Control Methods

Autonomous systems have the capability to independently (and successfully) perform complex tasks. Consumer and governmental demands for such systems are frequently forcing engineers to push many functions normally performed by humans into machines. s a functional architecture for an intelligent autonomous controller with an interface to the process involving sensing (e.g., via conventional sensing technology, vision, touch, smell, etc.), actuation (e.g., via hydraulics, robotics, motors, etc.), and an interface to humans (e.g., a driver, pilot, crew, etc.) and other systems. The “execution level” has low-level numeric signal processing and control algorithms (e.g., PID, optimal, adaptive, or intelligent control; parameter estimators, failure detection and identification (FDI) algorithms). The “coordination level” provides for tuning, scheduling, supervision, and redesign of the execution-level algorithms, crisis management, planning and learning capabilities for the coordination of execution-level tasks, and higher-level symbolic decision making for FDI and control algorithm management. The “management level” provides for the supervision of lower-level functions and for managing the interface to the human(s) and other systems.

The main objective of optimal control is to determine control signals that will cause a process (plant) to satisfy some physical constraints and at the same time extremize (maximize or minimize) a chosen performance criterion (performance index or cost function). The formulation of optimal control problem requires a mathematical description of the process to be controlled, a specification of the performance index, and a statement of boundary conditions and the physical constraints on the states and/or controls. The main advantage of robust control techniques is to generate control laws that satisfy the two requirements. More specifically, given a specification of desired behaviour and frequency estimates of the magnitude of uncertainty, the theory evaluates the feasibility, produces a suitable control law, and provides a guaranty on the range of validity of this control law (strength). This combined approach is systematic and very general. In particular, it is directly applicable to Multiple-Input Multiple Output systems.
  • Adaptive Control
  • Robust Control
  • Optimal Control
  • Process Control
  • Stochastic Systems Control and Remote Supervisory Control
  • Manufacturing Systems Control
  • Co-Operative Control
  • Predictive, Intelligent and Servo Control
  • Cooperative, Coordinated and Decentralized Control
  • Advanced Process Control

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