A Control Algorithm and Vehicle Model for Stop & Go Cruise Contr下载-CSDN社区

摘要-本文介绍了一种针对停车和行驶情况设计的车辆距离和跟踪控制系统。详细描述了控制方案，并给出了测试控制系统的实验结果。本文还简要介绍了一种针对停车和行驶情况设计的车辆纵向模型。最后得出结论。两个连续车辆之间的距离被定义为距离控制问题。在同一环境中，两辆连续车辆的行驶路径的自动维护被定义为跟踪控制问题。这是两个截然不同的控制问题，构成了自动驾驶汽车最终目标的两个组成部分。本文提出了这两个问题的综合解决方案。组合问题变成了一个多变量系统。本文还简要介绍了车辆纵向模型。大多数现有模型旨在模拟汽车在相当高的速度下的行为。因此，使用这些模型开发的系统大多仅适用于高速公路。本文所讨论的模型被证明适用于高速和低速情况，因此可用于设计和模拟低速巡航控制系统。

In this thesis we consider the problem of designing and implementing Model Predictive Controllers (MPC) for stabilizing the dynamics of an autonomous ground vehicle. For such a class of systems, the non-linear dynamics and the fast sampling time limit the real-time implementation of MPC algorithms to local and linear operating regions. This phenomenon becomes more relevant when using the limited computational resources of a standard rapid prototyping system for automotive applications. In this thesis we first study the design and the implementation of a nonlinear MPC controller for an Active Font Steering (AFS) problem. At each time step a trajectory is assumed to be known over a finite horizon, and the nonlinear MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We demonstrate that experimental tests can be performed only at low vehicle speed on a dSPACE rapid prototyping system with a frequency of 20 Hz. Then, we propose a low complexity MPC algorithm which is real-time capable for wider operating range of the state and input space (i.e., high vehicle speed and large slip angles). The MPC control algorithm is based on successive on-line linearizations of the nonlinear vehicle model (LTV MPC). We study performance and stability of the proposed MPC scheme. Performance is improved through an ad hoc stabilizing state and input constraints arising from a careful study of the vehicle nonlinearities. The stability of the LTV MPC is enforced by means of an additional convex constraint to the finite time optimization problem. We used the proposed LTV MPC algorithm in order to design AFS controllers and combined steering and braking controllers. We validated the proposed AFS and combined steering and braking MPC algorithms in real-time, on a passenger vehicle equipped with a dSPACE rapid prototyping system. Experiments have been performed in a testing center equipped with snowy and icy tracks. For both controllers we showed that vehicle stabilization can be achieved at high speed (up to 75 Kph) on icy covered roads. This research activity has been supported by Ford Research Laboratories, in Dearborn, MI, USA.

Abstract—In this paper, new real-time methods for the lateral vehicle velocity and roll angle estimation are presented. Lateral tire forces, obtained from a multi-sensing hub (MSHub) unit, are used to estimate lateral vehicle velocity and a roll angle. In order to estimate lateral vehicle velocity, the recursive least square (RLS) algorithm is utilized based on a linear vehicle model and sensor measurements. In the roll angle estimation, the Kalman filter is designed for real-time estimation. The proposed estimation methods, RLS-based estimator and the Kalman filter, were verified by field tests on an experimental electric vehicle. Test results show that the proposed estimation methods provide better estimation performances and these methods are robust to road conditions.

Preface Acknowledgments Chapter 1—Intelligent Control Techniques 1 Introduction 2 Knowledge-Based Systems 3 Neural Networks 3.1 Introduction 3.2 Biological Neuronal Morphology 3.3 Static Neural Networks 3.4 Common Types of Artificial Neural Networks 3.5 Backpropagation Learning Algorithm 4 Fuzzy Logic 4.1 Fuzzy Systems and Rules 4.2 Fuzzy Reasoning and Aggregation 4.3 Fuzzy Control 5 Evolutionary Computing 5.1 GA Searching Algorithm 5.2 GA Selection 5.3 GA Reproduction 6 Summary References Chapter 2—Learning and Adaptation in Complex Dynamic Systems 1 Introduction 2 Systems Identification and Adaptive Control 2.1 Techniques for Systems Identification 2.1.1 Nonparametric methods 2.1.2 Parametric methods 2.2 Adaptive Control 2.2.1 Gain scheduling 2.2.2 Model-referenced adaptive control 2.2.3 Self-tuning regulators 3 Learning Techniques 3.1 Symbolic Learning 3.2 Numerical Learning 4 Artificial Neural Networks 4.1 Multilayer Perceptron 4.2 Kohonen Self-Organizing Network 4.3 Neural Networks as Identification Tools 4.4 Other Applications of ANN 5 Summary References Chapter 3—Applications of Evolutionary Algorithms to Control and Design 1 Introduction 1.1 Some Basics 1.2 Types of Evolutionary Algorithms 1.3 Types of Operations 1.3.1 Representation of Chromosomes 1.3.2 Fitness Value 1.3.3 Selection 1.3.4 Recombination and Mutation 1.4 Applications 2 Applications in Control 2.1 Robot Control 2.1.1 Planning of Joint Configuration of Manipulators 2.1.2 Acquisition of Behavior Rules of Autonomous Mobile Robots 2.2 Communication System Control 3 Applications in Design 3.1 Circuit Design 3.2 Graphics Design 3.2.1 Interactive EA Approach 3.2.2 Fitness Estimation in Interactive Approach 3.3 Music Design 3.3.1 Sound Synthesis 3.3.2 Algorithmic Composition 4 Other Applications 5 Summary References Chapter 4—Neural Control Systems and Applications 1 Introduction 2 Artificial Neural Networks (ANN) 2.1 The Backpropagation Network (BPN) 2.2 Kohonen Networks 2.3 Counterpropagation Networks 2.4 Hebbian Networks 2.5 Radial Basis Function Networks 2.6 Hopfield Networks 3 Neural Modeling and Identification 4 Neurocontroller-Design Methods 4.1 Supervised Control 4.2 Direct Inverse Control 4.3 Neural Adaptive Control 4.4 Backpropagation Through Time 4.5 Reinforcement Learning Control 4.6 Hybrid Control 5 Hardware and Software for ANN 6 Modular Neural-Visual Servo Control System 6.1 Introduction 6.2 Modular Neural-Control System 6.2.1 Control Networks 6.2.2 Decision Networks 6.3 Evaluation System 6.4 Preliminary Results 6.5 Conclusion 7 Summary References Chapter 5—Feature Space Neural Filters and Controllers 1 Introduction 2 Feature Space Filtering Using Neural Networks 2.1 Adaptive Signal Processing for Pattern Recognition 2.2 General Structure of the FSF System 2.3 A FSF Architecture Using Adaptive Linear Combiner Filter and Radial Basis Function Network Feature Extractor 2.3.1 FSF Realization 2.3.2 ALC-RBF FSF System Learning 2.3.3 Image Contour Enhancement and Recognition Optimization Using ALC-RBF FSF System 2.3.4 Discussion 2.4 FSF Architecture Using Multilayer Perceptron Filter and Principal Component Analysis Network Feature Extractor 2.4.1 MLP-PCA FSF System Realization 2.4.2 Learning in MLP-PCA FSF System 2.4.3 Example of MLP-PCA Feature Space System in Signal Filtering 2.4.4 Discussion 2.5 Concluding Remarks 3 Feature Space Identification and Control Schemes Using Neural Networks 3.1 Feature Space Neural Identification Topologies 3.2 Feature Space Neural Control Topologies 3.2.1 Data Versus Feature Space Neural Control Topologies 3.2.2 Feature Space Control Example 3.2.3 Identification Neural Network 3.2.3.1 Feature Extractor 3.2.3.2 Control Network 3.3 Discussion and Concluding Remarks 4 Summary References Chapter 6—Discrete-Time Neural Network Control of Nonlinear Systems 1 Introduction 2 Background 2.1 Neural Networks 2.2 Advantage of NN Over Adaptive Controllers 2.3 Stability of Dynamical Systems 2.4 MIMO Dynamical Systems 2.5 Tracking Problem 3 Neural Network Controller Design 3.1 NN Controller Structure and Error System 3.2 Well-Defined Control Problem 3.3 Proposed Controller 3.4 Weight Updates for Guaranteed Performance 4 Passivity of Dynamical Systems 4.1 Passive Systems 4.2 Passivity of the Closed-loop System and NN 5 Simulation Results 6 Summary References Chapter 7—Robust Adaptive Control of Robots Based on Static Neural Networks 1 Introduction 2 Notation 2.1 Permutation Operator “⊗” 2.2 GL Product Operator “•” 3 Neural Network Approximation 4 Lagrange-Euler Formulation of Robots 5 Dynamic Modeling of Robots Using Neural Networks 6 Controller Design 7 Case Study 7.1 Trajectory Planning 7.2 Simulation Settings 7.3 Non-Adaptive Control 7.4 Adaptive Control 8 Summary References Chapter 8—Error Correction Using Fuzzy Logic in Vehicle Load Measurement 1 Introduction 2 Vehicle Load Indicator 3 Describing Vehicle Loading States 4 Fuzzy Reasoning 5 Simulation Experiment 6 Summary References Chapter 9—Intelligent Control of Air Conditioning Systems 1 Fuzzy Controlled Air Conditioning System for Energy Conservation Applied to the Synthetic Fiber Plant 1.1 Introduction 1.2 Role and Problems of Air Conditioning Equipment in Synthetic Fiber Plant 1.2.1 Role of Air Conditioners in Spinning and Drawing/Twisting Processes 1.2.2 Problems of Air Conditioners in Spinning and Drawing/Twisting Processes 1.3 Fuzzy-Controlled Air Conditioning System for Energy Conservation 1.4 Results 1.5 Future Directions 1.6 Acknowledgment 2 A Learning Type Fuzzy Logic Control for Stabilizing Temperature and Humidity in a Clean Room 2.1 Introduction 2.2 Ordinary Fuzzy Logic Control System 2.3 A Learning Type Fuzzy Logic Control System 2.3.1 Structure of a Hierarchical Fuzzy Model 2.3.2 Succession of the Ordinary Fuzzy Model 2.4 Simulation Experiments 2.5 Practical Results 2.6 Conclusion 3 Occupant Condition Detecting Algorithm for Air Conditioning Systems 3.1 Introduction 3.2 Structure of the Pyroelectric Infrared Rays Detector 3.3 Segmentation of Occupants from Thermal Images 3.3.1 Removing Background Using the Fuzzy C-Means Algorithm 3.3.2 Identifying the Number of Occupants 3.3.3 Region Growing Algorithm 3.4 Method to Locate Occupants 3.4.1 Estimating the Distance Between the Sensor and Occupants 3.4.2 Experimental Results 3.5 Conclusion References Chapter 10—Intelligent Automation Systems at Petroleum Plants in Transient State 1 PID Controller Using Neuro-Fuzzy Hierarchical System in Feed Oil Switching 1.1 Introduction 1.2 Process Description 1.3 Control Problems in Feed Oil Switching 1.4 Neuro-Fuzzy Hierarchical Control System 1.4.1 Prediction Function 1.4.2 Correction Function 1.5 Control Algorithm 1.6 Results 1.7 Conclusion 2 Fuzzy Control System in Pump Start-up 2.1 Introduction 2.2 Outline of Pump Start-up Operation 2.3 Problems to Automate by Conventional Controller 2.3.1 Ramp controller 2.3.2 PID controller 2.4 Fuzzy Controller 2.4.1 Input Variable and Output Variable 2.4.2 Fuzzy Control Rules 2.5 Results 2.6 Conclusion 3 Fuzzy-PID Hybrid Control System in Feed Property Changing 3.1 Introduction 3.2 Process Description 3.3 Control Problems 3.4 Fuzzy-PID Hybrid Control System 3.5 Parameter Tuning 3.6 Results 3.7 Conclusion References Chapter 11—Intelligent Control for Ultrasonic Motor Drive 1 Introduction 2 Ultrasonic Motor Drive 2.1 The Equivalent Model of the USM 2.2 The Driving Circuit for the USM 3 Fuzzy Model-Following Control 4 Neural Network Model-Following Control 4.1 Description of the Neural Network 4.2 On-Line Learning Algorithm 5 Fuzzy Neural Network Model-Following Control 5.1 Description of the Fuzzy Neural Network 5.2 On-Line Learning Algorithm 6 The PC-Based Ultrasonic Motor Drive 7 Experimental Results 7.1 Fuzzy Model-Following Control 7.2 Neural Network Model-Following Control 7.3 Fuzzy Neural Network Model-Following Control 8 Summary References Chapter 12—Intelligent Automation of Herring Roe Grading 1 Introduction 2 Herring Roe Grading Process 2.1 Pre-Extraction Stage 2.2 Main Grading 2.3 Price Negotiation 3 Grading Technology 3.1 Shape Analysis 3.2 Ultrasonic Echo Imaging for Firmness Measurement 3.3 Vision-Based Weight Estimation 3.4 Color Grading 3.5 Fuzzy Decision-Making System 4 Prototype Development 4.1 Conveyor System 4.2 Ejection Mechanism 4.3 Sensory System 4.4 Prototype Control System 5 Prototype Testing 5.1 Laboratory Experiments 5.2 On-Site Production Test 5.3 Performance Evaluation and Possible Improvements 6 Summary Acknowledgment References Chapter 13—Intelligent Techniques for Vehicle Driving Assistance 1 Introduction 2 Multisensor Data Fusion 2.1 Introduction 2.2 Sensors 2.2.1 Static Environment 2.2.2 Dynamic Environment 2.3 Temporal Data Fusion 2.3.1 The Static Environment Perception 2.3.2 The Dynamic Environment Perception 2.3.2.1 Definition of the sensor and global maps 2.3.2.2 The different steps of the filtering operation 2.3.2.3 Reliability definition 2.3.3 The Copilot Mapping 2.4 Conclusion 3 Vehicle Modeling for Supervision of Manoeuvres 3.1 Introduction 3.2 Supervision of Manoeuvres 3.3 Situation Analysis 3.3.1 The Dynamic Model of the Vehicle 3.3.2 Vehicle Following 3.3.3 Highway Access Manoeuvre 3.3.4 A Lane-Changing Manoeuvre 3.4 Manoeuvre Monitor 3.5 Danger Controller 3.6 Requests Generation and Sensors Planning 3.7 The Driver Information Level 3.8 Conclusion 4 On-Board Real-Time Expert System for Control of the Vehicle 4.1 Introduction 4.2 Development of an Expert System for Control 4.2.1 Building the Knowledge Base 4.2.2 Basic Functioning of the Expert System for Control 4.3 Development of the Real-Time Expert System 4.3.1 Integration of the Expert System in the Real-Time Environment 4.3.2 Asynchronous Data Flows 4.3.3 Control Strategies for the IP 4.3.4 Interrupt Handling 4.3.5 Temporal Reasoning and Multiagent KBS 4.4 Conclusion 5 Summary Notation References Chapter 14—Intelligent Techniques in Air Traffic Management 1 Introduction 1.1 Future Air Navigation Systems (FANS) 1.2 Air Traffic Management 1.3 ATM System Issues for FANS 1.4 Applying AI Technology to ATC 1.4.1 Scheduling and Planning 1.4.2 Agent Technology 2 Intelligent ATC Systems 2.1 OASIS 2.2 COMPAS 2.3 CTAS 3 Intelligent Air Traffic Flow Management 3.1 A Model of Air Traffic Flow Management 3.2 Scheduling for ATFM 3.3 Heuristics 4 The Air Traffic Simulation Test 4.1 Interactive Plan Steering Architecture 4.2 AirTFM - the ATFM Test 5 Real-Time Search Algorithm for Air Traffic Flow Management 5.1 Real-Time Search Algorithms 5.1.1 Real-Time Planning and Scheduling Algorithms 5.1.2 Real-Time Monitoring and Control Algorithms 5.2 Time-Dependent Heuristic Search (TDHS) 5.3 Complexity Analysis of TDHS 5.4 Time-Dependent Cost Function 5.5 Experimental Results on TDHS 5.5.1 The Traveling Salesperson Problem 5.5.2 Base Performance of Heuristics 5.5.3 Results of TDHS on TSP 6 Summary References Index Copyright © CRC Press LLC

A simulation model based on temporal–spatial conflict and congestion for pedestrian–vehicle mixed evacuation has been investigated. Assuming certain spatial behaviors of individuals during emergency evacuation, a discrete particle swarm optimization with neighborhood learning factor algorithm has been proposed to solve this problem. The proposed algorithm introduces a neighborhood learning factor to simulate the sub-group phenomenon among evacuees and to accelerate the evacuation process. The ap