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In this paper, a robust fault-tolerant control strategy for constrained multisensor linear systems, subject to sensor faults and in the presence of bounded state and output disturbances, is proposed. The scheme verifies that, for each sensors-estimator combination, suitable residual variables lie inside pre-computed sets and selects a more appropriate combination based on a chosen criterion. An active fault tolerant output feedback controller yields an MPC-based control law and, by means of the notion of a "tube" of trajectories, we ensure robust closed-loop exponential stability and good performance in the fault-free case and under the occurrence of abrupt sensor faults.
2
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Self-tuning generalized predictive control with input constraints

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EN
The handling of various input constraints in the self-tuning generalized predictive control (STGPC) problem of ARIMAXARMAX systems is considered. The methods based on the Lagrange multipliers and Lemke's algorithm are used to solve the constrained optimization problem. A self-tuning controller is implemented in an indirect way, and the considered constraints imposed on the control input signal are of the rate, amplitude and energy types. A comparative simulation study of self-tuning control system behaviour is given with respect to the design parameters and constraints. The stability of a closed-loop control system is analyzed and the computational loads of both the methods are compared.
3
88%
EN
Linear stationary dynamical systems with multiple constant delays in the state are studied. Their relative and approximate controllability properties with constrained controls are discussed. Definitions of various types of controllability with constrained controls for systems with delays in the state are introduced. Some theorems concerning the relative and the approximate relative controllability with constrained controls for dynamical systems with delays in the state are established. Various types of constraints are considered. Numerical examples illustrate the theoretical analysis. An example of a real technical dynamical system is given to indicate one of possible practical applications of the theoretical results.
4
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Input constraints handling in an MPC/feedback linearization scheme

88%
EN
The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.
EN
The paper is concerned with time-delay linear fractional systems with multiple delays in the state. A formula for the solution of the discussed systems is presented and derived using the Laplace transform. Definitions of relative controllability with and without constraints for linear fractional systems with delays in the state are formulated. Relative controllability, both with and without constraints imposed on control values, is discussed. Various types of necessary and sufficient conditions for relative controllability and relative U -controllability are established and proved. Numerical examples illustrate the obtained theoretical results.
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