Dry Clutch Control for Automotive Applications (eBook)

Professor Canudas-de-Wit was born in Villahermosa, Tabasco, Mexico in 1958. He received his B.Sc. degree in electronics and communications from the Technological Institute of Monterrey, Mexico in 1980. In 1984 he received his M.Sc. in the Department of Automatic Control, Grenoble, France. In 1985 he was visiting researcher at Lund Institute of Technology, Sweden. In 1987 he received his Ph.D. in automatic control from the Polytechnic of Grenoble (Department of Automatic Control), France. Since then he has been working at the same department as "Directeur de Recherche at the CNRS", where he teaches and conducts research in the area of nonlinear control of mechanical systems, and on networked controlled systems. His research topics includes: vehicle control, adaptive control, identification, control of walking robots, systems with friction, AC and CD drives, and networked controlled systems. He has established several industrial collaboration projects with major French companies (FRAMATOME, EDF, CEA, IFREMER, RENAULT, SCHNEIDER, ILL). He has written a book in 1988 on adaptive control of partially known systems: theory and applications (Elsevier). In 1991, he edited Advanced Robot Control (978-3-540-54169-1), in Lecture Notes in Control and Information Sciences and, in 1997, Theory of Robot Control (978-3-540-76054-2) in Communications and Control Engineering. He was an associate editor of IEEE Transactions on Automatic Control, from 1992, to 1997, and of Automaticafrom 1999, to 2002. His research publications include: more than 120 International conference papers, and more than 47 published papers in international journals. He has supervised 20 Ph. D. students.

Series Editors’ Foreword 8
Contents 11
1 Introduction 15
Part I - Mechanical System and Comfort Requirements 18
2 Powertrain 19
2.1 Brief Mechanical Description 19
2.1.1 Elements of the Engine Block 19
2.1.2 Engine 20
2.1.3 Flywheel and Dual-mass Flywheel 23
2.1.4 Dry Clutch 25
2.1.5 Driveline 30
2.2 Models 32
2.2.1 Simulation Model 32
2.2.2 Control Model 38
2.2.3 Driver Model 42
3 Clutch Comfort 45
3.1 Detailed Analysis of the Clutch Use 45
3.1.1 When the Clutch Is Used 45
3.1.2 Standing-start Analysis 46
3.1.3 Upward Gearshift Analysis 48
3.1.4 Clutch Torque at Synchronization 49
3.1.5 Clutch-related Driving Comfort 50
3.2 Influence of the Driveline Parameters 51
3.3 State-of-the-art 52
3.3.1 Manual Transmission 52
3.3.2 Automated Manual Transmission 52
3.3.3 Clutchless Gearshifting 53
3.4 Motivation and Methodology 54
3.4.1 A Manual Transmission in Troubled Waters 54
3.4.2 Passive Means of Increasing the Clutch Comfort 54
3.4.3 Conclusion on the Passive Means of Improvement 58
Part II - Dry Clutch Engagement Control 59
4 Synchronization Assistance 60
4.1 Principle 60
4.2 Synchronization Assistance Assuring the GV No-lurch Condition 62
4.2.1 Control Law 62
4.2.2 Feedback Effects and Engine Torque Control 63
4.3 GV No-lurch Condition Limitations 66
4.4 Synchronization Assistance with Ideal Engagement Conditions 68
4.4.1 Principle 68
4.4.2 Cost Function 68
4.4.3 Optimal Problem Formulation 69
4.4.4 Linear Quadratic Optimal Control 70
4.4.5 Optimal Control by Differential Analysis 70
4.4.6 Optimal Control by Quadratic Programming 79
5 Optimal Standing-start 81
5.1 Principle 81
5.2 Exact Dynamic Replanning 82
5.2.1 Model Predictive Control 82
5.2.2 Optimization Horizon Update 82
5.2.3 Model Predictive Control Control Structure 84
5.2.4 Results 85
5.3 Simplified Dynamic Replanning 87
5.3.1 Segment-approximated Model Predictive Control 87
5.3.2 State Vector Reduction 89
5.3.3 Results 90
6 Clutch Friction and Torque Observer 93
6.1 Principle 93
6.2 Friction-coefficient Observer 94
6.2.1 Motivation 94
6.2.2 Driveline Models 95
6.2.3 Multi-input Multi-output Linear Time-variable Observer 95
6.2.4 Sampled-data Observer 98
6.3 Clutch Torque Observer for Automated Manual Transmission 99
6.3.1 Principle 99
6.3.2 Unknown-input Ob 99
6.3.3 Estimation Error Analysis 100
6.3.4 Performance Comparison 103
6.4 Clutch-torque Observer for Manual Transmission 104
6.4.1 Motivation 104
6.4.2 Observer Structure 104
6.4.3 Continuous Unknown-input Observer 105
6.4.4 Non-uniform Sampling 106
6.4.5 Results 110
6.5 Conclusions 110
7 Experimental Results and Control Evaluation 113
7.1 Track Testing 113
7.2 Synchronization-assistance Strategy 114
7.2.1 Clio II K9K Prototype 114
7.2.2 Control Sequencing 117
7.2.3 First Phase: Open-loop Control 118
7.2.4 Second Phase: Optimal Control 118
7.2.5 Third Phase: Final Clutch Closure 122
7.2.6 Experimental Results 122
7.3 Conclusions 128
8 Open Problems and Conclusions 129
8.1 Conclusions 129
8.2 Further Work 131
Appendix 132
Appendix A - Optimization Methods 132
A.1 Dynamic Lagrangian Multipliers 132
A.1.1 Inequality Constraints-free Optimization 133
A.1.2 Optimization Under Inequality Constraints 134
A.2 Alternative Solution of the Two-point Boundary-value Problem by Generating Functions 136
A.2.1 Generating Functions 138
A.2.2 Hamiltonian System Flow 138
A.2.3 Two-point Boundary-value Problem Solution 139
A.3 Reconduction to a Quadratic Programming Formulation 140
Appendix B - Proof of Theorem 6.1 143
Appendix C - Brief Description of the LuGre Model 145
References 146
Index 148

"5 Optimal Standing-start (p. 71--72)

5.1 Principle

The solution of the optimal control problem through the use of a quadratic programming formulation has raised the activation time limitation imposed by the solution of the TPBVP over long intervals, allowing calculation of a complete optimal standing-start trajectory. This kind of solution could be used for the control of a AMT or a clutch-by-wire system where the clutch has to be controlled by the gearbox control unit from the very beginning of the engagement. In the previous chapter the solution of the optimal control problem has been obtained under the hypothesis of a constant engine torque.

This condition, perfectly reasonable for a short activation time over the last part of the engagement, is very hard if not downright impossible to assure when considering a whole standing-start operation. The possibility of having an engine torque evolution described by an homogeneous linear system could partially solve this di?culty but does not allow for a change of the driver’s wish during the engagement.

Taking into account these changes not only increases the driving comfort but is essential for security since the driver has to be always able to intervene in the vehicle behavior in order to react to a rapid change in his environment. To meet the opposing needs of taking into account the driver’s input and having some simple hypothesis on future input allowing for an optimal planning, the trajectory is not computed once and for all at the beginning of the engagement and then simply tracked by feedback, but periodically updated to follow the changes in the driver’s input and the actual behavior of the vehicle.

5.2 Exact Dynamic Replanning

5.2.1 Model Predictive Control

The model-based predictive control (MPC) is a control strategy that, in its most general formulation, consists in solving an optimal control problem in QP formulation for a simpli?ed and/or liberalized system over a ?nite time horizon of Nh samples and issuing the ?rst Nc control samples before using the newly measured or estimated system state x0 as the starting point for a new optimization. Compared to the previous control schemes the MPC strategy shows two main advantages, the ?rst being that a change in the driver’s input can be readily taken into account in the next optimization. The second advantage is that trajectory stabilization does not need to be assured by an external feedback loop since each new optimal trajectory has as a starting point the measured or estimated system state x0 that directly takes into account the actual system behavior."