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Motion Control of Underactuated Mechanical Systems

:	Javier Moreno-Valenzuela, Carlos Aguilar-Avelar
:	Motion Control of Underactuated Mechanical Systems
:	Springer-Verlag
:	9783319583198
:	1
:	CHF 85.70
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	230
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

This volume is the first to present a unified perspective on the control of underactuated mechanical systems. Based on real-time implementation of parameter identification, this book provides a variety of algorithms for the Furuta pendulum and the inertia wheel pendulum, which are two-degrees-of-freedom mechanical systems. Specifically, this work addresses and solves the problem of motion control via trajectory tracking in one joint coordinate while another joint is regulated. Besides, discussions on extensions to higher degrees-of-freedom systems are given. The book, aimed at control engineers as well as graduate students, ranges from the problem of parameter identification of the studied systems to the practical implementation of sophisticated motion control algorithms. Offering real-world solutions to manage the control of underactuated systems, this book provides a concise tutorial on recent breakthroughs in the field, original procedures to achieve bounding of the error trajectories, convergence and gain tuning guidelines.

	Preface	6
	Contents	8
	Acronyms	12
	1 Introduction	13
	1.1 Background	13
	1.1.1 Underactuated Systems	13
	1.1.2 Nonlinear Dynamics and Control	15
	1.1.3 Parameter Identification	18
	1.1.4 Motion Control of Underactuated Systems	19
	1.2 Motivations and Objectives	21
	1.3 Outline	21
	2 Preliminaries	25
	2.1 Fundamentals of Nonlinear Systems	25
	2.2 Fundamental Properties	27
	2.3 Concepts of Stability	27
	2.4 Barbalat's Lemma	30
	2.5 Boundedness and Ultimate Boundedness	30
	2.6 Feedback Linearization	31
	2.7 Artificial Neural Networks	34
	2.7.1 Universal Function Approximation Property	36
	3 Identification of Underactuated Mechanical Systems	1
	3.1 Introduction	38
	3.2 Identification of the Furuta Pendulum	39
	3.2.1 Dynamic Model	39
	3.2.2 Filtered Regression Model	41
	3.2.3 Discretization of the Filtered Regression Model	43
	3.2.4 Experimental Platform	44
	3.2.5 Motion Control Experiment	45
	3.2.6 Joint Velocity Calculation	46
	3.2.7 Least Squares Algorithm	47
	3.2.8 Results of the Identification Procedure	48
	3.3 Identification of the Inertia Wheel Pendulum	51
	3.3.1 Dynamic Model	51
	3.3.2 Filtered Regression Model	53
	3.3.3 Discretization of the Filtered Regression Model	54
	3.3.4 Experimental Platform	55
	3.3.5 Motion Control Experiment	56
	3.3.6 Joint Velocity Calculation	56
	3.3.7 Least Squares Algorithm	57
	3.3.8 Results of the Identification Procedure	58
	3.4 Concluding Remarks	60
	4 Composite Control of the Furuta Pendulum	61
	4.1 Introduction	61
	4.2 Dynamic Model	62
	4.3 Control Problem Formulation	63
	4.4 Design of the Proposed Scheme	64
	4.4.1 Feedback Linearization Part	64
	4.4.2 Energy-Based Compensation	65
	4.4.3 Summary of the Composite Controller	69
	4.5 Analysis of the Closed-Loop Trajectories	70
	4.6 Controller for the Performance Comparison	71
	4.6.1 Output Tracking Controller	71
	4.7 Experimental Evaluation	72
	4.7.1 Experimental Results	72
	4.7.2 Performance Comparison	75
	4.8 Concluding Remarks	78
	5 Feedback Linearization Control of the Furuta Pendulum	79
	5.1 Introduction	79
	5.2 Dynamic Model and Error Dynamics	80
	5.3 Control Problem Formulation	82
	5.4 Design of the Proposed Scheme	82
	5.5 Analysis of the Closed-Loop Trajectories	83
	5.5.1 Ultimate Bound	88
	5.5.2 Boundedness of the Error Trajectories	89
	5.6 Controllers for the Performance Comparison	90
	5.6.1 PID Controller	90
	5.6.2 Output Tracking Controller	91
	5.7 Experimental Evaluation	92
	5.7.1 Experimental Results	92
	5.7.2 Performance Comparison	95
	5.8 Concluding Remarks	101
	6 Adaptive Neural Network Control of the Furuta Pendulum	103
	6.1 Dynamic Model and Error Dynamics	104
	6.2 Control Problem Formulation	106
	6.3 Design of the Proposed Scheme	106
	6.4 Analysis of the Closed-Loop Trajectories	109
	6.5 Controllers for the Performance Comparison	118
	6.5.1 PID Controller	118
	6.5.2 Jung and Kim Controller	119
	6.5.3 Chaoui and Sicard Controller	119
	6.6 Experimental Evaluation	120
	6.6.1 Experimental Results and Performance Comparison	120
	6.7 Concluding Remarks	128
	7 Composite Control of the IWP	1
	7.1 Introduction	129
	7.2 Dynamic Model	131
	7.3 Control Problem Formulation	132
	7.4 Design of the Proposed Scheme	132
	7.4.1 Feedback Linearization Controller	132
	7.4.2 Energy-Based