Gain-scheduled H-infinity observer design for nonlinear stochastic systems with time-delay and actuator saturation
In this paper, we propose a method for designing continuous gain-scheduled robust H ∞ observer on a class of extended stochastic nonlinear systems subject to time delay and actuator saturation. Initially, gradient linearization procedure is applied to describe such extended nonlinear systems into several model-based linear systems. Next, a robust linear H ∞ observer is designed to such linear stochastic models. Subsequently, a convex hull set is investigated and sufficient condition is derived in terms of feedback observer to determine whether a given initial condition belongs to an ellipsoid invariant set. Finally, continuous gain-scheduled approach is employed to design continuous nonline…
Observer-based H∞ control on stochastic nonlinear systems with time-delay and actuator nonlinearity
Abstract In this paper, an observer-based H ∞ controller is designed for a class of extended Markov jump systems subject to time-delay and actuator saturation nonlinearity. Gradient linearization procedure is employed to describe such nonlinear systems by several linear Markov jump systems. Next, a mode-dependent Lyapunov function is constructed for these linear systems, and a sufficient condition is derived to make them stochastically stable, and then, a continuous gain-scheduled approach is applied to design a continuous nonlinear observer-based controller on the entire extended nonlinear jump system. A simulation example is given to illustrate the effectiveness of developed techniques.
A simplified predictive control of constrained Markov jump system with mixed uncertainties
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/475808 Open Access A simplified model predictive control algorithm is designed for discrete-time Markov jump systems with mixed uncertainties. The mixed uncertainties include model polytope uncertainty and partly unknown transition probability. The simplified algorithm involves finite steps. Firstly, in the previous steps, a simplified mode-dependent predictive controller is presented to drive the state to the neighbor area around the origin. Then the trajectory of states is driven as expected to the origin by the final-step mode-independent pre…
Predictive control of convex polyhedron LPV systems with Markov jumping parameters
The problem of receding horizon predictive control of stochastic linear parameter varying systems is discussed. First, constant coefficient matrices are obtained at each vertex in the interior of linear parameter varying system, and then, by considering semi-definite programming constraints, weight coefficients between each vertex are calculated, and the equal coefficients matrices for the time variable system are obtained. Second, in the given receding horizon, for each mode sequence of the stochastic convex polyhedron linear parameter varying systems, the optimal control input sequences are designed in order to make the states into a terminal invariant set. Outside of the receding horizon…