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**State Feedback Controller Design**

Lecture 08 State Feedback Controller Design 8.1 State Feedback and Stabilization 8.2 Full-Order Observer Design 8.3 Separation Principle 8.4 Reduced-Order Observer 8.5 State Feedback Control Design with Integrator Modern Control Systems

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**Modern Control Systems**

State Feedback and Stabilization Stabilization by State Feedback: Regulator Case Plant: State Feedback Law: Closed-Loop System: Theorem Given Controllable There exists a state feedback matrix, F, such that Modern Control Systems

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**Modern Control Systems**

A B D F State Feedback System (Regulator Case) Modern Control Systems

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**Modern Control Systems**

State Feedback Design in Controllable Form (8.1) Modern Control Systems

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**Modern Control Systems**

Suppose the desired characteristic polynomial (8.2) Comparing (8.1) and (8.2), we have (8.3) Modern Control Systems

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**Modern Control Systems**

State Feedback: General Case (Non-Zero Input Case) D B C A F State Feedback Control System Modern Control Systems

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**Modern Control Systems**

State Feedback Design with Transformation to Controllable Form Controllable From: Modern Control Systems

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**Modern Control Systems**

Transform to Controllable Form Coordinate Transform Matrix Controllable Form: Modern Control Systems

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**Modern Control Systems**

Example (A, B) is in controllable from, we can derive the state feedback gain from eq. (8.3) Modern Control Systems

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**Modern Control Systems**

Obtain the State Feedback Matrix by Comparing Coefficients Plant: State Feedback: Closed Loop System: Char. Equation: Suppose that the system is controllable, i.e. Modern Control Systems

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**Modern Control Systems**

Then, for any desired pole locations: We can obtain the desired char. polynomial By controllability, there exists a state feedback matrix K, such that (8.4) From (8.4), we can solve for the state feedback gain K. Modern Control Systems

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**Modern Control Systems**

Example Plant: State Feedback: Fig. State Feedback Design Example Modern Control Systems

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**Modern Control Systems**

Spec. for Step Response: Percent Overshoot 5%, Settling Rise time 5 sec. Desired pole locations: From (8.4), we get (8.5) By comparing coefficients on the both sides of 8.5), we obtain Modern Control Systems

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**Modern Control Systems**

Simulation Results Fig. Step response of above example Modern Control Systems

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**Modern Control Systems**

Ackermann Formula for SISO Systems Plant: State Feedback: The Matrix Polynomial Then the state feedback gain matrix is Modern Control Systems

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**Modern Control Systems**

Steady State Error Error Variable Lapalce Transform of the Error Variable From (3.6) By Final Value Theorem Modern Control Systems

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**Modern Control Systems**

Full-Order Observer Design Full-Order Observer Plant: Suppose is the observer state L: Observer gain Estimation error: Error Dynamics Equation: Modern Control Systems

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**Modern Control Systems**

Hence if all the eigenvalues of (A-LC) lie in LHP, then the error system is asy. stable and C A B + L Fig. Full-Order Observer Modern Control Systems

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**Modern Control Systems**

By duality between controllable from and obeservable form we have the following theorem. Theorem Given Observable There exists a observer matrix, L, such that Modern Control Systems

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**Modern Control Systems**

The eigenvalues of can be assigned arbitrarily by proper choice of K. Since have same eigenvalues, if we choose then the eigenvalues of (A-LC) can be arbitrarily assigned. Modern Control Systems

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**Modern Control Systems**

Separation Principle Plant: State Feedback Law using estimated state: State Equation: (8.6) Observer: Error Dynamics: (8.7) Modern Control Systems

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**Modern Control Systems**

Separation Principle (Cont.) From (8.6) and (8.7), we obtain the overall state equation (8.8) Eigenvalues of the overall state equation (7.17) (8.9) Equation (8.9) tells us that the eigenvalues of the observer-based state feedback system is consisted of eigenvalues of (A-BF) and (A-LC). Hence, the design of state feedback and observer gain can be done independently. Modern Control Systems

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**Observer-Based Control System**

Plant: Observer： State Feedback Law: Modern Control Systems

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**Modern Control Systems**

A B L K Fig. Observer-based control system Modern Control Systems

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**Modern Control Systems**

A B L K Fig. Observer-based control system with compensating gain Modern Control Systems

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**Modern Control Systems**

Reduced-Order Observer Design Consider the n-dimensional dynamical equation (8.10a) (8.10b) Here we assume that C has full rank, that is, rank C =q. Then, there exists a coordinate transformation which can be partitioned as (8.11) Modern Control Systems

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**Modern Control Systems**

Since , we have (8.12a) (8.12b) which become Plant: (8.13a) (8.13b) where Observer: Observer Modern Control Systems

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**Modern Control Systems**

Note that and w are function of known signals u and y. Now if the dynamical equation above is observable, an estimator of can be constructed. Theorem: The pair {A, C} in (8.10) or, equivalently, the pair in (8.12) is observable if and only if the pair in (8.13) is observable. Modern Control Systems

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**Modern Control Systems**

Let the estimate of be (8.14) Such that the eigenvalues of can be arbitrarily assigned by a proper choices of The substitution of w and into (8.143) yields (8.15) To eliminate the term of the derivative of y, by defining (8.16) Modern Control Systems

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**Modern Control Systems**

Using (8.15), then the derivative of (8.16) becomes From (8.15), we see that is an estimate of Define the following matrices Modern Control Systems

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**Modern Control Systems**

Reduced-Order Observer: where Modern Control Systems

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**Modern Control Systems**

A B + Fig. Reduced-Order Observer Modern Control Systems

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**Modern Control Systems**

Define Error Variable then we have Modern Control Systems

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**Modern Control Systems**

Since the eigenvalues of can be arbitrarily assigned, the rate of e(t) approaching zero or, equivalently, the rate of approaching can be determined by the designer. Now we combine with to form Then from We get Modern Control Systems

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**Modern Control Systems**

How to transform state equation to the form of (8.11) Consider the n-dimensional dynamical equation (8.17a) (8.17b) Here we assume that C has full rank, that is, rank C =q. Define where R is any (n-q)n real constant matrix so that P is nonsingular. Modern Control Systems

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**Modern Control Systems**

Compute the inverse of P as where Q1 and Q2 are nq and n(n-q) matrices. Hence, we have Modern Control Systems

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**Modern Control Systems**

Now we transform (8.17) into (8.11), by the equivalence transformation which can be partitioned as Modern Control Systems

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**Modern Control Systems**

SISO State Space System Integral Control: Augmented Plant: Modern Control Systems

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**Modern Control Systems**

State Feedback Control Design with Integrator Closed-Loop System: Modern Control Systems

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**Modern Control Systems**

Block diagram of the integral control system B C A K Fig.Block diagram of the integral control system Modern Control Systems

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**Modern Control Systems**

Example Spec. for Step Response: Percent Overshoot 10%, Settling time 0.5 sec. State Feedback Design: Modern Control Systems

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**Modern Control Systems**

From the steady state analysis in Sec. 3.4 Modern Control Systems

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**Modern Control Systems**

State Feedback Design with Error Integrator: Closed-Loop System: (8.18) Modern Control Systems

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**Modern Control Systems**

From (8.18), we get the char. eq. of the closed-loop system is (8.19) The desired char. eq. of the closed-loop system is (8.20) By comparing coefficients on left hand sides of (8.19) and (8.20), we obtain Modern Control Systems

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**Modern Control Systems**

Closed-Loop System: Final Value Theorem Steady State Error Modern Control Systems

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MULT. INTEGERS 1. IF THE SIGNS ARE THE SAME THE ANSWER IS POSITIVE 2. IF THE SIGNS ARE DIFFERENT THE ANSWER IS NEGATIVE.

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