Neural-sliding Control with Cmac in Variable Structure Systems

This work proposes a new method of dynamical control systems implementation using the Cerebellar Model Articulation Controller (CMAC) neural network as part of a variable structure system. CMAC is a neural network easily implemented in hardware, quite efficient in the reproduction of multivariable functions and extremely fast in its training process. These features yield CMAC applications in robust multivariable on line control in the presence of uncertainties in plant model. Variable Structure Systems (VSS) theory yields the concatenation of at least two, possibly unstable, dynamical structures, in order to realize a low cost controller that guarantees robustness with respect to nonidealities in the control system. This is done by a deliberate introduction of a special behavior named sliding mode where the state of the plant is confined to a subspace determined by the eigenvalues of the desired resultant dynamics until the origin is reached. Further, if the system model equations are written in a particular regular form and complete access to states is guaranteed for any time, the CMAC network in a variable structure system collects information enough about plant dynamics for the closed loop eigenvalues be allocated on line with no previous knowledge of plant model other than its order and structure. Furthermore, if the supposed unknown plant parameters change due to environment interaction, the resultant dynamics is invariant.