Linear quadratic optimal learning control (LQL)

A learning control solution to the problem of finding a finite-time optimal control history that minimizes a quadratic cost is presented. Learning ach ieves optimization without requiring detailed knowledge of the system, whic h may be affected by unknown but repetitive disturbances. The optimal solut ion is synthesized one basis function at a time, reaching optimality in a f inite number of trials. These system-dependent basis functions are special in that (1) each newly added basis function is learned without interfering with the previously optimized ones, and (2) it is extracted using data from previous learning trials. Numerical and experimental results are used to i llustrate the algorithm.