Morteza Mohammadzaheri

Identification and control of power plant de-superheater using soft computing techniques

Tight turbine steam temperature control is a necessity for obtaining long lifetime, high efficiency, high load following capability and high availability in power plants. The present work reports a systematic approach for the control strategy design of power plants with non-linear characteristics. The presented control strategy is developed based on optimized PI control with genetic algorithms (GAs) and investigates performance and robustness of the GA-based PI controller (GAPI). In order to design the controller, an effective neuro-fuzzy model of the de-superheating process is developed based on recorded data. Results indicate a successful identification of the high-order de-superheating process as well as improvements in the performance of the steam temperature controller.

Multi-zone temperature prediction in a commercial building using artificial neural network model

Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.

Intelligent modeling of a piezoelectric tube actuator

Various model-based control methods are currently used in control of piezoelectric tubes, others such as internal model control and model predictive control are anticipated to be employed soon. All these control systems are designed based on black box models. However, systematic black box modeling of piezoelectric tubes has been overlooked in the literature to a large extent or has been presented in a too brief and faulty way. In this article, a novel structure of artificial neural networks is used to model and to assess the nonlinearity of piezoelectric actuators. Apart from nonlinearity, other features of the achieved models like delay time, sampling time, orders as well as system identification process are clearly stated, and more importantly, it is clarified that different definitions of accuracy are needed for different purposes of black box modeling, with change in model features, the accuracy may decrease for one purpose (e.g. predictive control) and increase for another one (e.g. simulation). This highly critical point has never been raised and addressed in modeling of piezoelectric tubes, and a definition of accuracy which suits static systems/models has been widely used in the past to assess models of piezoelectric tubes which are obviously dynamic. Experimental results support the proposed modeling ideas.

Intelligent Modelling of MIMO Nonlinear Dynamic Process Plants for Predictive Control Purposes

Abstract In this research, input/output data of a MIMO nonlinear system are used to create intelligent models. Multi layer perceprtrons and neuro-fuzzy networks are utilized for this purpose. For the purpose that these models suit predictive control in their best, a variety of subtle points should be considered. Recurrent models and subtractive clustering are used in this research, and a pre-processing is exerted on the columns of raw data. Then the prepared data are used to train models. A reliable checking process is also offered. A Catalytic Continuous Stirred Tank Reactor is used as case study. A computer model is used to gather input/data rather than a real one. Finally, the simulation is successfully performed to indicate the capabilities of intelligent modeling methods as well as the importance of the points offered through this paper.

A comparative study of different physics-based approaches to modelling of piezoelectric actuators

This article reviews different approaches to modelling of piezoelectric actuators (PZA). The electric charge/voltage variation causes shape deformation of piezoelectric materials. If the piezoelectric material is in contact with a structure, it has the tendency to actuate that structure; in this case, the piezoelectric material plays the role of an actuator. Piezoelectric actuators are the foremost actuators in nanopositioning, manipulating material at nano/micro metre scale, applicable in Atomic Force Microscopy (AFM), highly precise manufacturing and .... In nanopositioning, displacement of piezoelectric actuators should be precisely controlled. However, the application of displacement sensors is limited by their high expense and practical constraints. Estimating displacement of piezoelectric actuators, based on their input voltage, can eliminate expensive displacement sensors from control systems. Therefore, several models have been developed to predict the displacement of piezoelectric actuators based on their input voltage. Models are basically created either merely based on data mapping, black box models, or inspired by physical phenomena, physics-based models. Physics-based models are superior in offering a clear definition of the relationship between all parts of the system dynamics. The main physics-based models are Kelvin-Voigt, Maxwell-Slip, Duhem, Preisach and Prandtl-Ishlinskii models; for each one some critical features such as rate-dependency and reversibility are addressed in this paper. This article compares the mentioned approaches and states advantages/disadvantages of each method. Parameter identification in these models is done by adhoc and non-optimal methods motivating researchers to look for alternative methods.

Design of an anti-overshoot Mamdani-type fuzzy-adaptive controller for yaw angle control of a model helicopter

In this research, a fuzzy knowledge-base controller is designed for yaw control of model helicopter. At the next stage, an adjusting algorithm is presented to reduce the influence of high inertia on fuzzy controlled systems. Inertia may cause significant overshoot, which is undesirable and difficult to eliminate. In order to solve this problem, a simple algorithm is presented to reduce the control input by adjusting the fuzzy controller parameters while the system is getting close to the desired condition. Implementing this approach (including a lateral algorithm to reset the parameters in special conditions) for yaw angle control of a model helicopter reduces the overshoot and energy consumption considerably without significant decrease of the settling time.

IMC-based feedforward control of a piezoelectric tube actuator

This article addresses sensorless control of a piezoelectric tube actuator to avoid the expense and practical limits of displacement sensors in nanopositioning. Three electrical signals have been used to estimate displacement: the piezoelectric voltage, the induced voltage and the sensing voltage. In this work, the piezoelectric voltage was employed to estimate displacement which does not require drift removal like the sensing voltage and does not suffer from a time lag respect to displacement like the induced voltage. This signal is the actuating signal at the same time, so the sensorless control system is feedforward. It was shown the relationship between the piezoelectric actuator and displacement is nearly linear at the designated operation area: excitation of the tube by triangular voltage functions with the magnitude up to 60V and the frequency up to 60Hz. Therefore, Internal Model Control (IMC) was employed to design this feedforward controller based on a second order linear discrete model which maps the piezoelectric voltage into displacement. The performance of the proposed feedforward controller has been compared with a well-tuned feedforward P-action controller and a remarkable improvement has been observed.

A digital charge amplifier for hysteresis elimination in piezoelectric actuators

Piezoelectric actuators are commonly used for nanopositioning due to their high resolution, low power consumption and wide operating frequency, but they suffer hysteresis, which affects linearity. In this paper, a novel digital charge amplifier is presented. Results show that hysteresis is reduced by 91% compared with a voltage amplifier, but over long operational periods the digital charge amplifier approach suffers displacement drift. A non-linear ARX model with long-term accuracy is used with a data fusion algorithm to remove the drift. Experimental results are presented.

An enhanced physics-based model to estimate the displacement of piezoelectric actuators

Piezoelectric actuators are the foremost actuators in the area of nanopositioning. However, the sensors employed to measure the actuator displacement are expensive and difficult, if not impossible, to use. Mathematical models can map the easy-to-measure electrical signals to the displacements of the actuators as the displacement sensors are replaced with the models. In addition, these models can be used in model-based control system design. Two main groups of mathematical models are used for this purpose: black box and physics-based models. As an advantage, the latter has a much smaller number of parameters reducing computational demand in real-time applications. However, physics-based models suffer from (1) the relatively low accuracy of the models and (2) non-standard and ad-hoc parameter identification methods. In this research, to improve the model accuracy, mathematical structure of a well-known physics-based model, the Voigt model, is enhanced by adding two complementary terms inspired by another model, the Preisach model. Then, a standard method based on the evolutionary algorithms is proposed to identify the model’s parameters. The proposed ideas are substantiated to increase the applicability and accuracy of the model, and they are easily extendable to other physics-based models of piezoelectric actuators. The newly proposed enhanced structure of the Voigt model doubles the estimation accuracy of the original model and results in accuracies comparable with black box models.

Physics-based modelling of a piezoelectric actuator using genetic algorithm

A number of models have been presented to estimate the displacement of piezoelectric actuators; these models remove the need for accurate displacement sensors used in nanopositioning. Physics based models, inspired by physical phenomena, are widely used for this purpose due to their accuracy and comparatively low number of parameters. The common issue of these models is the lack of a non-ad-hoc and reliable method to estimate their parameters. Parameter identification of a widely accepted physics-based model, introduced by Voigt, is addressed in this paper. Non-linear governing equation of this model consists of five parameters needing to be identified. This research aims at developing/adopting an optimal and standard (non-ad-hoc) parameter identification algorithm to accurately determine the parameters of the model and, in a more general view, all physics-based models of piezoelectric actuators. In this paper, Genetic Algorithm (GA) which is a global optimisation method is employed to identify the model parameters.