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

A survey of recent advances in fractional order control for time delay systems

Several papers reviewing fractional order calculus in control applications have been published recently. These papers focus on general tuning procedures, especially for the fractional order proportional integral derivative controller. However, not all these tuning procedures are applicable to all kinds of processes, such as the delicate time delay systems. This motivates the need for synthesizing fractional order control applications, problems, and advances completely dedicated to time delay processes. The purpose of this paper is to provide a state of the art that can be easily used as a basis to familiarize oneself with fractional order tuning strategies targeted for time delayed processes. Solely, the most recent advances, dating from the last decade, are included in this review

An automatic tuner with short experiment and probabilistic plant parameterization

A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise

System identification and speed control of electro- mechanical dual acting pulley continuously variable transmission

Researchers at Universiti Teknologi Malaysia (UTM) has designed, developed and patented an Electro-Mechanical Dual Acting Pulley Continuously Variable Transmission (EMDAP CVT). The newly developed EMDAP CVT is a complex nonlinear system. Since the system is difficult to be modeled, designing the suitable controller for the EMDAP CVT is a challenging task. However, it is possible to obtain model system and transfer function by employing System Identification (SI) technique. By having mathematical representation of the EMDAP CVT in form of transfer function, controller’s analysis and future works relating to the EMDAP CVT will be much easier. The main part of this research is to develop a model which is able to imitate the current EMDAP CVT system behaviours. Therefore, SI was performed to develop the model system and transfer function. Genetic Algorithm (GA) is used as an estimator with Nonlinear ARX (NARX) as a model structure. The mathematical modelling of the EMDAP CVT system is successfully presented and verified in form of 3rd order nonlinear transfer function. The focus of this research work is more on the implementation of speed control for the EMDAP CVT system based on model obtained from the SI. The EMDAP CVT speed controllers are designed for adjusting speed through providing appropriate CVT ratio to the system. The control objective is to achieve a desired output speed, which is used to specify and maintain the desired CVT ratio for the EMDAP CVT system. Proportional-Integral-Derivative (PID) controller is used as the basis and then fined tuned using conventional Ziegler-Nichols and Particle Swarm Optimization (PSO) method. Three controllers which are Proportional-plus-PSO (PPSO), Proportional-Derivative-plus-PSO (PD-PSO) and Proportional-Integral- Derivative-plus-PSO (PID-PSO) were developed to test the reliability of the obtained model system and transfer function. The performance of the designed controllers was demonstrated and validated through simulations and experiments. The error performance of the developed controllers is evaluated in terms of Integral of Absolute Error (IAE), Integral Square of Errors (ISE), Integral of Time multiplied by Absolute Errors (ITAE), and Mean Square Error (MSE). Based on the results, the PIDPSO speed controller gives a sufficient performance, such as settling time, overshooting and error performance. The validation approach resulted in lower than 5% percentage error thus verified the 95% confidence limit of the model system. Further controller’s analysis using Fuzzy Logic (FL) and Neural Network (NN) controllers were performed on the obtained model system and transfer function. The performance of the tested controllers were evaluated in terms of Steady State Error (SSE) and MSE values. All of the tested controllers produced good performance with steady state response within 5 seconds and SSE percentage lower than 5%. The end results show that, NARMA-L2 neural speed controller gives the best performance with SSE percentage of 0.91% and smallest MSE value of 3.28

Controller design for periodic disturbance rejection

Master'sMASTER OF ENGINEERIN

Applications of neural networks to control systems

Tese de dout., Engenharia Electrónica, School of Electronic Engineering Science, Univ. of Wales, Bangor, 1992This work investigates the applicability of artificial neural networks to control systems. The following properties of neural networks are identified as of major interest to this field: their ability to implement nonlinear mappings, their massively parallel structure and their capacity to adapt. Exploiting the first feature, a new method is proposed for PID autotuning. Based on integral measures of the open or closed loop step response, multilayer perceptrons (MLPs) are used to supply PID parameter values to a standard PID controller. Before being used on-line, the MLPs are trained offline, to provide PID parameter values based on integral performance criteria. Off-line simulations, where a plant with time-varying parameters and time varying transfer function is considered, show that well damped responses are obtained. The neural PID autotuner is subsequently implemented in real-time. Extensive experimentation confirms the good results obtained in the off-line simulations. To reduce the training time incurred when using the error back-propagation algorithm, three possibilities are investigated. A comparative study of higherorder methods of optimization identifies the Levenberg-Marquardt (LM)algorithm as the best method. When used for function approximation purposes, the neurons in the output layer of the MLPs have a linear activation function. Exploiting this linearity, the standard training criterion can be replaced by a new, yet equivalent, criterion. Using the LM algorithm to minimize this new criterion, together with an alternative form of Jacobian matrix, a new learning algorithm is obtained. This algorithm is subsequently parallelized. Its main blocks of computation are identified, separately parallelized, and finally connected together. The training time of MLPs is reduced by a factor greater than 70 executing the new learning algorithm on 7 Inmos transputers

The estimation and compensation of processes with time delays

The estimation and compensation of processes with time delays have been of interest to academics and practitioners for several decades. A full review of the literature for both model parameter and time delay estimation is presented. Gradient methods of parameter estimation, in open loop, in the time and frequency domains are subsequently considered in detail. Firstly, an algorithm is developed, using an appropriate gradient algorithm, for the estimation of all the parameters of an appropriate process model with time delay, in open loop, in the time domain. The convergence of the model parameters to the process parameters is considered analytically and in simulation. The estimation of the process parameters in the frequency domain is also addressed, with analytical procedures being defined to provide initial estimates of the model parameters, and a gradient algorithm being used to refine these estimates to attain the global minimum of the cost function that is optimised. The focus of the thesis is subsequently broadened with the consideration of compensation methods for processes with time delays. These methods are reviewed in a comprehensive manner, and the design of a modified Smith predictor, which facilitates a better regulator response than does the Smith predictor, is considered in detail. Gradient algorithms are subsequently developed for the estimation of process parameters (including time delay) in closed loop, in the Smith predictor and modified Smith predictor structures, in the time domain; the convergence of the model parameters to the process parameters is considered analytically and in simulation. The thesis concludes with an overview of the methods developed, and projections regarding future developments in the topics under consideration

Control and Automation

Control and automation systems are at the heart of our every day lives. This book is a collection of novel ideas and findings in these fields, published as part of the Special Issue on Control and Automation. The core focus of this issue was original ideas and potential contributions for both theory and practice. It received a total number of 21 submissions, out of which 7 were accepted. These published manuscripts tackle some novel approaches in control, including fractional order control systems, with applications in robotics, biomedical engineering, electrical engineering, vibratory systems, and wastewater treatment plants. This Special Issue has gathered a selection of novel research results regarding control systems in several distinct research areas. We hope that these papers will evoke new ideas, concepts, and further developments in the field