Download presentation

Presentation is loading. Please wait.

Published byReece Carnley Modified over 3 years ago

1
Contents 1.Motivation for Nonlinear ControlMotivation for Nonlinear Control 2.The Tracking ProblemThe Tracking Problem 1.Feedback LinearizationFeedback Linearization 3.Adaptive ControlAdaptive Control 4.Robust ControlRobust Control 1.Sliding modeSliding mode 2.High-gainHigh-gain 3.High-frequencyHigh-frequency 5.Learning ControlLearning Control 6.The Tracking Problem, Revisited Using the Desired Trajectory 1.Feedback Linearization 2.Adaptive Control 7.Filtered tracking error r(t) for second-order systems)Filtered tracking error r(t) for second-order systems) 8.Introduction to ObserversIntroduction to Observers 9.Observers + ControllersObservers + Controllers 10.Filter Based ControlFilter Based Control 1.Filter + Adaptive ControlFilter + Adaptive Control 11.SummarySummary 12.Homework ProblemsHomework Problems 1.A1 2.A2 3.A3 4.A4 – Design observer, observer + controller, control based on filterA4 – Design observer, observer + controller, control based on filter

2
2 Nonlinear Control Why do we use nonlinear control : –Tracking, regulate state setpoint –Ensure the desired stability properties –Ensure the appropriate transients –Reduce the sensitivity to plant parameters find state feedback output feedback Consider the following problem:

3
Applications and Areas of Interest Mobile Platforms UUV, UAV, and UGV Satellites & Aircraft Automotive Systems Steer-By-Wire Thermal Management Hydraulic Actuators Spark Ignition CVT Mechanical Systems Textile and Paper Handling Overhead Cranes Flexible Beams and Cables MEMS Gyros Robotics Position/Force Control Redundant and Dual Robots Path Planning Fault Detection Teleoperation and Haptics Electrical/Computer Systems Electric Motors Magnetic Bearings Visual Servoing Structure from Motion Nonlinear Control and Estimation Chemical Systems Bioreactors Tumor Modeling

4
The Mathematical Problem Typical Electromechanical System Model Classical Control Solution Obstacles to Increased Performance –System Model often contains Hard Nonlinearities –Parameters in the Model are usually Unknown –Actuator Dynamics cannot be Neglected –System States are Difficult or Costly to Measure

5
Nonlinear Lyapunov-Based Techniques Provide –Controllers Designed for the Full-Order Nonlinear Models –Adaptive Update Laws for On-line Estimation of Unknown Parameters –Observers or Filters for State Measurement Replacement –Analysis that Predicts System Performance by Providing Envelopes for the Transient Response The Mathematical Solution or Approach Mechatronics Based Solution Transient Performance Envelopes

6
6 Nonlinear Control Vs. Linear Control Why not always use a linear controller ? –It just may not work. Ex: Choose We see that the system cant be made asymptotically stable at On the other hand, a nonlinear feedback does exist : Then Asymptotically stable if Then

7
7 Example Even if a linear feedback exists, nonlinear one may be better. Ex: + _ + _

8
8 Example (continued) Let us use a nonlinear controller : To design it, consider the same system in the form: If Why is that especially interesting? If we could get onto that line then the system converges to the origin Both systems have interesting properties, can we combine the best features of each into a single control?

9
9 Example (continued) sliding line Created a new trajectory: the system is insensitive to disturbance in the sliding regime Variable structure control HW Simulate this system and control. Be sure to plot the evolution of the states

10
Example (continued) 10

11
11 The Tracking Problem

12
12 The Tracking Problem (continued) Feedback Linearization Exact Model Knowledge

13
Example Exact Model Knowledge Dynamics: Mass Nonlinear Damper DisturbanceVelocity Control Input a, b are constants Tracking Control Objective: Open Loop Error System: Controller: Closed Loop Error System: Solution: Feedforward Feedback Assume a, b are known Drive e(t) to zero Exponential Stability

14
Example Exact Model Knowledge Mass Nonlinear Damper DisturbanceVelocity Control Input a, b are constants Open Loop Error System: Control Design: Closed Loop Error System: Solution: Feedforward Feedback Assume a, b are known Exponential Stability Lyapunov Function: A different perspective on the control design

15
15 Adaptive Control By Assumption 2: both f(x) and W(x) are bounded. Constant that can be factored out Yet to be designed, feed- forward term based on an estimate of the parameters

16
16 Adaptive Control (continued) Lyapunov-like lemma Note: detailed in deQueiroz

17
17 Adaptive Control (continued)

18
Example Unknown Model Parameters Open Loop Error System: Control Design: a, b are unknown constants Same controller as before, but and are functions of time How do we adjust and ? Use the Lyapunov Stability Analysis to develop an adaptive control design tool for compensation of parametric uncertainty Closed Loop Error System: At this point, we have not fully developed the controller since and are yet to be determined. parameter error

19
( is UC) Example Unknown Model Parameters Fundamental Theorem effects of conditions i) and ii) i) If ii) If is bounded iii) If is bounded satisfies condition i) finally becomes a constant Non-Negative Function: Time Derivative of V(t): is bounded examine condition ii) design and substitute the dynamics for constant effects of condition iii)

20
Example Unknown Model Parameters Substitute Error System: How do we select and such that ? Update Law Design: Substitute in Update Laws: and Fundamental Theorem is bounded all signals are bounded Fundamental Theorem Feedforward Feedback control structure derived from stability analysis control objective achieved is bounded

21
How Can We Use the Adaptive Controller? Design adaptive control to track a desired trajectory while compensating for unknown, constant parameters (parametric uncertainty) Adaptive control with backstepping in cascaded subsytems to track a desired trajectory while compensating for unknown, constant parameters (parametric uncertainty)

22
How Can We Use the Adaptive Controller? (continued) Adaptive control with backstepping in cascaded subsytems to track a desired trajectory while compensating for unknown, constant parameters (parametric uncertainty)

23
How Can We Use the Adaptive Controller? What about the case where input multiplied by an unknown parameter, can we design adaptive control to track a desired trajectory while compensating for unknown, constant parameters (parametric uncertainty) Homework A.2-2

24
24 Robust Control Restriction on the structure but not the uncertainty

25
25 Robust Control (continued)

26
26 Robust (Sliding Mode) Control

27
27 Robust (Sliding Mode) Control (continued)

28
28 Robust (High-Gain) Control

29
29 Robust (High-Gain) Control (continued)

30
30 Robust (High-Gain) Control (continued)

31
31 Robust (High-Frequency) Control =

32
32 Learning Control

33
33 Learning Control (continued)

34
34 Learning Control (continued)

35
35 Learning Control (continued)

36
36 The Tracking Problem, Revisited Using the Desired Trajectory Differentiable assumption needed in analysis but not required to implement control. Feedback Linearization

37
Mean Value Theorem for scalar function

38
38 The Tracking Problem, Revisited (continued)

39
39 The Tracking Problem, Revisited (continued) Region is adjustable (not a fixed local region)

40
40 Adaptive Control 40 The Tracking Problem, Revisited (continued)

41
41 The Tracking Problem, Revisited (continued)

42
42 Continuous Asymptotic Tracking

43
43 Continuous Asymptotic Tracking (continued)

44
44 Continuous Asymptotic Tracking (continued)

45
45 Continuous Asymptotic Tracking (continued)

46
46 Continuous Asymptotic Tracking (continued)

47
47 Continuous Asymptotic Tracking (continued)

48
48 Continuous Asymptotic Tracking (continued)

49
49 Continuous Asymptotic Tracking (continued)

50
50 Continuous Asymptotic Tracking (continued)

51
51 Continuous Asymptotic Tracking (continued)

52
52 Continuous Asymptotic Tracking (continued)

53
53 Feedback Linearization for Second-Order Systems

54
54 Feedback Linearization Problem (continued)

55
55 Feedback Linearization Problem (continued)

56
56 Feedback Linearization Problem (continued)

57
57 Previous Problem Using a Robust Approach

58
58 Previous Problem Using a Robust Approach

59
Nonlinear Lyapunov-Based Techniques Provide –Observers or Filters for State Measurement Replacement Observers Mechatronics Based Solution

60
Estimate of the State Observers Alone xfxy (,) ygxyu (,,) u ? x Nonlinear Observer

61
61 Observers Example: If angle is measured with an encoder then the velocity must be estimated, e.g. using backwards difference. Encoder Measured Position Position Velocity Estimate Backwards difference may yield noisy estimate of actual velocity

62
62 Observers (continued)

63
63 Observers (continued)

64
64 Observers (continued)

65
65 Observers (continued)

66
66 Observers (continued)

67
67 Observers (continued)

68
68 Observers (continued)

69
69 Observers (continued)

71
71 Observers (continued)

73
73 Observers (continued)

74
74 Combining Observers & Controllers (continued)

75
75 Observers (continued)

76
76 Observers (continued) Mean Value Theorem (in one variable)

77
77 Observers (continued)

78
78 Observers (continued)

79
Observer + Controller xfxy (,) ygxyu (,,) u ? x Nonlinear Controller Nonlinear Observer

80
80 Combining Observers & Controllers

81
81 Combining Observers & Controllers

82
82 Combining Observers & Controllers (continued)

83
83 Combining Observers & Controllers (continued)

84
84 Combining Observers & Controllers (continued)

85
85 Combining Observers & Controllers (continued)

86
86 Combining Observers & Controllers (continued)

87
87 Combining Observers & Controllers (continued)

88
88 Filter Based Control

89
89 Filtering Control (continued)

90
90 Filtering Control (continued)

91
91 Filtering Control (continued)

92
92 Filtering Control (continued)

93
93 Filtering Control (continued)

94
94 Filtering Control (continued)

95
95 Filtering Control (continued)

96
96 Filtering Control (continued)

97
97 Adaptive Approach

98
98 Adaptive Approach (continued)

99
99 Adaptive Approach (continued)

100
100 Adaptive Approach (continued)

101
101 Adaptive Approach (continued)

102
102 Adaptive Approach (continued)

103
103 Variable Structure Observer

104
104 Variable Structure Observer (continued)

105
105 Variable Structure Observer (continued)

106
106 Variable Structure Observer (continued)

107
107 Variable Structure Observer (continued)

108
108 Filtering Control, Revisited

109
109 Filtering Control, Revisited (continued)

110
110 Filtering Control, Revisited (continued)

111
111 Filtering Control, Revisited (continued)

112
112 Filtering Control, Revisited (continued)

113
113 Filtering Control, Revisited (continued)

114
114 Filtering Control, Revisited (continued)

115
Summary 115

116
Summary 116

117
Homework A.1

118
Homework A.1-1 (sol)

121
Homework A.1-2 (sol)

127
Homework A.1-3 (sol)

128
k=1 Homework A.1-3 (sol)

129
Homework A.1-2 (sol)

130
Homework A.1-3 (sol)

132
Note that the analysis only guaranteed Ultimate Bounded tracking error. Homework A.1-3 (sol)

134
Homework A.1-2 (sol)

135
Homework A.1-3 (sol)

138
Homework A.1-2 (sol)

139
One of the advantages of the repetitive learning scheme is that the requirement that the robot return to the exact same initial condition after each learning trial is replaced by the less restrictive requirement that the desired trajectory of the robot be periodic.

140
Homework A.1-2 (sol) a=1, k=kd=5

141
Homework A.2

142
Homework A.2-1 (sol)

143
Homework A.2-2 (sol)

144
Homework A.2-3 (sol)

147
Homework A.3

148
2.

149
Homework A.3 3. 4.

150
Homework A.3-1 (sol)

151
Homework A.3-2 (sol) 2.

152
Homework A.3-3 (sol) 3.

153
Homework A.3-4 (sol) 4.

154
Homework A.3-5 (sol)

158
Homework A.4

159
Homework A.4-1 (sol)

160
But that estimate has velocity measurement in it?

161
Homework A.4-2 (sol)

165
Homework A.4-3 (sol)

Similar presentations

OK

Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.

Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Download ppt on verb in hindi Ppt on intelligent manufacturing Ppt on care of public property records Ppt on pricing policy benefits Ppt on bharatiya janata party Ppt on meeting skills pdf Blood vessels anatomy and physiology ppt on cells Ppt on resistance spot welding Ppt on 2 dimensional figures and 3 dimensional slides Ppt on business cycle phases in order