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Optimal Control Riccati Equation

The Optimal Control Riccati Equation is a fundamental component in the field of optimal control theory, particularly in the context of linear quadratic regulator (LQR) problems. It is a second-order differential or algebraic equation that arises when trying to minimize a quadratic cost function, typically expressed as:

J=∫0∞(x(t)TQx(t)+u(t)TRu(t))dtJ = \int_0^\infty \left( x(t)^T Q x(t) + u(t)^T R u(t) \right) dtJ=∫0∞​(x(t)TQx(t)+u(t)TRu(t))dt

where x(t)x(t)x(t) is the state vector, u(t)u(t)u(t) is the control input vector, and QQQ and RRR are symmetric positive semi-definite matrices that weight the state and control input, respectively. The Riccati equation itself can be formulated as:

ATP+PA−PBR−1BTP+Q=0A^T P + PA - PBR^{-1}B^T P + Q = 0ATP+PA−PBR−1BTP+Q=0

Here, AAA and BBB are the system matrices that define the dynamics of the state and control input, and PPP is the solution matrix that helps define the optimal feedback control law u(t)=−R−1BTPx(t)u(t) = -R^{-1}B^T P x(t)u(t)=−R−1BTPx(t). The solution PPP must be positive semi-definite, ensuring that the cost function is minimized. This equation is crucial for determining the optimal state feedback policy in linear systems, making it a cornerstone of modern control theory

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