Robust and Adaptive Control With Aerospace Applications by Eugene Lavretsky

By Eugene Lavretsky

Half I: strong and optimum regulate of Linear Systems.- creation to manage of Aerial Vehicles.- Command monitoring and Servomechanism Design.0 optimum keep watch over and Linear Quadratic Regulator (LQR).- H-infinity optimum Control.- balance Margins and Frequency area Consideration.- Projective Control.- Linear Quadratic Gaussian with Loop-transfer restoration (LQG/LTR) Control.- Simulation Examplse and Case Studies.- Exercises.- half II: version Reference Adaptive Control.- Motivation.- Lyapunov balance idea: creation and Overview.- Adaptive keep watch over Architectures: Direct vs. Indirect.- MRAC and version Matching Conditions.- Adaptive Dynamic Inversion.- Persistency of Excitation.- implementing Robustness in MRAC Systems.- Approximation-based MRAC.- Adaptive Augmentation of a Linear Baseline Controller.- Simulation Examples and Case Studies.- Exercises.- half III: MRAC layout Extensions.- Limited-authority Adaptive Control.- Predictor-based Control.- Combined/Composite MRAC.- Filtered MRAC Design.- MRAC layout utilizing Output Feedback.- Simulation Examples and Case Studies.- Conclusions, destiny paintings and Flight keep watch over demanding situations

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1. It is often desirable when simulating the dynamics to compute and examine the _ If we differentiate u, we get peak values of the optimal control u and its rate u. 48) _ as We can form a closed-loop simulation model, with outputs x, u, and u, x_ ¼Ax þ Bu u ¼ ÀKx x_ ¼ðA À BK Þ x ¼ Acl x 2 3 2 3 x I 6 7 6 7 y ¼4 u 5 ¼ 4 ÀK 5x u_ ÀKAcl ð2:49Þ In real-life applications, and especially in flight control, it is critical to prevent saturation of the control surface positions and rates. When this happens, nonlinear effects begin to dominate the system response, stability is no longer guaranteed, and the system could depart.

This is done through selection of the LQR penalty matrix weights Q and R. Later in Chap. 3, we will use this fact to tune the design of optimal controllers to achieve performance and robustness. 7 Conclusions In this chapter, we briefly discussed optimal control theory and the linear quadratic regulator. Many control systems today are designed using this method due to the frequency domain guarantees and the ease of the design. In the next chapter, we shall extend the regulator architecture to command tracking systems.

63), we add and subtract (s P) from both sides and rearrange the terms. 62) to both sides and note that K ¼ RÀ1 BT P and LðsÞ ¼ K ðsI À AÞÀ1 B ¼ KFB ¼ RÀ1 BT PFB . 71) where ðI þ LðsÞÞ is the return difference matrix, computed at the system input break point. The term BT FÃ QFB is a Hermitian positive semidefinite matrix. By removing this term on the right side, we form the inequality ðI þ LðsÞÞÃ RðI þ LðsÞÞ ! 72) If we assume an equal penalty on each control, that is, R ¼ rI, r>0, then, 44 2 Optimal Control and the Linear Quadratic Regulator Multi-Input Systems Single Input Systems σ ( I+L) Im -1 L ( jω) Re ω 0 db Nyquist Loci Never Enters Unit Disk Centered at (–1,j0) Min Singular Value of Return Difference Is Greater Than One Fig.

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