lyapunov theory and design - indian institute of ... techniques/ppt/bhasin.pdf · lyapunov theory...
TRANSCRIPT
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Lyapunov Theory and Design
TEQIP Workshop on
Control Techniques and Applications IIT Kanpur, 19-23 September 2016
Dr. Shubhendu Bhasin
Department of Electrical Engineering IIT Delhi
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Feedback Control 101
Syste
m Controller
Sensors
desired
behavior
disturbance
Actuator
s
actual
behavior
noise
Control = Sensing + Computation + Actuation Control Design Process
Control System Objectives
• Modeling – ODE, PDE.
• Analysis – stability, robustness, performance
• Synthesis – Feedback Design Tools
• Regulation
• Tracking Stability
• Modeling Uncertainties
• Disturbances
• Sensor Noise
Robustness
• Transient
• Steady State
• Minimizing cost function
Performance
Inverted pendulum regulation Satellite attitude tracking
disturbance rejection
u + -
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Why Study Nonlinear Systems? Real world is inherently nonlinear !
Mass-Spring-Coulomb Damper
Pendulum
Saturation Deadzone u u
Quantization u
Inherently nonlinear physical
laws
Actuator
nonlinearities
50
-50
e.g. on-off control, adaptive control laws Intentional nonlinearities
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Why Study Linear Systems?
• Linear approximation about operating point
Steady level flight
• Superposition: Impulse response characterizes LTI system behavior
• Closed-form solution
• Universal controllers: Pole-placement, LQR etc.
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Limitations of Linearization
• Linearization of and produce the same linear
system!
• Linearization captures local behavior around the operating point
• Linearization cannot capture rich nonlinear behavior
Limit Cycle
Multiple Equilibria
Bifurcation
Chaos
• Linearization not possible for “hard” nonlinearities e.g. backlash, saturation etc.
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Nonlinear System Analysis
• No general method to solve nonlinear differential equations
• Superposition does not hold
• No general method to design controllers
Lyapunov (1857-1918)
Challenges
Lyapunov Theory (1892)
• Select a scalar positive function
• Choose u such that V(x, t) decreases i.e.
|e(t)|
t 0
|e(t)|
t 0
Bounded or
Ultimately
Bounded Exponential |e(t)|
t 0
Asymptotic
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Nonlinear Systems
Autonomous System:
Non-Autonomous System:
Existence and Uniqueness of Solutions
Equilibrium Point (s)
Solution of
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Example: Pendulum System
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Stability of Equilibrium Points
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Van der Pol Oscillator
Stable or unstable ?
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Asymptotic Stability
Asymptotic Stability = Stability + Convergence Convergence Stability ?
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Exponential Stability (Rate of Convergence)
Exponential Asymptotic ?
Asymptotic Exponential ?
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Local Vs Global Stability
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Lyapunov’s Indirect Method (Linearization)
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Lyapunov’s Direct Method (Motivating Example)
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Motivating Example (contd..)
Key Observations:
• Zero Energy corresponds to equilibrium
• Asymptotic stability convergence of mechanical energy to zero
• Stability properties are related to variation of mechanical energy
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Lyapunov’s Direct Method (Basic Idea)
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Lyapunov’s Stability Theorem (Local)
V(x) is positive definite
negative-definite
negative semi-definite
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Exercise
Asymptotically stable?
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Lyapunov’s Stability Theorem (Global)
V(x) is radially unbounded
Radial Unboundedness is Necessary
Divergence of states while
moving to lower “energy” curves
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Exercise
Remarks:
• Lyapunov theorems give sufficient conditions for stability
• Failure of a Lyapunov function candidate to satisfy the theorem does not
mean that the eq. point is unstable.
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Example (Pendulum with Friction)
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Stability Analysis
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Using a different V(x)
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LaSalle’s Invariance Set Theorem
• Useful for proving asymptotic stability when
derivative of V(x) is only negative semi-definite
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Pendulum with friction (Revisit)
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Non-Autonomous Systems
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Non-Autonomous Systems (Stability Definitions)
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Lyapunov Theorems for Non-Autonomous Systems
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Barbalat’s Lemma
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Asymptotic Properties of functions and its derivatives
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Lyapunov-Like Lemma
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Example continued…
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Boundedness of Solutions
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Uniformly Ultimately Bounded Stability
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Adaptive Control
TEQIP Workshop on
Control Techniques and Applications IIT Kanpur, 19-23 September 2016
Dr. Shubhendu Bhasin
Department of Electrical Engineering IIT Delhi
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Introduction
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Historical Perspective
X-15
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Basic Idea
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Adaptive Control: A Parametric Framework
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Indirect and Direct Adaptive Control
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Indirect and Direct Adaptive Control
Indirect Adaptive Control
Direct Adaptive Control
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Gain Scheduling Adaptive Control
No parameter estimation
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Model Reference Adaptive Control (MRAC)
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Adaptive Control Topics
• Direct MRAC (SISO)
• Indirect MRAC (SISO)
• Lyapunov-Based Nonlinear Adaptive Control
• Parameter Convergence
• Parameter Drift
• Adaptive Backstepping