In this paper, a standard predictive control problem (SPCP) is formulated, which consists of one extended process description with a feedback uncertainty block. The most important finite horizon predictive control problems can be seen as special realizations of this SPCP. The SPCP and its solution are given in a state-space form. The objective of the controller is a nominal performance subject to...

Distillation processes reveal complicated multivariable nonlinear dynamics for which it is difficult to design a high-performance control system. This paper proposes an adaptive control scheme for a distillation column. The proposed adaptive system consists of a multivariable receding-horizon predictive controller using a transfer function model and a recursive least-squares (RLS) based estimator...

Predictive control algorithms have been worked out mainly to control linear plants. There is a great demand to apply different control ideas to nonlinear systems. Using predictive control algorithms for nonlinear systems is a promising technique. Extended horizon one-step-ahead and long-range optimal predictive control algorithms are given here for the parametric Volterra model (which includes also...

A Riccati-equation-based solution to a class of receding-horizon predictive control problems for an explicit-delay state-space model of an ARMAX system is found and the corresponding vector Chandrasekhar-type equations are derived for both filter and controller gains to improve the computational efficiency.

Predictive control based on linear models has become a mature technology in the last decade. Many successful real-time applications can be found in almost every sector of industry. Nonlinear predictive control can further increase the performance of this easy-to-understand control strategy. One of the main problems of implementing nonlinear predictive control is the computational aspect, which is...

A constrained adaptive predictive control method that uses uncertain process modelling based on orthonormal series functions is considered. Such unstructured modelling is described as a weighted sum of orthonormal functions using approximate information about the time constant of the process. The orthonormal series functions model can thus be used to derive a j-step-ahead output prediction according...

This paper deals with prediction of controlled autoregressive processes with additive white Gaussian noise and random coefficients adapted to an observation process. Our aim is twofold. We begin by extending to the standard Kalman predictor a result of Chen et al. (1989) on the optimality of the standard Kalman filter when applied to linear stochastic processes with almost surely finite random coefficients...

With a recently renewed interest in the continuous-time approach to control system design the continuous-time generalised predictive control (CGPC) is also worth considering. The main objective of this presentation is the development of an analytical perspective that results in explicit design procedures for stable control of both minimum-phase and non-minimum-phase SISO systems. The basic project...

A new variant of Model Predictive Control and Identification (MPCI) is proposed. The on-line objective is not to minimize the sum of square errors, but to maximize on-line the sum of the lower bounds on the minimum eigenvalues of the information matrices over finite horizons. In that way, inputs to the controlled process are allowed to excite the process highly enough to generate as much modelling...

In the paper, we discuss how to design a predictive controller capable of addressing a number of important issues ranging from nominal stability to the model identification/controller design interplay. Nominal stability is ensured by resorting to Constrained Receding Horizon Predictive Control. As for robust stability, the connections between the frequency weighting P-polynomial in the cost function...

This paper addresses an important issue of information of granulation and relationships between the size of information granules and the ensuing robustness aspects. The use of shadowed sets helps identify and quantify absorption properties of set-based information granules. Discussed is also a problem of determining an optimal level of information granulation arising in the presence of noisy data...

We consider a mathematical model which describes the flow of a Bingham fluid with friction. We assume a stationary flow and we model the contact with damped response and a local version of Coulomb's law of friction.The problem leads to a quasi-variational inequality for the velocity field. We establish the existence of a weak solution and, under additional assumptions, its uniqueness. The proofs are...

In this article, we present a relay control scheme based on LQR design with fast convergence. This scheme provides a practical and simple way to achieve fast convergence based on the well-known LQR design principle. The controller is a global stabiliser in the sense that for any given initial condition, we can always initialize the controller to drive the system to reach the origin. This controller...

In this paper, we consider the design of tracking controllers for linear MIMO systems described by an input-output model. The presence of known 'weak' interactions among SISO or MIMO subsystems may allow the designer to achieve objectives by using independent controllers of lower complexity than are necessary in general (control decentralization problem). Sufficient conditions for asymptotic tracking...

The paper is devoted to the investigation of reliability of large channel graphs having links with low reliabilities. Under some regularity assumptions regarding such graphs, we derive bounds and limit distributions of their capacities. The main goal of the paper is to prove a Poisson convergence of the capacity.

A system of differential equations for the control of tumor growth cellsin a cycle non-specific chemotherapy is analyzed. Spontaneously acquired drug resistance is taken into account by means of a mutation rate non-decreasingly dependent on time and the drug kill rate is supposed to depend on the growth rate of sensitive cells. For general tumor growth and drug kill rates the optimal treatment consists...

This paper is concerned with the problem of robust stabilization of linear time-varying delay systems containing saturating actuators in the presence of nonlinear parametric perturbations. Based on Razumikhin's approach to the stability of functional differential equations, we determine upper bounds on the time-varying delay such that the uncertain system under consideration is robustly globally or...

A distributed discrete-time hereditary system is considered. An unknown input is supposed to be a perturbation. First, we investigate the possibility of reconstructing this input using the information provided by an output equation. Then we treat the problem of keeping the observation as close as possible to some desired values (with the system still perturbed by the unknown input). To illustrate...

There exist criteria for reducing the order of a large state-space model based on the accuracy of the approximate solutions to the Lyapunov matrix equations and the Hankel operator. Iterative solution techniques for the Lyapunov equations with the Arnoldi method have been proposed in a number of papers. In this paper we derive error bounds for approximations to the solutions to the Lyapunov equations...

An approach to the synthesis and simulation of wide-sense stationary multivariate orthogonal random processes defined by their power spectral density matrices is presented. The approach is based on approximating the non-parametric power spectral density representation by the periodogram matrix of a multivariate orthogonal multisine random time-series. This periodogram matrix is used to construct the...