Nonlinear Robust Predictive Mean Control of Bounded Dynamic Stochastic Systems  

Oleh:

Wang, Yongji ; Zhao, Hongfang ; Hong Wang

Jenis: Article from Article
Dalam koleksi: Final Program and Book of Abstracts: The 4th Asian Control Conference, September 25-27, 2002 (Sep. 2002), page 1461-1466.
Topik: Nonlinear Robust; Predictive Mean Control; Bounded Dynamic; Stochastic Systems
Fulltext: AC021306.PDF (143.56KB)

Isi artikel

Following the recently developed algorithms for the modeling and control of bounded dynamic stochastic systems, this short paper proposes a nonlinear one-step-ahead predictive mean control with guaranteed stability for bounded dynamic stochastic systems. The B-spline neural network based square root model is used to represent the output probability density function; the mean control of output distribution is then addressed to a nonlinear optimization problem. To solve this problem, the L-M modification for gradient search approach is adopted, and a Lyapunov based stability analysis is carried out, and a sufficient condition for the closed loop asymptotic stability is derived. When the system is subjected to either random noise or modeling error, the robustness analyses show that the closed mean control loop system is still local asymptotic stable and the tracking errors are bounded. Simulation examples are used to demonstrate the use of the algorithm and the good results have been obtained.

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