Fast Evaluation of Quadratic Control-Lyapunov Policy

作者：Wang Yang^*; Boyd Stephen

来源：IEEE Transactions on Control Systems Technology, 2011, 19(4): 939-946.

DOI：10.1109/TCST.2010.2056371

• Approximate dynamic programming
• model predictive control (MPC)
• optimization-based control
• real-time convex optimization
• stochastic control

The evaluation of a control-Lyapunov policy, with quadratic Lyapunov function, requires the solution of a quadratic program (QP) at each time step. For small problems this QP can be solved explicitly; for larger problems an online optimization method can be used. For this reason the control-Lyapunov control policy is considered a computationally intensive control law, as opposed to an "analytical" control law, such as conventional linear state feedback, linear quadratic Gaussian control, or H-infinity, too complex or slow to be used in high speed control applications. In this note we show that by precomputing certain quantities, the control-Lyapunov policy can be evaluated extremely efficiently. We will show that when the number of inputs is on the order of the square-root of the state dimension, the cost of evaluating a control-Lyapunov policy is on the same order as the cost of evaluating a simple linear state feedback policy, and less (in order) than the cost of updating a Kalman filter state estimate. To give an idea of the speeds involved, for a problem with 100 states and 10 inputs, the control-Lyapunov policy can be evaluated in around 67 mu s, on a 2 GHz AMD processor; the same processor requires 40 mu s to carry out a Kalman filter update.

• 出版日期2011-7