Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework

• Published in: IEEE transaction on neural networks and learning systems 2004-05, Vol.15 (3), p.663-673
• Main Authors: Lingji Chen, Narendra, K.S.
• Format: Article
• Language: English
• Subjects: Asymptotic properties; Control systems; Control theory; Delay; Dynamical systems; Feedback; Jacobian matrices; Linear feedback control systems; Linear Models; Neural networks; Neurofeedback; Nonlinear control systems; Nonlinear Dynamics; Nonlinear systems; Nonlinearity; Output feedback; State feedback; Studies; Time Factors
• ISSN: 1045-9227; 2162-237X; 1941-0093; 2162-2388
• DOI: 10.1109/TNN.2004.826206

This paper presents a unified theoretical framework for the identification and control of a nonlinear discrete-time dynamical system, in which the nonlinear system is represented explicitly as a sum of its linearized component and the residual nonlinear component referred to as a "higher order function." This representation substantially simplifies the procedure of applying the implicit function theorem to derive local properties of the nonlinear system, and reveals the role played by the linearized system in a more transparent form. Under the assumption that the linearized system is controllable and observable, it is shown that: 1) the nonlinear system is also controllable and observable in a local domain; 2) a feedback law exists to stabilize the nonlinear system locally; and 3) the nonlinear system can exactly track a constant or a periodic sequence locally, if its linearized system can do so. With some additional assumptions, the nonlinear system is shown to have a well-defined relative degree (delay) and zero-dynamics. If the zero-dynamics of the linearized system is asymptotically stable, so is that of the nonlinear one, and in such a case, a control law exists for the nonlinear system to asymptotically track an arbitrary reference signal exactly, in a neighborhood of the equilibrium state. The tracking can be achieved by using the state vector for feedback, or by using only the input and the output, in which case the nonlinear autoregressive moving-average (NARMA) model is established and utilized. These results are important for understanding the use of neural networks as identifiers and controllers for general nonlinear discrete-time dynamical systems.