Control Theory and Optimization Technique

In open loop control, it is assumed that the dynamical model of the system is well known, that there is little or no environmental noise and that the control signal can be applied with high precision. This approach is generally utilized when there is a target value, to achieve at a particular final time, T. The disadvantage of open-loop control is that the performance of the controller is highly susceptible to any unanticipated disturbances. In feedback control, continuous or discrete time measurements of the system output, y(t), are used to adjust the control signal in real time. At each instant, the observed process, y is compared to a tracking reference, r(t), and used to generate an error signal. Feedback therefore provides the backbone of most modern control applications. In learning control, a measurement of the system, y(t), is also used to design the optimal feedback signal; however, it is not done in real time. Instead, a large number of trial control signals are tested in advance.
  • Dynamic Programming in Continuous Time
  • Kalman Filter and Certainty Equivalence
  • Observability
  • Controllability
  • Continuous-Time Markov Decision Processes
  • Programming Average-Cost
  • Optimal Stopping Problems
  • Dynamic Programming over the Infinite Horizon
  • Markov Decision Problems
  • Dynamic Programming
  • Optimization Problems in Control Engineering
  • Automotive Control Systems and Autonomous Vehicles
  • Process Control and Automatic Control Theory
  • Control System Modeling
  • Control Theory and Application
  • Control Theory and Methodologies