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In this paper, we propose a method for designing continuous gain-scheduled worst-case controller for a class of stochastic nonlinear systems under actuator saturation and unknown information. The stochastic nonlinear system under study is governed by a finite-state Markov process, but with partially known jump rate from one mode to another. Initially, a gradient linearization procedure is applied to describe such nonlinear systems by several model-based linear systems. Next, by investigating a convex hull set, the actuator saturation is transferred into several linear controllers. Moreover, worst-case controllers are established for each linear model in terms of linear matrix inequalities. Finally, a continuous gain-scheduled approach is employed to design continuous nonlinear controllers for the whole nonlinear jump system. A numerical example is given to illustrate the effectiveness of the developed techniques. Refereed/Peer-reviewed

In this paper, an observer-based finite-time continuous gain-scheduled control is designed for a class of stochastic bioreactor systems with partially known jump rates. By using gradient linearization approach, the nonlinear stochastic systems are described by a series of linear jump models at some selected working points, then based on stochastic Lyapunov-Krasovskii functional approach, a new robust stochastic finite-time stabilizable criterion is derived to ensure robust finite-time stabilization of the each jump linear system by means of linear matrix inequalities. This method is then extended to provide observer-based finite-time state feedback H∞ controllers for such linear jump systems. Lastly, continuous gain-scheduled approach is employed to design observer-based continuous H∞ controllers for the whole bioreactor jump systems. Simulation examples show the effectiveness and potential of the developed techniques. Refereed/Peer-reviewed