A LabVIEW-based PI controller for controlling CE 105 coupled Tank System

In this paper, use of Proportional-Integral (PI) controller to monitor and control liquid level in an interconnected CE 105 model coupled tank is investigated. To achieve a system which can instantaneously and accurately control the liquid level in a coupled tank, two different PI controllers have been tested. The LabVIEW library for the PI controller is used to measure liquid levels in the coupled tank. The PI SubVI already exists in the LabVIEW library that gives reasonable performance but to get a better system performance and monitor the liquid levels more accurately another SubVI is derived from the PI controller mathematical equations. The practical results and the system performance of the second SubVI show a faster response and more accurate instantaneous data which minimises the error in the measurements to ±1 mm. Furthermore, the robustness of the controller to change in the system’s parameters is also investigated and established

Simulation of a two degrees of freedom controller with a liquid tank

The article is dealing with the control of liquid tank system. For the process control, it is implemented two degrees of freedom controller. The system is simulated in Matlab and the results are compared with a control system with standard controller.Tento článek se zabývá řízením systému nádrže na vodu. Pro řízení nádrže je navržen regulátor se dvěma stupni volnosti. Systém nádrže je simulován v Matlabu a výsledky regulace jsou porovnány s regulací se standardním regulátorem

Universal direct tuner for loop control in industry

This paper introduces a direct universal (automatic) tuner for basic loop control in industrial applications. The direct feature refers to the fact that a first-hand model, such as a step response first-order plus dead time approximation, is not required. Instead, a point in the frequency domain and the corresponding slope of the loop frequency response is identified by single test suitable for industrial applications. The proposed method has been shown to overcome pitfalls found in other (automatic) tuning methods and has been validated in a wide range of common and exotic processes in simulation and experimental conditions. The method is very robust to noise, an important feature for real life industrial applications. Comparison is performed with other well-known methods, such as approximate M-constrained integral gain optimization (AMIGO) and Skogestad internal model controller (SIMC), which are indirect methods, i.e., they are based on a first-hand approximation of step response data. The results indicate great similarity between the results, whereas the direct method has the advantage of skipping this intermediate step of identification. The control structure is the most commonly used in industry, i.e., proportional-integral-derivative (PID) type. As the derivative action is often not used in industry due to its difficult choice, in the proposed method, we use a direct relation between the integral and derivative gains. This enables the user to have in the tuning structure the advantages of the derivative action, therefore much improving the potential of good performance in real life control applications

Identification of Nonlinear Systems Using Radial Basis Function Neural Network

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function

Automation of Aircraft Engine Fuel Controls Tests: An Industrial Case Study involving PID Control of a Nozzle Emulator

The test of fuel control systems used on civil aircraft engines is performed with a network of distributed and, by design, isolated systems. The co-ordination of these test systems is performed manually by human operators in order to verify the airworthiness of a fuel control system throughout the products’ lifecycle. The main objective of this study is the automation of an existing network of systems for fuel control tests. The aspect of automation that is considered in this paper is the control of the engine nozzle emulator which is critical to determine the airworthiness of repaired fuel control systems. This system is realized using a model following PID controller design approach. The results from simulation studies and a hardware-in-the-loop test are presented. These demonstrate that this PID control structure provides the necessary level of accuracy and robustness for this engineering process

Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models

Matlab/Simulink is a development and simulation language that is widely used by the Cyber-Physical System (CPS) industry to model dynamical systems. There are two mainstream approaches to verify CPS Simulink models: model testing that attempts to identify failures in models by executing them for a number of sampled test inputs, and model checking that attempts to exhaustively check the correctness of models against some given formal properties. In this paper, we present an industrial Simulink model benchmark, provide a categorization of different model types in the benchmark, describe the recurring logical patterns in the model requirements, and discuss the results of applying model checking and model testing approaches to identify requirements violations in the benchmarked models. Based on the results, we discuss the strengths and weaknesses of model testing and model checking. Our results further suggest that model checking and model testing are complementary and by combining them, we can significantly enhance the capabilities of each of these approaches individually. We conclude by providing guidelines as to how the two approaches can be best applied together.Comment: 10 pages + 2 page reference

Integrated Design and Implementation of Embedded Control Systems with Scilab

Embedded systems are playing an increasingly important role in control engineering. Despite their popularity, embedded systems are generally subject to resource constraints and it is therefore difficult to build complex control systems on embedded platforms. Traditionally, the design and implementation of control systems are often separated, which causes the development of embedded control systems to be highly time-consuming and costly. To address these problems, this paper presents a low-cost, reusable, reconfigurable platform that enables integrated design and implementation of embedded control systems. To minimize the cost, free and open source software packages such as Linux and Scilab are used. Scilab is ported to the embedded ARM-Linux system. The drivers for interfacing Scilab with several communication protocols including serial, Ethernet, and Modbus are developed. Experiments are conducted to test the developed embedded platform. The use of Scilab enables implementation of complex control algorithms on embedded platforms. With the developed platform, it is possible to perform all phases of the development cycle of embedded control systems in a unified environment, thus facilitating the reduction of development time and cost.Comment: 15 pages, 14 figures; Open Access at http://www.mdpi.org/sensors/papers/s8095501.pd