Predictive Control Of Stochastic Systems Research Articles

Управление с прогнозирующей моделью стохастическими системами с марковскими скачками и сериально коррелированными параметрами при ограничениях

Управление с прогнозирующей моделью стохастическими системами с марковскими скачками и сериально коррелированными параметрами при ограничениях

Output‐feedback model predictive control for stochastic systems with multiplicative and additive uncertainty

SummaryThis paper studies the output‐feedback model predictive control (MPC) design problem for linear systems with multiplicative and additive random uncertainty. We first present an off‐line optimization algorithm to optimize feedback gains of the observer and the dual‐mode control policy. After that, by defining a cuboid tube whose center and boundary are both time‐varying variables, we develop a set sequence with increased freedom to contain stochastic system trajectories. A quadratic performance function with analytic upper and lower bounds is minimized such that it decreases exponentially to a finite range under the expectation. The resulting MPC algorithms are proved to guarantee practically stochastic input‐to‐state stability. A numerical example of the wind turbine model illustrates the properties of the MPC algorithms.

Multi‐Step probabilistic sets in model predictive control for stochastic systems with multiplicative uncertainty

This study designs a model predictive controller for linear, discrete-time, stochastic systems with multiplicative noise and probabilistic constraints. The probabilistic invariance has shown its advantage in characterising the stochastic dynamics of the controlled state. Here multi-step probabilistic sets strengthen probabilistic invariance to further satisfy infinite-horizon probabilistic constraints. In addition, multi-step probabilistic sets offer some degrees of freedom to enlarge the feasible region ensured by probabilistic invariance. The controller satisfies given constraints and guarantees closed-loop mean-square stability. Moreover, a simplified controller with lower on-line computational burden is presented. Numerical examples show the performance of the proposed approach.

Correction to “Packetized Predictive Control of Stochastic Systems Over Bit-Rate Limited Channels With Packet Loss”

The authors correct the results in Section V of the above-named article (ibid., vol. 56, no. 12, pp. 2854-2868, Dec. 2011).

Packetized Predictive Control of Stochastic Systems Over Bit-Rate Limited Channels With Packet Loss

We study a control architecture for linear time-invariant plants with random disturbances and where a network is placed between the controller output and the plant input. The network imposes a constraint on the expected bit-rate and is affected by random independent and identically distributed (i.i.d.) dropouts. Dropout-rates and acknowledgments of receipt are not available at the controller side. To achieve robustness with respect to i.i.d. dropouts, the controller transmits data packets containing quantized plant input predictions. These are provided by an appropriate optimal entropy coded dithered lattice vector quantizer. Within this context, we derive stochastic stability results and provide a noise-shaping model of the closed loop system. This model is employed for performance analysis by using rate-distortion theory.

Explicit Solutions of Linear Matrix Equations

Explicit Solutions of Linear Matrix Equations