modeling and control overview - philadelphia university · 2015. 5. 18. · optimal control optimal...
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D R . T A R E K A . T U T U N J I
A D V A N C E D C O N T R O L S Y S T E M S
M E C H A T R O N I C S E N G I N E E R I N G D E P A R T M E N T
P H I L A D E L P H I A U N I V E R S I T Y
J O R D A N
P R E S E N T E D A T
H O C H S C H U L E B O C H U M
G E R M A N Y
M A Y 1 9 - 2 1 , 2 0 1 5
Modeling and Control Overview
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Math Modeling Review
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Math Modelling Categories
Static vs. dynamic
Linear vs. nonlinear
Time-invariant vs. time-variant SISO vs. MIMO Continuous vs. discrete Deterministic vs. stochastic
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Static vs. Dynamic
Models can be static or dynamic Static models produce no motion, fluid flow, or any other
changes.
Example: Battery connected to resistor
Dynamic models have energy transfer which results in power flow. This causes motion, or other phenomena that change in time.
Example: Battery connected to resistor, inductor, and
capacitor
4
v = 𝒊𝑹
idtC
1
dt
diLRiv
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Linear vs. Nonlinear
Linear models follow the superposition principle The summation outputs from individual inputs will be equal to the
output of the combined inputs
Most systems are nonlinear in nature, but linear models can be used to approximate the nonlinear models at certain point.
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Linear vs. Nonlinear Models
Linear Systems Nonlinear Systems
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Nonlinear Model: Motion of Aircraft
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Linearization using Taylor Series
sin 𝑥 = sin 0 + cos 0 𝑥 − 0 = 𝑥
Linearization is done at a particular point. For example, linearizing the nonlinear function, sin at x=0, using the first two terms becomes
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Time-invariant vs. Time-variant
The model parameters do not change in time-invariant models
The model parameters change in time-variant models Example: Mass in rockets vary with time as the fuel is
consumed.
If the system parameters change with time, the system is time varying.
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Time-invariant vs. variant
Time-invariant
F(t) = ma(t) – g
Where m is the mass, a is the acceleration, and g is the gravity
Time-variant
F = m(t) a(t) – g
Here, the mass varies with time. Therefore the model is time-varying
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Linear Time-Invariant (LTI)
LTI models are of great use in representing systems in many engineering applications.
The appeal is its simplicity and mathematical structure.
Although most actual systems are nonlinear and time varying
Linear models are used to approximate around an operating point the nonlinear behavior
Time-invariant models are used to approximate in short segments the system’s time-varying behavior.
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LTI Models: Block Diagrams Manipulation
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SISO vs. MIMO
Single-Input Single-Output (SISO) models are somewhat easy to use. Transfer functions can be used to relate input to output.
Multiple-Input Multiple-Output (MIMO) models involve combinations of inputs and outputs and are difficult to represent using transfer functions. MIMO models use State-Space equations
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System States
Transfer functions
Concentrates on the input-output relationship only.
Relates output-input to one-output only SISO
It hides the details of the inner workings.
State-Space Models
States are introduced to get better insight into the systems’ behavior. These states are a collection of variables that summarize the present and past of a system
Models can be used for MIMO models
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SISO vs. MIMO Systems
Transfer Function U(t) Y(t)
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Continuous vs. discrete
Continuous models have continuous-time as the dependent variable and therefore inputs-outputs take all possible values in a range
Discrete models have discrete-time as the dependent variable and therefore inputs-outputs take on values at specified times only in a range
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Continuous vs. discrete
Continuous Models
Differential equations
Integration
Laplace transforms
Discrete Models
Difference equations
Summation
Z-transforms
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Deterministic vs. Stochastic
Deterministic models are uniquely described by mathematical equations. Therefore, all past, present, and future values of the outputs are known precisely
Stochastic models cannot be described mathematically with a high degree of accuracy. These models are based on the theory of probability
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Control Overview
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Closed-Loop vs. Open-Loop Control
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Example: CNC Positional Control
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Transient and Steady State Response
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Step Response: First Order System
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Step Response: Second Order System
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Control Techniques / Strategies
Classical Control
Modern Control
Optimal Control
Robust Control
Adaptive Control
Variable Structure Control
Intelligent Control
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Classical Control
Classical control design are used for SISO systems.
Most popular concepts are:
Root Locus
Bode plots
Nyquist Stability
PID is widely used in feedback systems.
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Stability
Stable Unstable
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Classical Control: PID
Proportional-Integral-Derivative (PID) is the most commonly used controller for SISO systems
dt
)t(deKdt)t(eK)t(eK)t(u DIp
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Example
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Example
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Pole location effects
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Root Locus Example
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Modern Control
Modern control theory utilizes the time-domain state space representation.
A mathematical model of a physical system as a set of
input, output and state variables related by first-order differential equations.
The variables are expressed as vectors and the differential
and algebraic equations are written in matrix form.
The state space representation provides a convenient and compact way to model and analyze systems with multiple inputs and outputs.
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Optimal Control
Optimal control is a set of differential equations describing the paths of the state and control variables that minimize a “cost function”
For example, the jet thrusts of a satellite needed to bring it to desired trajectory that consume the least amount of fuel.
Two optimal control design methods have been widely used in industrial applications, as it has been shown they can guarantee closed-loop stability. Model Predictive Control (MPC)
Linear-Quadratic-Gaussian control (LQG).
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Robust Control
Robust control is a branch of control theory that deals with uncertainty in its approach to controller design.
Robust control methods are designed to function
properly so long as uncertain parameters or disturbances are within some set.
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Adaptive Control
Adaptive control involves modifying the control law used by a controller to cope with changes in the systems’ parameters
For example, as an aircraft flies, its mass will slowly
decrease as a result of fuel consumption; A control law can adapt to such changing conditions.
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Variable Structure Control
Variable structure control, or VSC, is a form of discontinuous nonlinear control.
The method alters the dynamics of a nonlinear system by application of a high-frequency switching control.
The main mode of VSC operation is sliding mode control (SMC).
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Intelligent Control
Intelligent Control is usually used when the mathematical model for the plant is unavailable or highly complex.
The most two commonly used intelligent controllers are
Fuzzy Logic
Artificial Neural Networks
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References
• Advanced Control Engineering (Chapter 8: State Space Methods for Control System Design) by Roland Burns 2001
• Modern Control Engineering (Chapter 10: State Space Design
Methods) by Paraskevopoulos 2002 • Modern Control Engineering (Chapters 9 and 10 Control System
Analysis and Design in State Space) by Ogata 5th edition 2010 • Feedback Systems: An Introduction to Scientists and Engineers
(Chapter 8: System Models) by Astrom and Muray 2009 • Modern Control Technology: Components and Systems by Kilian 2nd
edition