The latter can be solved numerically very efficiently using currently available software packages. A key point in this design technique is that it can be used to design controllers with different structures depending on the number of constraints that are added. In particular, it is shown that a controller with the structure of a regulator and estimator can be designed so that switching based on state estimates rather than on the output can be performed. It is also shown that many other desired features can be included in the design, such as boundedness of the control signals and avoidance of sliding modes. Furthermore, the applicability of this design method to systems with multiple equilibrium points is shown in simulation examples.
Decision making under uncertainty is a notoriously difficult problem.
Reinforcement learning algorithms try to learn a good decision policy using a combination of simulation, compact representations and function approximation. In this talk, we consider a particular class of algorithms called actor-critic algorithms. I present a formulation of the learning problem and derive the relationship between the actor and the critic that guarantees the convergence of the algorithms. The wave variable model suggests that the cerebellum mediates scheduled linear servo control of simple single-joint arm movements, while maintaining stability of the overall system in the presence of delays. A 3rd-order reduced model, which captures significant behavior of the full 20th order model, is then derived as a function of model parameters.