Sliding Modes in Control and Optimization pp 131-144 | Cite as
Dynamic Optimization
Chapter
Abstract
Optimization of linear systems of the type (7.1) usually relies upon minimization of the quadratic criterion where Q is a positive semi-definite symmetric matrix, and R is a positive definite symmetric matrix. If the controlled variable is of the form y = Dx where y∈ℝk, k<n, and D is constant kxn matrix, then taking one obtains that the first term in the criterion (9.1) is yTy and characterizes the degree of deviation of the controlled variable from the zero state. The second term in the criterion defines the penalty for controll “expenses”. The relation between the weight matrices Q and R defines the tradeoff between the two contradictory desires such as to have a rapidly decaying control process and to reduce power consumption for its realization.
$$I = \mathop \smallint \limits_0^\infty ({x^T}Q + {u^T}Ru)dt,$$
(9.1)
$$Q = {D^T}D\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{ \geqslant } 0$$
(9.2)
Keywords
Asymptotic Stability Dynamic Optimization Bellman Equation Discontinuity Surface Positive Definite Symmetric Matrix
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© Springer-Verlag Berlin, Heidelberg 1992