NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS
Eduardo D. Sontag · 1992
This paper briefly surveys some recent results relevant to the suitability of "neural nets" as models for dynamical systems as well as controllers for nonlinear plants. In particular, it touches upon questions of approximation, identifiability, construction of feedback laws, classification and interpolation, and computational capabilities of nets. No discussion is included of "learning" algorithms, concentrating instead on representational issues. 1. Introduction The basic paradigm for control is that of a "plant" or physical device P interconnected with a controller C. The controller uses measurements from P in order P C Figure 1: Basic Paradigm to compute signals, which are then fed back into the plant so as to attain a given regulation objective. (This description can be extended to incorporate the effect of external disturbances, the specification of desired trajectories, and so forth.) The plant P represents an existing system, and it is essential to have a mathematical model ...