurations due to component failures. The requirements will guarantee a level of fault tolerance in case of control failure during its operation. Conditions are provided under which such structural stability is maintained.

Torque estimation in electrical power steering systems

In this paper, an optimal control method is employed to design controllers for improved performance and robustness of electrical steering systems. Electric Steering systems are nonlinear MIMO systems with multiple objectives, including fast response to the driver torque command, good driver feel, and attenuation of load disturbance and sensor noises. Since the Electric steering system has nonlinear frictions, component deviations and load disturbances, its linearized model is subject to significant modeling errors and external disturbances. Consequently, electric steering controllers must provide substantial robustness. In this paper, a control design method is introduced which employs the boost curve to form a feedforward control and an MISO H∞ optimal feedback to ensure refined performance, robustness, and disturbance attenuation. The controller is evaluated via simulation on both linearized systems and original nonlinear systems, and verified on vehicle testing data.

Optimal control and gain scheduling of electrical power steering systems

This paper is focused on the application of a feedforward algorithm applied in a variable speed drive to improve closed loop airspeed response in climatic wind tunnels. This use is uncommon in that a calculated feedforward value from the supervisory controller is inserted between the speed controller and current controller in the variable speed drive. This application was piloted in a flywheel application using a DC drive and motor, which proved that the method could be used. A Simulink model was developed to duplicate the results for the flywheel. The model was then leveraged to demonstrate that this application can be used for wind tunnel airspeed.

Feedforward algorithm: Wind tunnel airspeed control

Feedforward Algorithm: Wind Tunnel Airspeed Control Application of Feedforward in a Variable Speed Drive

Modern electric cars require electrical power steering systems (EPAS). Many control algorithms where employed in this field. Some of these controllers exhibit robustness and stability problems for certain road conditions. Neural networks are known for their ability to imitate systems and stay stable if operation conditions change. In this paper we use neural controllers to imitate the Hoo controller we have already designed to control the EAPS system. We collect the Hoo performance signals and use them as training data for the suggested neural controllers. Fuzzy Adaptive Resonance Theory (fuzzy ARTMAP) and back propagation neural controllers are used in this paper to do the control act. The performance of each controller is recorded for comparison purposes.

Rakan Chabaan

Papers

Torque estimation in electrical power steering systems

Optimal control and gain scheduling of electrical power steering systems

Feedforward algorithm: Wind tunnel airspeed control

Feedforward Algorithm: Wind Tunnel Airspeed Control Application of Feedforward in a Variable Speed Drive

Neural controllers for electrical power steering systems