Earticle

There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

We propose the robust nonlinear controller design methodology for the multivariable system which has hard nonlinearities (Coulomb friction, dead-zone, etc) and the structured real parameter uncertainty. The hard nonlinearity can be linearized by the RIDF technique and structured real parameter uncertainty can be modelled as the sense of Peterson-Hollot's quadratic Lyapunov bound. For this system, we apply the robust QLQG/H$_{\infty}$ control and then can obtain four Riccati equations. Because of the system's nonlinearity, however, one Riccati equation contains the nonlinear correction term that is very difficult to solve numerically, In order to treat this problem, using some transformations to Riccati equations, the nonlinear correction term can be eliminated. Then, only two Riccati equations need to design a controller. Finally, the robust nonlinear controller is synthesized via IRIDF techniques. To test this proposed control method, we consider the direct-drive robot manipulator system that has Coulomb frictions and varying inertia.

A kinematic nonlinear multivariable launcher is modeled of which the azimuth and elevation axes are drived simultaneously and position control systems are designed for this system by the PD and LQG/LTR control methods. Also, the suitable command input fonction is suggested for the desired command following performance and the two control systems with disturbances and load variation are evaluated for the entire operating range by computer simulation. It is found that the two linear controllers can be used for the kinematic nonlinear multivariable launcher in the entire operating range and LQG/LTR controller is more effective for disturbance rejection.

In the control theory, a system has one or more input and output signals. With the development of control theory, a modern control system is carried out in the state space, and can process multi-input and multi-output (MIMO) systems. According to the complexity of a control system, a control system may have been described by a high order differential equation or transfer function, thus control system analysis and design becomes more difficult. Therefore a study to obtain a low order model with the dynamic characteristics similar to a high order system is very meaningful. In this paper, an approximation algorithm for multivariable system based on the generalized block pulse transform is presented. To solve the functional differential equation and transfer function, the generalized block pulse function is used. The algorithm adopted in this paper has shown that computational results are more accurate and convenient rather than conventional block pulse transform.

Nonlinear adaptive control for the laboratory pressure-flow model. Control valves are installed on both sides. The pressure and the outlet flow rate are measured. The pressure and outlet flow rate are controlled variables and the control valve stem positions on both sides are the manipulated variables. The variation in both inputs will influence both controlled variables. The control performance is good, in spite of varying valve coefficients of inlet and outlet.

본 논문에서는 다변수 퍼지 제어 시스템 이용한 선박의 자동 항해 시스템을 제안한다. 제안된 다변수 퍼지 제어 시스템은 세 개의 인력과 두 개의 출력을 가지는 서브시스템으로 구성되어지며, 제안된 시스템의 효과성을 증명하기 시뮬레이션을 통해 동적인 환경에서도 스스로 장애물을 인식하고 회피할 수 있음을 보였다.

In this paper, we propose an automatic navigation system of ship using multivariable fuzzy control system in dynamic sea environment. The proposed multivariable fuzzy control system consists of two subsystems with three inputs and two outputs. The effectiveness of the proposed multivariable fuzzy control system is shown by simulation results.

This paper presents improved empirical representations of a general class of open-loop unstable systems using closed-loop system identification. A multi-axis magnetic-levitation (maglev) nanopositioning system with an extended translational travel range is used as a test model to verify the closed-loop system-identification method presented in this paper. A closed-loop identification technique employing a known controller structure is used for model identification and validation. Direct and coupling transfer functions (TFs) are then derived from the experimental input-output time sequences and the knowledge of controller dynamics. A persistently excited signal with a bandwidth in the frequency range of interest is used as a reference input. An order-reduction algorithm is developed to obtain TFs with predefined orders, which gives the closest match in the frequency range of interest without missing any significant plant dynamics. The entire analysis is performed in the discrete-time domain in order to avoid any errors due to continuous-to-discrete-time conversion and vice versa. Continuous-time TFs are used only for order-reduction and performance analysis of the identified TFs. Experimental results are presented in the time as well as frequency domains to verify the accuracy of the identified plant TFs. These results also demonstrate the effectiveness of the developed closed-loop identification method in meeting all of the three core objectives-(i) reduction in cross-axial coupling from 9.213 ${\mu}m$ to 0.911 ${\mu}m$ in translation and from 22.03 ${\mu}rad$ to 1.353 ${\mu}rad$ in rotation, (ii) large range motion capability with a travel range of ${\pm}2.9$ mm, and (iii) improved robust stability.

A 160 MW drum-type boiler-turbine system is developed in the present research through a multi-multivariable dynamic matrix control (DMC) scheme and a multi-multivariable model approach. A novel intelligence-based decision mechanism (IBDM) is realized to support both model approach and control scheme. In such case, the responsibility of the proposed IBDM is to identify the best multivariable model of the system and the corresponding multivariable DMC scheme to cope with the system at each instant of time in an appropriate manner.

This paper deals with the fault detection problem in uncertain linear multivariable systems and its application. A robust fault detection method presented by Kim et a. (1998) for MIMO (Multi Input/Multi Output) systems has been adopted and applied to the twin rotor MIMO experimental setup using industrial DSP. The system identification problem is formulated for the twin rotor MIMO system and its parameters are estimated using experimental data. Based on the estimated parameters, some fault detection simulations are performed using the robust fault detection method, which shows that the preformance is satisfied.

Recently, an output feedback variable structure control scheme(OFVSCS) is proposed to remove the assumption of full state availability and to make the application of VSC scheme to the high order systems with unmeasurable state variables possible. In this paper, a design method of an output feedback variable structure control system (IOFVSCS) that guarantees the invariance of the sliding mode against process parameter variation and external disturbance is proposed. The IOFVSCS is composed of two components; dynamic switching surface driven by measured I/0 informations and switching control input generator driven by switching surface information and measured output, where the two components are constructed by adopting unknown vector modelling approach. The invariance condition for the IOFVSCS is proved to be the same as that of the conventional VSCS. Simulation results show that the IOFVSCS can be designed to have robust properties better than that of the conventional VSCS in spite that the IOFVSCS is driven by small amount of measured information.

In this paper, PID control method using genetic algorithm to control the nonlinear multivariable system is presented. Genetic algorithms are global search techniques for nonlinear optimization. For experiment, the x-y rod balancing system with driver circuit board is fabricated. Experiments such as angle and position control for system are performed. The validity and control performance of the GA-based PID controller are confirmed by experimental results.

In the single-input and single-output system, the parameter of plant is scalar polynomial, but in the multiple input and multiple output, it accompanies, being matrix polynomial, the consideration of observable conrolability index or problems of non-commutation in matrix polynomial as well as degree, and it is more complex to deal with. Therefore, it is thought that a full reserach on the single-input and single-output system is not made. This reserach propose that problems of minimum variance self-tuning regulator of multivariable system and pole placement self-tuning regulator.

선형 다변수 원자력 발전설비에 있어서 순환펌프 동력변화, 스로틀 밸브 개도등의 변화에 따라 plenum chamber물온도, core inletdhs도, 연료에 미치는 온도등의 응답이 상호간섭을 받지않고 최적제어 될 수 있는 방법을 선형 레귤레이터 기법을 이용해 고찰하였다. 그 결과 순환펌프의 동력변화, 스로틀 밸브 개도의 외란에 대해 plenum chamberanfdhs도, 노심입구 물온도, 연료 온도 변화 등이 적어 비간섭화 된 안정된 제어가 될 수 있음을 나타냈다.

A fuzzy learning method to control an unstable and multivariable system is presented in this paper, Because the multivariable system has generally a coupling effect between the inputs and outputs, it is difficult to find its modeling equation or parameters. If the system is unstable, initial condition rules are needed to make it stable because learning is nearly impossible. Therefore, this learning method uses the initial rules and introduces a cost function composed of the actual error and error-rate of each output without the modeling equation. To minimize the cost function, we experimentally got the Jacobian matrix in the operating point of the system. From the Jacobian matrix, we can find the direction of the convergence in the learning, and the optimal control rules are finally acquired when the fuzzy rules are updated by changing the portion of the errors and error rates.

A method for on-line tuning of the PID-controller parameters for a discrete-time multivariable process system is presented. And it is based on a step change in the controller set point. The system is presumed to be a linear, open loop stable and known one. The controller parameters are determined by the performance criterion and Fletcher-Powell methods.

본 논문에서는 상태궤환에 의한 다변수 시스템의 비간섭 제어기법을 2입력2출력 터어보 발전기 시스템에 적용하는 문제를 연구하였다. 시뮬레이션 결과 터어보 발전기 시스템은 정준형으로 비간섭 될 수 있었고 궤한이득에 의해 임의로 극 배치함으로써 시스템이 주파수 또는 부하변화에 관계없이 비간섭 되고 안정한 제어가 될 수 있음을 보였다.

In this paper, application of decoupling control method of multivariable system by state feedback to turbo-generating system with 2-input and 2-output is studied. The results of simulation shows tat turbo-generating system is canonically decoupled, and can be controlled against the change of load or frequency by feedback gain.

The aim of this paper is to suggest a design method of the optimal multivariable $H_{\infty}$ control system using genetic algorithm(GA). This $H_{\infty}$ control system is designed by applying GA to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by Glover-Doyle algorithm which can design $H_{\infty}$ controller in the state space. The effectiveness of this $H_{\infty}$ control system is verified by nonlinear simulation.

In this paper, we deal with design method of two-degree-of-freedom control system which desired property of robustness and tracking can be achieved simultaneously, Controller is designed by means of model matching method and H$\infty$ weighted sensitivity minimization design method. Satisfactory result of design example is obtained by simulation.