Control parameter identification: MLC translates to a parameter identification[1] if the structure of the control law is given but the parameters are unknown. One example is the genetic algorithm for optimizing coefficients of a PID controller[2] or discrete-time optimal control.[3] Control design as regression problem of the first kind: MLC approximates a general nonlinear mapping from sensor signals to actuation commands, if the sensor signals and the optimal actuation command are known for every state. One example is the computation of sensor feedback from a known full state feedback.