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Model predictive control – Advanced method of process control; Optimal control – Mathematical way of attaining a desired output from a dynamic system; Process control – Discipline that uses industrial control to achieve a production level of consistency; Robust control – Approach to controller design that explicitly deals with uncertainty
Optimal control is an extension of the calculus of variations, and is a mathematical optimization method for deriving control policies. [6] The method is largely due to the work of Lev Pontryagin and Richard Bellman in the 1950s, after contributions to calculus of variations by Edward J. McShane. [7] Optimal control can be seen as a control ...
A mathematical model is an abstract description of a concrete ... physics models are often made by mathematical methods such as finite ... in control of a ...
The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. [1] System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction.
Tuning a control loop is the adjustment of its control parameters (proportional band/gain, integral gain/reset, derivative gain/rate) to the optimum values for the desired control response. Stability (no unbounded oscillation) is a basic requirement, but beyond that, different systems have different behavior, different applications have ...
H ∞ (i.e. "H-infinity") methods are used in control theory to synthesize controllers to achieve stabilization with guaranteed performance. To use H ∞ methods, a control designer expresses the control problem as a mathematical optimization problem and then finds the controller that solves this optimization.
New mathematical techniques included developments in optimal control in the 1950s and 1960s followed by progress in stochastic, robust, adaptive, nonlinear control methods in the 1970s and 1980s. Applications of control methodology have helped to make possible space travel and communication satellites, safer and more efficient aircraft, cleaner ...
Modern control theory, instead of changing domains to avoid the complexities of time-domain ODE mathematics, converts the differential equations into a system of lower-order time domain equations called state equations, which can then be manipulated using techniques from linear algebra. [2]