On the outputs of linear control systems

This paper studies autonomous, single-input, single-output linear control systems on finite time intervals. The object of interest is the output operatorO, which associates to each input function and initial state vector the corresponding system output. Main result: If the system has relative degree r<∞, then for any “admissible” Banach space U of inputs, O is a bounded operator taking U×Cn onto the “Sobolev space” of complex functions f∈C(r−1)([0,T]) for which the (r−1)-order derivative f(r−1) is absolutely continuous, with f(r)∈U. This completes recent results of Jönsson and Martin [Ulf Jönsson, Clyde Martin, Approximation with the output of linear control systems, J. Math. Anal. Appl. 329 (2007) 798-821] who showed that if the system is minimal and U is either L²([0,T]) or C([0,T]), then has dense range.     

Robust stabilization control design for multivariable nonlinear uncertain systems

The paper develops a new control technique for multivariablenonlinear systems in the presence of uncertainties and externaldisturbances. The proposed design method does not require thatthe uncertainties should satisfy matching conditions; nor doesit require that the nominal system should be stable or prestabilized.The robust-control strategy is established using concepts fromvariable-structure theory and is based on Lyapunov stabilitytheory. The control possesses a quite simple structure whichis related to the given uncertainty bounds.     

Static control optimization in multivariable technological systems

The static operation of a technological process is optimized by several variables. An optimal control algorithm using the fastest descent (steepest ascent) method is described. Some test results are presented."Stroimaterialy" Ukrainian Scientific-Industrial Association. Translated from Vychislitel'naya i Prikladnaya Matematika, No. 74, pp. 68–77, 1992;     

Time-optimal control of multivariable systems near the origin

This article deals with bang-bang solutions of linear time-optimal control problems. Linear multivariable systems are considered which have one or more control components. It is shown in which way the control components act together to make the system achieve the ultimate aim (namely, the origin in the state space) as quickly as possible. The theory only applies to initial positions sufficiently near the origin. Criteria are given which give the number of switches per control component.Asymptotic dependences of the switching times and the final time on the distance of the initial position from the origin are established. The theory provides a numerical procedure to calculate the time-optimal control. These calculations are very simple. Basic to the proof of these results is a generalized implicit function theorem due to Artin (Ref. 1).     

Modelling of characteristic delays in multivariable control systems wcbsl

Pure time delays in multivariable control systems place severe restrictions on achievable feedback performance. This paper considers an approach to modelling distributed time-delay systems using discrete convolution. The basis for convolution algebra is briefly outlined and the new concepts of characteristic pattern and vector delays are introduced. A process control example is given that illustrates the concepts and shows typical results obtained using WCBSL (Windows Convolution-Based Simulation Language)     

On external estimates for reachable sets of nonlinear control systems

The paper is devoted to the problem of constructing external estimates for reachable sets of a nonlinear control system. The estimates are constructed in the form of level sets of smooth functions in the space of states satisfying differential inequalities. In the system under consideration, the linear part is found, for which the corresponding functions are assumed to be known. The method proposed for estimating trajectories of a nonlinear system is based on modifying estimates for the linear part and on applying the comparison principle.     

Minimal system inverses for linear multivariable systems

The problem of minimal inverses for linear time invariant multivariable systems is formulated and constructively solved in a state space setting. Unknown initial states as well as zero initial states are considered. The spectrum of the minimal inverse is shown to be unique and constructable from the original system without first calculating the whole inverse. This leads to a simple way of introducing the equivalence of “zeros” in state space terminology.     

Impulsive control systems with commutative vector fields

We consider variational problems with control laws given by systems of ordinary differential equations whose vector fields depend linearly on the time derivativeu=(u ¹,...,u ^m ) of the controlu=(u ¹,...,u ^m ). The presence of the derivativeu, which is motivated by recent applications in Lagrangian mechanics, causes an impulsive dynamics: at any jump of the control, one expects a jump of the state.The main assumption of this paper is the commutativity of the vector fields that multiply theu . This hypothesis allows us to associate our impulsive systems and the corresponding adjoint systems to suitable nonimpulsive control systems, to which standard techniques can be applied. In particular, we prove a maximum principle, which extends Pontryagin's maximum principle to impulsive commutative systems.     

An interdependency index for the outputs of uncertain systems

The study of mechanical systems with uncertain parameters is gaining increasing interest in the field of system analysis to provide an expedient model for the prediction of the system behavior. Making use of the Transformation Method, the uncertain parameters of the system are modeled by fuzzy numbers in contrast to random numbers used in stochastic approaches. As a result of this analysis, a quantification of the overall uncertainty of the system outputs, including a worst-case scenario, is obtained. The inputs of the resulting fuzzy-valued model are a priori uncorrelated but after the uncertainties are propagated through the model, interdependency (or interaction) between the outputs may arise. If such interdependency is neglected, a misinterpretation of the results may occur. For example, in the case of applying uncertainty analysis in the early design phase of a product to determine the relevant design-parameter space, the interdependency between the design variables may reduce significantly the available part of the design space. This paper proposes a measure of interdependency between the uncertain system outputs. The interdependency index can be derived by a postprocessing of the data gained by the analysis with the Transformation Method. Such information can be obtained by a negligible amount of extra computation time.     

Computation algorithms for disturbance rejection in multivariable systems

In this paper, one way of solving an important problem in linear time-invariant multivariable systems control for synthesizing feedback controllers to make the outputs of a physical system respond in a desirable manner to reference inputs and external disturbances is presented. The proposed algorithms can be regarded as a logical extension of the pole assignment problem in that, measurable or unmeasurable multiple disturbance acting on multivariable systems described by the 4-tuples (A,B,C,E) or the 6-tuples (A,B,C,D,E,F) can be rejected at the outputs in steady state. This is done by assignment of the `disturbance blocking zeros' at specified locations using the concept of pole assignment for computing the state feedback controllers. The performance of the algorithms is illustrated by a numerical example.     

Convergence of HLS estimation algorithms for multivariable ARX-like systems

A hierarchical least squares (HLS) algorithm is derived in details for identifying MIMO ARX-like systems based on the hierarchical identification principle. It is shown that the parameter estimation errors by the HLS algorithm consistently converge to zero for bounded noise variances by using the stochastic martingale theory. A numerical example is given.     

A posteriori estimates for eigenvalue/vector approximations

We combine abstract eigenvalue/eigenvector estimates (from our earlier work) with a saturation assumption for finite element solution of associated stationary problem to obtain a posteriori estimates of the accuracy of finite element Rayleigh–Ritz approximations. Attention will be payed to the interplay between the accuracy estimate for the finite element method and a strategy for generating an adapted mesh. The obtained results use a preconditioned residuum of Neymeyr and extend his study of eigenvalue approximations with eigenvector estimates. We also prove that this eigenvalue estimator is equivalent to the global error. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Inequalities for vector systems

Summary For a vector system in Eⁿ, we establish a class of inequalities for k-dimensional space angle (defined in this paper), and give some application thereof.     

Robust multivariable implicit self-tuning control

A robust multivariable linear quadratic implicit self-tuningalgorithm is presented. It is shown that the closed-loop outputerror is bounded for multiplicative modelling uncertainty, providedthat all the unstable zeros of the plant are captured in thenominal model. The algorithm can be applied to nonminimum-phasesystems, and does not require the explicit solution of the algebraicmatrix polynomial equation. The controller parameters are directlyestimated from two implicit prediction models which containthe plant and controller parameters in bilinear form. The stability analysis is performed by applying conic-sectortheory to a new error equation which is decomposed into twocomponents embodying a linear and non-linear operator, namelythe modelling uncertainty and the parameter estimator respectively.Simulation results are presented to demonstrate the performanceof the proposed adaptive controller.     

Taylor series method for dynamical systems with control: Convergence and error estimates

To optimize a complicated function constructed from a solution of a system of ordinary differential equations (ODEs), it is very important to be able to approximate a solution of a system of ODEs very precisely. The precision delivered by the standard Runge-Kutta methods often is insufficient, resulting in a “noisy function” to optimize. We consider an initial-value problem for a system of ordinary differential equations having polynomial right-hand sides with respect to all dependent variables. First we show how to reduce a wide class of ODEs to such polynomial systems. Using the estimates for the Taylor series method, we construct a new “aggregative” Taylor series method and derive guaranteed a priori step-size and error estimates for Runge-Kutta methods of order r. Then we compare the 8,13-Prince-Dormand’s, Taylor series, and aggregative Taylor series methods using seven benchmark systems of equations, including van der Pol’s equations, the “brusselator,” equations of Jacobi’s elliptic functions, and linear and nonlinear stiff systems of equations. The numerical experiments show that the Taylor series method achieves the best precision, while the aggregative Taylor series method achieves the best computational time. The final section of this paper is devoted to a comparative study of the above numerical integration methods for systems of ODEs describing the optimal flight of a spacecraft from the Earth to the Moon. __________ Translated from Sovremennaya Matematika i Ee Prilozheniya (Contemporary Mathematics and Its Applications), Vol. 24, Dynamical Systems and Optimization, 2005.     

Conversion estimates for gas-solid reactions: Improved estimates for irreversible systems

Solid conversion has been of much interest to research workers and the Cumulative Gas Concentration Method (CGCM) has been developed to provide bounds for the reactant solid in irreversible systems having power-law type kinetics. This CGCM is used in conjunction with the Pseudo-Steady-State (PSS) assumption and these bounds appear to be good under certain conditions and for long times.

In this work, the PSS condition is not assumed when deriving the bounds on the cumulative gas concentration and the effect of the inclusion of the transient part is analysed. A technique, based on an iterative scheme incorporating the CGCM, is developed to provide improved estimates for the reactant solid.     

The spectral factorization problem for multivariable distributed parameter systems

This paper studies the solution of the spectral factorization problem for multivariable distributed parameter systems with an impulse response having an infinite number of delayed impulses. A coercivity criterion for the existence of an invertible spectral factor is given for the cases that the delays are a) arbitrary (not necessarily commensurate) and b) equally spaced (commensurate); for the latter case the criterion is applied to a system consisting of two parallel transmission lines without distortion. In all cases, it is essentially shown that, under the given criterion, the spectral density matrix has a spectral factor whenever this is true for its singular atomic part, i.e. its series of delayed impulses (with almost periodic symbol). Finally, a small-gain type sufficient condition is studied for the existence of spectral factors with arbitrary delays. The latter condition is meaningful from the system theoretic point of view, since it guarantees feedback stability robustness with respect to small delays in the feedback loop. Moreover its proof contains constructive elements.