System Identification I

This paper presents a comparative investigation of the performance characteristics of a directly connected photovoltaic (PV) pumping system and a scheme utilizing a constant voltage maximum power point tracking algorithm. A simple and accurate model is developed for each individual component of the system based on its measured characteristics and the system is simulated numerically. System performance is analyzed and energy utilization efficiency is calculated for different weather conditions. A detailed comparison identifying the advantages and drawbacks of each technique is presented. Experimental results obtained using a 1080-Wp PV array connected to a 1-kW permanent magnet dc motor-centrifugal pump set show very good agreement with the numerical simulation of the systems.

Comparison of Directly Connected and Constant Voltage Controlled Photovoltaic Pumping Systems

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Introduction to Dynamics and Control of Flexible Structures

In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control.

Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

Fuzzy logic control to be conventional method

Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments

Nonlinear modeling and identification of a spark ignition engine torque

The control of water supply systems is becoming more important, since there are increasing requirements to improve operation. A need exists to model and simulate water supply systems so that their behavior can be fully understood and the total process optimized. This paper describes the simulation and control of a water supply system consisting of a sequence of pumping stations that deliver water through pipelines to intermediate storage reservoirs. The system is represented by dominant system variables that represent active and passive dynamical elements. The hydraulic models include the nonlinear coupling between flow rates and reservoir heads. The bisection numerical solution approach is used to obtain a roughness dependent friction coefficient. The whole system is simulated and the results are presented and compared with the real-time measured data. A water level controller using the robust polynomial H ″ optimization method by manipulating pump speed is obtained. The stochastic nature of the disturbance and loads is considered for controller design. The parametrized dynamic weighting functions of the design theory are selected to achieve the required control functions and robustness.

Operation and control of a water supply system.

The paper presents nonlinear modeling and on-line identification of a two mass mechanical system driven by a DC motor together with real-time experiments made. The paper also describes how to obtain a simpler identification routine for the nonlinear systems. Linear and nonlinear models for the system are obtained for identification purposes and the major nonlinearities in the system such as the Coulomb friction and the dead-zone are investigated and integrated in the nonlinear model. Hammerstein nonlinear system approach is used for the identification of the nonlinear system model. On-line identification of the linear and nonlinear system models is performed using the Recursive Least Squares (RLS) method. Results of the real-time experiments are graphically and numerically presented, and advantages of the nonlinear identification approach are definitely revealed.

Experimental nonlinear identification of a two mass system

Identification of systems operating in closed loop has been of primary concern in many industrial applications and scientific research for the last few decades. Control engineering has been one of the major fields of application of system identification in closed loop. DC motors are widely used in control systems as actuating elements or parts of the plant to be controlled. Major nonlinearities affecting the behavior of a motor and its load take effect especially at low speeds and during reversal of direction. The purpose of the current contribution is to present a nonlinear direct approach to the identification of a DC motor with load in closed loop, and show that the suggested nonlinear direct approach significantly reduces the error caused by the nonlinear effects at certain time intervals. The nonlinear model was built utilizing a Hammerstein system structure. The linear approach and the suggested nonlinear approach are tested via real time experiments and results are graphically presented. The accuracy of the suggested method is also revealed by graphics and tabulated data.

Tolgay Kara

Papers

Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments

Nonlinear modeling and identification of a spark ignition engine torque

Operation and control of a water supply system.

Experimental nonlinear identification of a two mass system

Nonlinear closed-loop direct identification of a DC motor with load for low speed two-directional operation