Optimal Control and Estimation"An excellent introduction to optimal control and estimation theory and its relationship with LQG design. . . . invaluable as a reference for those already familiar with the subject." — Automatica./DIVThis highly regarded graduate-level text provides a comprehensive introduction to optimal control theory for stochastic systems, emphasizing application of its basic concepts to real problems. The first two chapters introduce optimal control and review the mathematics of control and estimation. Chapter 3 addresses optimal control of systems that may be nonlinear and time-varying, but whose inputs and parameters are known without error.Chapter 4 of the book presents methods for estimating the dynamic states of a system that is driven by uncertain forces and is observed with random measurement error. Chapter 5 discusses the general problem of stochastic optimal control, and the concluding chapter covers linear time-invariant systems.Robert F. Stengel is Professor of Mechanical and Aerospace Engineering at Princeton University, where he directs the Topical Program on Robotics and Intelligent Systems and the Laboratory for Control and Automation. He was a principal designer of the Project Apollo Lunar Module control system."An excellent teaching book with many examples and worked problems which would be ideal for self-study or for use in the classroom. . . . The book also has a practical orientation and would be of considerable use to people applying these techniques in practice." — Short Book Reviews, Publication of the International Statistical Institute.DIV"An excellent book which guides the reader through most of the important concepts and techniques. . . . A useful book for students (and their teachers) and for those practicing engineers who require a comprehensive reference to the subject." — Library Reviews, The Royal Aeronautical Society. |
Contents
| 1 | |
| 19 | |
OPTIMAL TRAJECTORIES | 184 |
Contents | 222 |
OPTIMAL STATE ESTIMATION | 299 |
TimeCorrelated Measurement Noise | 364 |
184 | 418 |
STOCHASTIC OPTIMAL CONTROL | 420 |
NeighboringOptimal Control | 426 |
Dual Control | 436 |
NeighboringOptimal Control | 443 |
Problems | 489 |
LINEAR MULTIVARIABLE CONTROL | 497 |
EPILOGUE | 627 |
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Common terms and phrases
adjoint algorithm angle assumed asymptotic stability Au(t Ax(t closed-loop components computed constant continuous-time system control gain control history control law control system corresponding cost function covariance estimate covariance matrix defined density matrix derived deterministic diagonal differential equation dimension discrete-time disturbance input dynamic constraint dynamic system effect eigenvalues eigenvectors elements equality constraint Euler-Lagrange equations evaluated example expected value filter gain frequency gain matrix Gaussian Hamiltonian initial conditions integral inverse iteration k₁ Kalman filter Kalman-Bucy filter linear linear-optimal linear-quadratic linear-quadratic regulator measurement error minimizing minimum neighboring-optimal noise nonlinear open-loop optimal control optimal trajectory output parameter perturbation positive definite pr(x probability density function problem propagation provides random residual Riccati equation root locus roots sampled-data scalar Section sequence singular solution specified spectral density stability stochastic optimal t₁ time-invariant Transactions on Automatic transform update variables variations vector x₁ zero ди дх