This paper studies the infinite horizonH∞control problem for a general class of nonlinear stochastic systems with time-delay and multiplicative noise. The exponential/asymptotic mean squareH∞control design of delayed nonlinear stochastic systems is presented by solving Hamilton-Jacobi inequalities. Two numerical examples are provided to show the effectiveness of the proposed design method.

This paper investigates the problem of static output feedback H 2 /H ∞ control with spectrum constraints for linear continuous-time stochastic systems with state dependent noises. By using the spectrum technique, the notion of D(−β, −α) stability for continuous-time stochastic systems is proposed. Subsequently, the design method of the static output feedback controller is presented by solving a set of linear matrix inequalities. Under D(−β, −α) stability, a sufficient condition for the existence of stochastic H 2 /H ∞ control is derived, which transforms the problem of H 2 /H ∞ control into a convex optimization problem. Finally, a numerical example is given to show the effectiveness of the obtained results.

In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle (ORC) processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy (QMEE) is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization (PSO) algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. The simulation results testify the effectiveness of the presented control algorithm.

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