Об одной модификации рекуррентного метода наименьших квадратов

Рассматривается задача идентификации динамического объекта в предположении, что известен только уровень недостатка. Исследованы существующие алгоритмы. Разработан рекуррентный алгоритм идентификации, который имеет супремальные свойства и свойства МНК-оценок, выполнена оценка его сходимости. Преимуществом разработанного алгоритма является простота его использования в задачах контроля и управления.The problem of dynamical object identification is considered under suggestion that only noise level is known. A survey of existing algorithms is given. Recurrent identification algorithm is developed, having supremal properties of LSM-estimations and it’s convergence is analyzed. The dignity of developed algorithm is simplicity it’s application to the problems of monitoring and control

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

Abstract. This paper deals with adaptive regulation of a discrete-time linear time-invariant plant witharbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptivecontrol algorithm exploits the one-step-ahead control strategy and the gradient projection type estimationprocedure using the modified dead zone. The convergence property of the estimation algorithm is shown tobe ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously thesuboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented tosupport the theoretical results

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

Abstract. This paper deals with adaptive regulation of a discrete-time linear time-invariant plant witharbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptivecontrol algorithm exploits the one-step-ahead control strategy and the gradient projection type estimationprocedure using the modified dead zone. The convergence property of the estimation algorithm is shown tobe ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously thesuboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented tosupport the theoretical results

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

This paper deals with adaptive regulation of a discrete-time linear time-invariant plant with arbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptive control algorithm exploits the one-step-ahead control strategy and the gradient projection type estimation procedure using the modified dead zone. The convergence property of the estimation algorithm is shown to be ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously the suboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented to support the theoretical result

ADAPTIVE SUBOPTIMAL CONTROL OF INPUT CONSTRAINED PLANTS

This paper deals with adaptive regulation of a discrete-time linear time-invariant plant with arbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptive control algorithm exploits the one-step-ahead control strategy and the gradient projection type estimation procedure using the modified dead zone. The convergence property of the estimation algorithm is shown to be ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously the suboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented to support the theoretical result

The implementation of a generalised robust adaptive controller

An adaptive controller is developed, comprising a robust parameter estimator and an explicit pole assignment controller design. The controller is reformulated to have a standard PID structure. A practical implementation is facilitated on a digital microcomputer, connected to a physical process. Test results are presented for this real process subject to variable dead-time and an external disturbance. Simulation results are also presented, for a nominally non minimum-phase process subject to variable dead-time and large open-loop gain changes. Robust performance is demonstrated under all of these circumstances. Recommendations are given for the choices and considerations required in a robust practical implementation. Much research has been done in the field of adaptive control over the past few decades. However, a let needs to be learned about the robustness of adaptive control algorithms. This research investigates the implementation of a practical adaptive control algorithm, with numerous features incorporated to improve the robust performance of such a controller. Parameter estimation is performed using Recursive Least Squares (RLS), with various signal conditioning filters to reduce estimator sensitivity to noise and modelling errors. The control design is based on closed-loop pole assignment, with adaptive feed forward compensation included. Further, provision is made in both the estimation model and the feedback control structure to eliminate deterministic immeasurable disturbances, and to track deterministic set point variations. This is based on the Internal Model Principle. Measured random disturbance signals are included in the estimation model, for which "transfer function" polynomial coefficients are estimated and then used in the feed forward control d e sign. A new shift- operator, namely the 6-operator, is used in all controller and estimator formulations. This has been shown to have better numerical properties and to correspond more closely to continuous-time control, than the traditional q operator of z-domain discrete control. A practical implementation on a digital computer is investigated, applied to a real plant typical of an industrial application. Simulation results are also obtained for plant with non minimum-phase zeros and variable dead-time