## Control System Design: An Introduction to State-Space MethodsAddressed not only to students but also to professional engineers and scientists, this volume introduces state-space methods for direct applications to control system design, in addition to providing background for reading the periodical literature. Its presentation, therefore, is suitable both for those who require methods for achieving results and those more interested in using results than in proving them. Topics include feedback control; state-space representation of dynamic systems and dynamics of linear systems; frequency-domain analysis; controllability and observability; and shaping the dynamic response. Additional subjects encompass linear observers; compensator design by the separation principle; linear, quadratic optimum control; random processes; and Kalman filters. Concrete examples of how state-space methods can be used to advantage in several representative applications are woven into the fabric of the text and the homework problems. Many of the models are drawn from aerospace and inertial instrumentation; other examples are derived from chemical process control, maritime operations, robotics, and energy systems. |

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Control System Design: An Introduction to State-Space Methods Bernard Friedland Limited preview - 2012 |

Control System Design: An Introduction to State-Space Methods Bernard Friedland Limited preview - 2005 |

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acceleration aircraft algorithm angular velocity asymptotically stable autopilot behavior block diagram Chap characteristic equation characteristic polynomial closed-loop poles closed-loop system coefficients companion form compensator constant control gains control input control law control system design control theory corresponding covariance matrix defined density matrix determined differential equations distillation column Doyle-Stein dynamics matrix eigenvalues estimate Example exogenous Figure frequency frequency-domain full-state feedback gain margin gain matrix given gyro hence imaginary axis inertial integral inverse inverted pendulum Kalman filter Kalman filter gains Laplace transform linear system loop matrix G measured missile nonsingular Note obtained output parameters plot position Prob problem random process reduced-order observer result return difference Riccati equation right half-plane root locus scalar sensor shown in Fig solution state-space methods subsystem temperature time-invariant system transfer function transition matrix unstable variables vector white noise Wiener process zero