GA-tuning of nonlinear observers for sensorless control of automotive power steering IPMSMs

The paper considers two observer-based rotor position estimation schemes for sensorless control of interior permanent magnet synchronous motors (IPMSMs) for use in future automotive power steering systems. Specifically, emphasis is given to techniques based on feedback-linearisation followed by classical Luenberger observer design, and direct design of non-linear observers. Genetic algorithms (GAs), using the principles of evolution, natural selection and genetic mutation, are introduced to address difficulties in selecting correction gains for the observers, since no analytical tuning mechanisms yet exist. Experimental measurements from an automotive power steering test-facility are included, to demonstrate the enhanced performance attributes offered by tuning the proposed observer schemes, online, in this manner

Swarm-intelligence tuned current reduction for power assisted steering control in electric vehicle

In electric vehicle technology, battery energy conservation is paramount due to the dependency of all system operations on the available battery. The proportional, integral and derivative (PID) controller parameters in the electric power assisted steering system for electric vehicle needs to be tuned with the optimal performance setting so that less current is needed for its operation. This proposed two methods under the umbrella of swarm intelligence technique namely Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in order to reduce current consumption and to improve controller performance. The investigation involves an analysis on the convergence behavior of both techniques in search for accurate controller parameters. A comprehensive assessment on the assist current supplied to the assist motor of the system is also presented. Investigation reveals that the proposed controllers, PIDParticle Swarm Optimization and PID-Ant Colony Optimization are able to reduce the assist current supplied to the assist motor as compared to the conventional PID controller. This study also demonstrate the feasibility of applying both swarm intelligence tuning method in terms of reduced time taken to tune the PID controller as compared to the conventional tuning method

Control Performance Analysis of Power Steering System Electromechanical Dynamics

Modern power steering systems employ an electric motor drive system to provide torque assistance to the driver. The closed-loop mechanical system dynamics that impact stability, performance and steering feel are significantly impacted by the electrical dynamics of the actuator depending on the structure and tuning of the motor torque controller. This paper presents an integrated approach to the analysis of this electromechanical dynamic control interaction through mathematical modeling which is confirmed with simulations

Worked Example of X-by-Wire Technology in Electric Vehicle: Braking and Steering

The chapter emphasizes on the worked example of braking system and steering system for electric vehicle. The x-by-wire technology is investigated and validated comprehensively. Brake-by-wire is considered a new brake technology that uses electronic devices and control system instead of conventional brake components to carry out braking function based on wire-transmitted information. However, the physical parameters associated with braking function cause nonlinear characteristics and variations in the braking dynamics, which eventually degrade stability and performance of the system. Therefore, this study presents the design of fuzzy-PID controller for brake-by-wire (BBW) to overcome these undesired effects and also to derive optimal brake force that assists to perform braking operation under distinct road conditions and distinct road types. Electric power-assisted steering (EPAS) system is a new power steering technology for vehicles especially for electric vehicles (EV). It has been applied to displace conventional hydraulic power-assisted steering (HPAS) system due to space efficiency, environmental compatibility, and engine performance. An EPAS system is a driver-assisting feedback system designed to boost the driver input torque to a desired output torque causing the steering action to be undertaken at much lower steering efforts

Driver torque estimation in Electric Power Steering system using an H ∞ /H 2 Proportional Integral Observer

International audienceThis paper deals with the design of a Proportional Integral (PI) observer to estimate the driver torque in an Electric Power Steering (EPS) system. The PI observer is obtained by solving a multi-objective optimization problem: it should both be barely sensitive to road disturbances and sensor noise, and converge swiftly. The performance of the proposed observer is illustrated by simulation results using experimental data

Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications

Fault detection and tolerance of electrical machines in automotive applications

This project explores the drive for further electrification in the automotive industry and the challenges that this brings. Specifically this thesis focuses on the demands of safety and reliability; highlighting the subtle difference between the two concepts, explaining how legislation is forcing designers to consider the ways in which a system could fail and requiring them to create methods to detect and safely handle these failures, many of which can never be completely eliminated by design. With this motive in mind, the research within this thesis is focused on fault detection and condition monitoring. A novel method of rotor magnet condition monitoring is developed, an investigation into the effects of stator impedance variation is carried out to identify opportunities to develop diagnostic algorithms and sensorless control is considered as a back-up control method should a traditional position sensor fail. This thesis shows how current research and new techniques could be applied in the modern automotive industry; highlighting the demand for ever safer electronic systems as the world strives for greater levels of autonomy on the roads

Fault detection and tolerance of electrical machines in automotive applications

This project explores the drive for further electrification in the automotive industry and the challenges that this brings. Specifically this thesis focuses on the demands of safety and reliability; highlighting the subtle difference between the two concepts, explaining how legislation is forcing designers to consider the ways in which a system could fail and requiring them to create methods to detect and safely handle these failures, many of which can never be completely eliminated by design. With this motive in mind, the research within this thesis is focused on fault detection and condition monitoring. A novel method of rotor magnet condition monitoring is developed, an investigation into the effects of stator impedance variation is carried out to identify opportunities to develop diagnostic algorithms and sensorless control is considered as a back-up control method should a traditional position sensor fail. This thesis shows how current research and new techniques could be applied in the modern automotive industry; highlighting the demand for ever safer electronic systems as the world strives for greater levels of autonomy on the roads

Load Disturbance Torque Estimation for Motor Drive Systems with Application to Electric Power Steering System

Motors are widely used in industries due to its ability to provide high mechanical power in speed and torque applications. Its flexibility to control and quick response are other reasons for its widespread use. Disturbance torque acting on the motor shaft is a major factor which affects the motor performance. Considering the load disturbance torque while designing the control for the motor makes the system more robust to load changes. Most disturbance observers are designed for steady state load conditions. The observer designed here considers a general case making no assumptions about the load torque dynamics. The observer design methods to be used under different disturbance conditions are also discussed and the performances compared. The designed observer is tested in a Hardware-in-Loop (HIL) setup for different load conditions. A motor load torque estimation based Fault Tolerant Control (FTC) is then designed for an Electric Power Steering (EPS) system

Resilient Multi-range Radar Detection System for Autonomous Vehicles: A New Statistical Method

© 2023 Crown. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Critical issues with current detection systems are their susceptibility to adverse weather conditions and constraint on the vertical field view of the radars limiting the ability of such systems to accurately detect the height of the targets. In this paper, a novel multi-range radar (MRR) arrangement (i.e. triple: long-range, medium-range, and short-range radars) based on the sensor fusion technique is investigated that can detect objects of different sizes in a level 2 advanced driver-assistance system. To improve the accuracy of the detection system, the resilience of the MRR approach is investigated using the Monte Carlo (MC) method for the first time. By adopting MC framework, this study shows that only a handful of fine-scaled computations are required to accurately predict statistics of the radar detection failure, compared to many expensive trials. The results presented huge computational gains for such a complex problem. The MRR approach improved the detection reliability with an increased mean detection distance (4.9% over medium range and 13% over long range radar) and reduced standard deviation over existing methods (30% over medium range and 15% over long-range radar). This will help establishing a new path toward faster and cheaper development of modern vehicle detection systems.Peer reviewe