Control of nonlinear systems

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V. Nevistic, Ph.D. thesis, Constrained control of nonlinear systems, Swiss Federal Institute of Technology, 1997. 

M. Denai, F. Palis and A. Zeghbib. ANFIS based modeling and control of nonlinear systems A tutorial . Proceedings of the IEEE Conference on SMC (2004), pp. 3433-3438. 

DeHaan, D. Guay, M. Extremum seeking control of nonlinear systems with parametric uncertainties and state constraints. Proceedings of American Control Conference, Boston, MA, Jun 30 to Jul 2, 2004. 

A third model-based approach is neural predictive control, which is a neural network version of nonlinear model predictive control [Trajanoski and Wach, 1998]. In this approach, the neural network is used for ofif-Hne identification of a system model, which is then used to design a nonhnear model predictive controller. This design may provide suitable control of nonlinear systems with time-delays and thus maybe particularly useful in biomedical appHcations. Recent computer simulation studies have demonstrated positive results for control of insuhn dehvery [Trajanoski and Wach, 1998]. 

O.L. Mangasarian. Sufficient conditions for the optimal control of nonlinear systems. SIAM J. Control, 4 139-152, 1966. 

R. Luus and L. Lapidus. The control of nonlinear systems Part II Convergence by combined first and second variations. AIChE J., 13 108-113, 1967. 

Yang B (2006) Output feedback control of nonlinear systems with unstabilizable/undetectable linearization. In Systems and control engineering. PhD thesis. Case Western Reserve, Cleveland... 

Schiehlen, W. Seifried, R. 2006. Impact systems with uncertainty. In H. Hu E. Kreuzer Z. Wang (eds.), Proceedings of the lUTAM Symposium on Dynamics and Control of Nonlinear Systems with Uncertainty, Nanjing, China, September 18-22. 

Chen, F. Khalil, H.K. Adaptive Control of Nonlinear Systems using Neural Networks - A Dead-Zone Approach. Proc. Amer. Control Conf (1990), pp. 667-... 

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There has been a significant amount of work reported on controlling composition during copolymerization reactions. The Kalman filter method is based on a linear approximation of the nonlinear process [55] but has problems with stability and convergence [56-58]. For that reason, numerous nonlinear methods have been developed. Kravaris et al. [59] used temperature tracking as another nonlinear method to control copolymer composition. Model predictive control (MPC) [60-63], as well as nonlinear MPC (NLMPC) [64-67] algorithms have been suggested for control of nonlinear systems. 

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