Regulator Design Method Research Articles

Facts control design based on power system nonparametric models

This paper presents two applications of the sampled regulator design method (SRDM) to flexible AC transmission systems (FACTS) control, using unified power flow controller (UPFC) DC capacitor voltage control and static Var compensator (SVC) AC voltage control, based on nonparametric models of the power system. The SRDM was developed in the 1980s for the design of a class of sampled regulators for uncertain, nonlinear systems and can guarantee control system stability, optimality and robustness. The design only requires knowledge of the open-loop system step response – a nonparametric model – which is particularly attractive for applications in power systems, where field tests to obtain nonparametric models are usually much easier to apply than the development of parametric models. In this paper, a comprehensive demonstration of the SRDM for UPFC DC capacitor voltage control and SVC AC voltage control in example power systems is given.

An Adaptive Two Degree of Freedom Controller

A two-degree of freedom generic optimal controller structure and regulator design method is shown, which can be easily optimized in H2 - and H∞-norm space. It is shown how the regulator and some additional filters - depending on the reference signal, the output disturbance and the invariant process factors - should be selected by the solutions of Diophantine equations if optimality is important. It is proved that the optimal regulator of the generic scheme optimized in H2-norm space is the theoretical regulator providing minimal controller variance to the process and noise model variations. Based on the above special structure an adaptive dual control strategy is shown, designing the optimal filters for the optimal servo and regulatory paradigms of the controller.

Robustness and Reachable Bandwidth Versus Control and Identification Performances

This paper demonstrates the necessity of iterative controller refinement methods in control engineering practice. A generic optimal controller structure and regulator design method, where the identification and control errors are identical, are used in the investigation. It is shown that this iterative scheme maximizes a robustness measure, too. A new uncertainty relationship is introduced limiting the product of modeling accuracy and robustness measures.

Limiting Robustness Properties in an Iterative Identification and Control Scheme

Abstract This paper demonstrates the necessity of iterative controller refinement methods in control engineering practice. A generic optimal controller structure and regulator design method, where the identification and control errors are identical, are used in the investigation. It is shown that this iterative scheme maximizes a robustness measure, too. A new uncertainty relationship is introduced limiting the product of modeling accuracy and robustness measures.

An Iterative Redesign Technique of Reference Models: How to Reach the Maximal Bandwidth?

Abstract A generic optimal controller structure and regulator design method for linear systems are combined to form an iterative regulator refinement scheme, where the identification and control errors are identical. It is shown how a real nonlinear actuator limiting the amplitude of the control signal influences the control performances. Then the special modification of the original iterative scheme is presented, which allows the user to find the reachable maximal bandwidth by an iterative redesigning the original not reachable requirements. The effectiveness and usability of this new method is demonstrated by simulation examples.

Convergence and Robustness Properties of a Generic Regulator Refinement Scheme

This paper investigates a generic optimal controller structure and regulator design method, where the identification and control errors are identical. Several special properties of this combined scheme are discussed. The error relationships, controller sensitivity behaviors and convergence properties are also treated. It is shown that this iterative scheme maximizes a robustness measure, too. This last property is demonstrated by a simulation example and simple corollaries for a first order disturbance rejection model.

Interplay Between Robustness versus Control and Identification Perfonnances

Abstract This paper demonstrates the necessity of iterative controller refinement methods in control engineering practice. A generic optimal controller structure and regulator design method, where the identification and control errors are identical, are used in the investigation. It is shown that this iterative scheme maximizes a robustness measure, too. A new uncertainty relationship is introduced limiting the product of modeling accuracy and robustness measures.

Extension of Dual Control: The Concept of "Triple Control"

A new generic optimal controller structure and regulator design method is combined to form an adaptive control scheme, where the identification and control errors are identical. Then a worst-case optimal input design algorithm is introduced lo increase the robustness of the closed-loop control in the relevant medium frequency range by generating a "maximum-variance" reference signal. The combination of these three loops forms a special "triple-control", which is a natural extension of the classical dual-control principle.

Combined identification and control: Another way

A new generic optimal controller structure and regulator design method are introduced, avoiding the solution of polynomial equations. The model sensitivity properties of some combined identification and control schemes are investigated. It is shown that the new structure is superior to the others. An applicable strategy for iterative control refinement based on the generic scheme is presented and illustrated by simulation examples. The adaptive version of the control refinement strategy is also demonstrated.

Combined Identification and Control: Another Way

A new generic optimal controller structure and regulator design method are introduced avoiding the solution of polynomial equations. The model sensitivity properties of some combined identification and control schemes are investigated. It is shown that a new structure is superior to the others. An applicable strategy for iterative control refinement based on the generic scheme is presented and illustrated by simulation examples. A worst-case optimal input design algorithm is also introduced to increase the robustness of the closed-loop control in the relevant medium frequency range by generating a "maximum-variance" reference signal. The adaptive version of the control refinement strategy is also shown with a special "triple-control" extension for recursive optimal input design.

A nonlinear regulator design in the presence of system uncertainties using multilayered neural network

The authors present a novel nonlinear regulator design method that integrates linear optimal control techniques and nonlinear neural network learning methods. Multilayered neural networks are used to add nonlinear effects to the linear optimal regulator (LOR). The regulator can compensate for nonlinear system uncertainties that are not considered in the LOR design and can tolerate a wider range of uncertainties than the LOR alone. The salient feature of the regulator is that the control performance is much improved by using a priori knowledge of the plant dynamics as the system equation and the corresponding LOR. Computer simulations are performed to show the applicability and the limitations of the regulator.

Application of optimal regulator design method through the inverse problem approach to the power system stabilizer design

The theory and application of the optimal control of linear systems to stabilize or improve the system stability is well known (Anderson and Moore, 1971). For an optimal linear regulator design, the arbitrariness involved in the parameters of the cost function always affects the final control law and the closed-loop system poles which decide the system dynamic characteristics. A new approach, ‘The inverse problem approach’ (Yang et al., 1984), has been developed to overcome this difficulty. The system so designed will have preassigned closed-loop poles and the resulting closed-loop system performance can be ensured. In this paper, the inverse problem approach for optimal regulator design is applied to the power system stabilizer (PSS) design for single-machine infinite-bus systems. The results show that, as far as the linearized model is concerned, systems designed by the inverse problem approach have better closed-loop performance than those designed by the classical PSS design approach and a simple way of using a lead-lag compensator plus a PI regulator to realize the optimal state feedback control law is also presented.

LQ regulator design method for system with delay based on spectral decomposition of the hamiltonian

A design method to construct an LQ regulator for a system with time-delay is proposed. It is based on a new technique for composing the solution of an infinite-dimensional Riccati equation using the spectral decomposition of the hamiltonian. This design method has the feature that the closed-loop poles can be calculated a priori from the open-loop poles and one scalar design parameter, so that we can choose the parameter to obtain an optimal control law which results in the desired degree of exponential stability of the closed-loop system.

Automation of mine ventilation and development of main fan control systems

This paper makes a general review and a systematic analysis of various practical recommendations for centralized ventilation automation and fan control in mine workings. Control hierarchy in ventilation control systems is analyzed for both local and global parameters. The ventilation regime at each point in time is described as a simultaneous dynamic process involving all systems components and based on pressure stabilization in the isolated stages of the network as well as the maintenance of toxic impurities such as methane in the outgoing jets at an admissible level. Fan regulation methods are assessed and the parameters for proper choice of the regulator design method are outlined. The inclusion of the variability of the control object parameters into overall control strategy is addressed. The model of an optimal system is constructed.

Optimal Operation of a Multireservoir During Flood Periods

The main problem in applying conventional multireservoir operation principles lies in the difficulty of precise inflow prediction. In this paper, an optical multireservoir operation principle with real-time correction for inflow prediction error is proposed. The optical regulator design method is applied in order to utilize its state feedback characteristics. Results of numerical analysis show that the peak flow of the flood can be decreased and the occurence of the peal: delayed. Moreover, real-time correction of the discharge rate decreases the unexpected water level rise, even when the inflow prediction error is significant.

Minimum interaction-minimum variance controller for multivariable systems

Rosenbrock's inverse Nyquist array method for achieving diagonal dominance in multivariable systems and Astrom's self-tuning regulator design method for getting minimum variance controller for single input-single output systems have been combined to obtain controllers for multivariable systems which will give minimum interaction as well as minimum variance in the output.

Matrix Quadratic Solutions

Matrix quadratic equation solution derivation applied in finding steady state solutions of Riccati differential equations with constant coefficients