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Soft Computing Approach for Sensorless Control in Brake-By-Wire Systems with Electro-Mechanical Brake

Title
Soft Computing Approach for Sensorless Control in Brake-By-Wire Systems with Electro-Mechanical Brake
Translated Title
전기기계식 브레이크 기반 Brake-By-Wire 시스템에서의 센서리스 제어를 위한 소프트 컴퓨팅 접근법
Authors
Bae, Jun Hyung
Advisor(s)
Eun, Yong Soon
Co-Advisor(s)
Kim, Jong Hae
Issue Date
2017
Available Date
2017-08-02
Degree Date
2017. 8
Type
Thesis
Keywords
Brake-By-WireElectro-Mechanical BrakeGroup method of data handlingGenetic algorithmKalman filter
Abstract
In this thesis, we propose a new soft computing-based approach for sensorless fault-tolerant control in brake-by-wire (BBW) systems. Research on BBW systems in the automobile industry is actively proceeding. In or-der to mount and drive the electro-mechanical brake (EMB) used as the brake actuator in the hybrid vehicles and electric vehicles for operation reliability it is imperative that the clamping force data is not lost even if a failure occurs in the electrical and electronic sys-tems. In this study, the mathematical modeling of the mechanical part and the electric mo-tor of the EMB system was first established and the cascaded PI controller was designed based on the EMB model. The mechanical part consisted of a reduction gear, screw, in-ner/outer pads, and caliper. A permanent magnet synchronous motor (PMSM) was used for the electric motor and an electronic control unit (ECU) including the micro-controller and the inverter was constructed and experiments were performed. The EMB controller is configured as a cascaded PI control type, and the clamping force controller, speed control-ler, and the current controller are located in the order of the external controller to the inter-nal controller. The gain of the controller is designed to be easily adjusted using the param-eters of the motor. Also, the vector control method was applied to the PMSM to ensure optimal torque operation. Our goal is to apply a new hybrid-type system identification and estimation methods against failure or for sensorless control that can occur in the EMB electronic pedal sensor system and the clamping force sensor by applying the soft computing techniques such as a neural network, fuzzy and genetic algorithm. First, we propose a novel identification of an electronic brake pedal system for a vir-tual sensor system based on a hybrid approach using the group method of data handling (GMDH) and the genetic algorithm (GA). The main idea in the GMDH is to build an ana-lytical function in a feed-forward network based on a quadratic node transfer function whose coefficients are obtained using a regression technique. The analytical GMDH model has been found, and application of this model is very quick and inexpensive compared to other identification techniques. To develop the best network architecture for the GMDH, the GA is arranged in a new approach to design the whole architecture of the GMDH. Second, we study estimation of the clamping force in the EMB actuator part. The main sensors used in the EMB control system are a clamping force sensor to measure clamping force, a rotor position sensor to measure motor rotation angle, and a current sen-sor to measure the current of the three-phase motor. It is necessary to judge the failure of each sensor or developing without sensors in terms of cost and implementation and replace with an appropriate estimation value in the case of failure. In this study, the estimation of the clamping force is more accurate considering the hysteresis at the time of applying and releasing, and the dynamic stiffness model and torque balance model are combined by us-ing a novel Kalman filter optimized by the GA. The application of the GA improves the estimation accuracy by optimizing the noise covariance matrices of the Kalman filter and enables on-line estimation when using a high performance parallel processor. Finally, we verified the performance of the proposed algorithm through experiments. ⓒ 2017 DGIST
Table Of Contents
Abstract i-- List of contents iv-- List of tables vi-- List of figures vii-- Ⅰ. INTRODUCTION-- 1.1 Background and Motivation 1-- 1.2 Research Objective and Contribution 6-- 1.3 Thesis Organization 10-- Ⅱ. Modeling and Control of Electro-Mechanical Brake System: Basic Principle-- 2.1 Introduction 11-- 2.2 Modeling of the EMB System 13-- 2.2.1 Modeling of the PMSM 14-- 2.2.2 Modeling of the Planetary Gear 19-- 2.2.3 Modeling of the Screw Thread 23-- 2.2.4 Modeling of the Caliper 29-- 2.2.5 Modeling of the Pads 30-- 2.3 Control of the EMB System 33-- 2.3.1 Design of the Current Controller 34-- 2.3.2 Design of the Speed Controller 35-- 2.3.3 Design of the Force Controller 37-- 2.3.4 Simulation Results 37-- 2.3.5 Experiment for Evaluation of Simulation Results 39-- 2.4 Conclusions 45-- Ⅲ. Nonlinear Identification of Electronic Brake Pedal System Using Hybrid GMDH and Genetic Algorithm-- 3.1 Introduction 46-- 3.2 Related Works 47-- 3.3 Configuration of Developed BBW Systems 48-- 3.4 Review of System Identification Based on Soft Computing 49-- 3.4.1 Neural Networks 50-- 3.4.2 Fuzzy Model 52-- 3.4.3 Group Method of Data Handling 53-- 3.5 Application of Hybrid GMDH/GA to the Electronic Brake Pedal System 54-- 3.5.1 GMDH Algorithm 54-- 3.5.2 Hybrid GMDH/GA based on Genome Representation 57-- 3.5.3 Proposed Scheme of Electronic Brake Pedal System Identification 58-- 3.6 Experimental Results 60-- 3.6.1 Experimental Setup 60-- 3.6.2 Comparison of Results and Discussions 64-- 3.7 Conclusions 67-- Ⅳ. Clamping Force Estimation of Electro-Mechanical Brake Using a Hybrid Genetic Algorithm and Kalman Filter 4.1 Introduction 68-- 4.2 Related Works 70-- 4.3 EMB System Modeling 72-- 4.3.1 Electrical Modeling of the EMB 73-- 4.3.2 Mechanical Modeling of the EMB 74-- 4.3.3 Nonlinear Characteristics in the EMB 75-- 4.4 Estimation of the EMB Clamping Force 76-- 4.4.1 Dynamic Considerations 76-- 4.4.2 Dynamic Stiffness and Torque Balance Modeling 77-- 4.5 Design of a Hybrid Genetic Algorithm and Kalman Filter to Combine the Dynamic Stiffness Model and Torque Balance Model 82-- 4.5.1 Combining the Dynamic Stiffness Model and Torque Balance Model Using a Kal-man Filter 82-- 4.5.2 Optimizing the Noise Matrices of a Hybrid Genetic Algorithm and Kalman Filter 86-- 4.6 Experimental Setup and Results 88-- 4.6.1 Experimental Setup 88-- 4.6.2 Comparison of Results and Discussions 90-- 4.7 Conclusions 94-- Ⅴ. Conclusions and Future Works 96-- References 98
URI
http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000002375655
DOI
10.22677/thesis.2375655
Degree
Doctor
Department
Information and Communication Engineering
University
DGIST
Related Researcher
  • Author Eun, Yong Soon DSC Lab(Dynamic Systems and Control Laboratory)
  • Research Interests Resilient control systems; Control systems with nonlinear sensors and actuators; Quasi-linear control systems; Intelligent transportation systems; Networked control systems
Files:
Collection:
Emerging Materials ScienceThesesPh.D.


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