Stability and stabilization of stochastic systems with multiplicative noise

In this paper, stability and stabilization of linear stochastic time-invariant systems are studied based on spectrum technique. Firstly, the relationship among mean square exponential stability, asymptotical mean square stability, second-order moment exponential stability and the spectral location of the systems is revealed with the help of a spectrum operator L _A,C . Then, we focus on almost sure exponential stability and stochastic stabilization. A criterion on almost sure exponential stability based on spectrum technique is obtained. Sufficient conditions for mean square exponentially stability and asymptotic mean square stability are given via linear matrix inequality approach and some numerical examples to illustrate the effectiveness of our results are presented.     

Improving the state estimation for optimal control of stochastic processes subject to multiplicative noise

Computational models for the neural control of movement must take into account the properties of sensorimotor systems, including the signal-dependent intensity of the noise and the transmission delay affecting the signal conduction. For this purpose, this paper presents an algorithm for model-based control and estimation of a class of linear stochastic systems subject to multiplicative noise affecting the control and feedback signals. The state estimator based on Kalman filtering is allowed to take into account the current feedback to compute the current state estimate. The optimal feedback control process is adapted accordingly. The resulting estimation error is smaller than the estimation error obtained when the current state must be predicted based on the last feedback signal, which reduces variability of the simulated trajectories. In particular, the performance of the present algorithm is good in a range of feedback delay that is compatible with the delay induced by the neural transmission of the sensory inflow.     

Periodic and event-based impulse control for linear stochastic systems with multiplicative noise

This paper studies the performance comparison of periodic and event-based sampling for a class of linear stochastic systems with multiplicative noise, where the impulse control is adopted. By solving boundary value problems, we obtain the analytic expressions of the mean sampling time and the average state variance under the event-based sampling. It is shown that the event-based impulse control has substantially smaller average state variance than the periodic control under the same sampling frequency. Particularly, for the integrator case, the performance ratio of the two sampling methods is given explicitly. By simulation, it is demonstrated that the advantage of event-based sampling over periodic sampling is most obvious for unstable systems, followed by critical stable systems, and least obvious for stable systems.     

Preview control for a class of linear stochastic systems with multiplicative noise

ABSTRACT

In this paper, the preview control problem for a class of linear continuous time stochastic systems with multiplicative noise is studied based on the augmented error system method. First, a deterministic assistant system is introduced, and the original system is translated to the assistant system. Then, the integrator is employed to ensure the output of the closed-loop system tracking the reference signal accurately. Second, the augmented error system, which includes integrator vector, control vector and reference signal, is constructed based on the system after translation. As a result, the tracking problem is transformed into the optimal control problem of the augmented error system, and the optimal control input is obtained by the dynamic programming method. This control input is regarded as the preview controller of the original system. For a linear stochastic system with multiplicative noise, the difficulty being unable to construct an augmented error system by the derivation method is solved in this paper. And, the existence and uniqueness solution of the Riccati equation corresponding to the stochastic augmented error system is discussed. The numerical simulations show that the preview controller designed in this paper is very effective.     

Root-mean-square filtering of the state of polynomial stochastic systems with multiplicative noise

Some results obtained by the present author in the field of designing the finitedimensional root-mean-square filters for stochastic systems with polynomial equations of state and multiplicative noise from the linear observations were overviewed. A procedure to derive the finite-dimensional system of approximate filtering equations for a polynomial arbitrary-order equation of state was presented. The closed system of filtering equations for the root-mean-square estimate and covariance matrix error was deduced explicitly for special cases of linear and quadratic coefficients of drift and diffusion in the equation of state. For linear stochastic systems with unknown parameters, the problem of joint root-mean-square state filtering and identification of the parameters from linear observations was considered in the Appendix.     

Polynomial filtering of discrete-time stochastic linear systemswith multiplicative state noise

The problem of finding an optimal polynomial state estimate for the class of stochastic linear models with a multiplicative state noise term is studied. For such models, a technique of state augmentation is used, leading to the definition of a general polynomial filter. The theory is developed for time-varying systems with nonstationary and non-Gaussian noises. Moreover, the steady-state polynomial filter for stationary systems is also studied. Numerical simulations show the high performances of the proposed method with respect to the classical linear filtering techniques     

State estimation for multi‐channel stochastic singular systems with multiplicative noise

The state estimation problem for multi‐channel singular systems with multiplicative noise is considered based on singular value decomposition. First, two equivalent reduced order subsystems are obtained via the decomposition. Then, in order to solve the estimation problem, the subsystems are rewritten into a new form. It is noted that the measurement noise here becomes colored noise, which contains the dynamic noise, measurement noise, and multiplicative noise of the original system. In this situation, existing filtering methods cannot be directly applied, so a modified filtering method is given. The recursive algorithm for the state estimation is obtained by the filtering method. In addition, the estimation of dynamic noise is derived via the algorithm. A simulation example is given to show the effectiveness of the proposed algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

New observer‐based controller design for LPV stochastic systems with multiplicative noise

This paper addresses an observer‐based control problem of Linear Parameter Varying (LPV) stochastic systems. Based on the modeling approaches, the LPV stochastic systems can be represented by a set of linear systems with multiplicative noise term. To solve the observer‐based control problem, a less conservative stability criterion is developed via the chosen Parameter‐Dependent Lyapunov Function (PDLF). In the PDLF, none element in the positive definite matrix is required as zero. Besides, an Extended Projection Lemma is proposed to convert the derived sufficient conditions into Linear Matrix Inequality (LMI) form. According to the derived LMI conditions, all feasible solutions can be found by convex optimization algorithm at a single step. Based on those feasible solutions, an observer‐based Gain‐Scheduled (GS) controller can be established to guarantee the asymptotical stability of the closed‐loop system in the sense of mean square. Finally, two numerical examples are provided to demonstrate the effectiveness and applicability of the proposed method.     

A necessary and sufficient stabilization condition for discrete time-varying stochastic systems with multiplicative noise

This paper investigates a fundamental problem of stabilization for time-varying multiplicative noise stochastic systems. A necessary and sufficient stabilization condition is presented based on the receding horizon approach. The explicit time-varying controller is designed if the condition is satisfied. The presented results are new to the best of our knowledge.     

Parametrization method for calculating exact stability bounds of stochastic linear systems with multiplicative noise

For deterministic time-invariant linear systems, stability results are quite simple. For stochastic systems, however, even for linear ones, they are rather complicated. In this paper, some results on second mean stability (mean square stability) of time-invariant linear systems with multiplicative noise are summarized and the parametrization method of getting an exact bound for pth mean stability (p ≥ 2) via second mean stability is stated. Moreover, relations between pth mean stabilities for various values of p are given. On the basis of these relations, a simpler method for getting sufficient conditions for pth mean stability is also given, though the resulting sufficient bound is, of course, more conservative. Comparative studies of various conditions are made by using examples.     

Stability of optimum linear estimators of stochastic signals in white multiplicative noise

Conditions for uniform asymptotic stability in the large of the optimal minimum mean-square error linear filter are developed for linear systems whose observations are corrupted by white multiplicative noise in addition to white additive noise. Both discrete-time as well as continuous-time systems are considered. The multiplicative noise model may be useful in problems associated with phenomena such as fading, or reflection of the transmitted signal from the ionosphere, and also certain situations involving sampling, gating, or amplitude modulation. Conditions for existence, uniqueness, and stability of the steady-state optimal filter are also considered for time-invariant systems.     

Generalized invariant-unbiased masking and estimation of informational processes with multistructural noise

We develop a method of masking informational processes and estimating their various numerical characteristics after measurements that contain multistructural piecewise continuous noise with unknown structure switching moments.     

Control of multiplicative noise stochastic gene regulation systems by the attractive ellipsoid technique

This article describes the application of the so-called attractive ellipsoidal method to solve the trajectory stabilization problem for a class of genetic network systems modelled by a stochastic model. The genetic network model is described by a stochastic quasi-linear system affected by additive and multiplicative noises simultaneously. The solution of the control design provided in this study is based on a linear feedback structure. In this paper the algorithm to construct a suboptimal gain for adjusting the control design is introduced. The attractive ellipsoidal method is the key stone for designing the so-called suboptimal gain. Moreover, the practical stability of the genetic network trajectories is demonstrated on the mean and in almost sure senses. Some numerical simulations show how a set of stochastic trajectories are stabilized by the controller suggested in this study and how the predicted ellipsoid region is achieved by these trajectories.     

Markovian jump delayed Hopfield networks with multiplicative noise

A non-symmetric version of Hopfield networks subject to state-multiplicative noise, pure time delay and Markov jumps is considered. Such networks arise in the context of visuo-motor control loops and may, therefore, be used to mimic their complex behavior. In this paper, we adopt the Lur’e-Postnikov systems approach to analyze the stochastic stability and the L₂ gain of generalized Hopfield networks including these effects.     

A smoothing algorithm for systems with multiplicative noise

A fixed-point smoothing algorithm is derived for linear time-varying systems with multiplicative noise in the state channels of the plant and the measurements as well as additive noise. As in systems without multiplicative noise, the smoothed estimates depend on the filtered estimates. The steady-state behavior of the algorithm is examined. If the multiplicative part is absent, then the results coincide with known results for systems with additive noise     

Reliable dissipative control for uncertain time‐delayed stochastic systems with Markovian jump switching and multiplicative noise

This paper considers the robust reliable dissipative control problem for a class of hybrid systems, which includes stochastics, Markovian jumping, state time delay, parameter uncertainty, possible actuator failure, multiplicative noises and impulsive effects. We propose a linear feedback memoryless controller and impulsive controller such that the hybrid system is stochastically stable and strictly (Q, S, R) dissipative, which include H_∞ performance as a special case, for all the admissible uncertainties and actuator failures occurring among a prescribed subset of actuators. Based on Itô's differential formula and Lyapunov stability theory, sufficient conditions are obtained in terms of linear matrix inequalities. A numerical example is constructed to show the effectiveness of the controller designed in this paper. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

On state-constrained porous-media systems with gradient-type multiplicative noise

The aim of the present paper is to provide necessary and sufficient conditions to maintain a stochastic coupled system with porous media components and gradient-type noise in a prescribed set of constraints by using internal controls. This work is a complementary contribution to the results obtained by the same authors, also on the viability problem associated to the porous media equation, but with Lipschitz noise. Second, the present paper provides a different framework in which the quasi-tangency condition can be obtained with optimal speed. In comparison with the aforementioned result, and from a technical point of view, here, we transform the stochastic system into a random-PDE one, via the rescaling approach, and then we study the viability of random sets. As an application, (stronger) conditions for the stabilization of the stochastic porous media equations are obtained. These are illustrated on a simple example.     

Robust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise

Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean‐square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three‐gene network to illustrate the applicability and usefulness of the design. Copyright © 2010 John Wiley & Sons, Ltd.     

The linear quadratic optimization problems for a class of linear stochastic systems with multiplicative white noise and Markovian jumping

In this paper, the linear quadratic optimization problem for a class of linear stochastic systems subject both to multiplicative white noise and Markovian jumping is investigated. Two classes of admissible controls are considered. One of these classes contains controls with additional property that corresponding trajectories tend to zero (in mean square) when tends to /spl infin/, while concerning the controls contained in the second class of admissible controls there is not any stability assumption. In the optimization problem over the first class of admissible controls, the cost functional could have indefinite sign of weights matrices. An iterative procedure to compute the maximal solution of the systems of generalized Riccati equations is provided. A numerical example to illustrate the applicability of the iterative procedure is given.