Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system

PMAC:Probabilistic Multimodality Adaptive Control

This paper develops a probabilistic multimodal adaptive control approach for systems that are characterised by temporal multimodality where the system dynamics are subject to abrupt mode switching at arbitrary times. In this framework, the control objective is redefined such that it utilises the complete probability distribution of the system dynamics. The derived probabilistic control law is thus of a dual type that incorporates the functional uncertainty of the controlled system. A multi-modal density model with prediction error-dependent mixing coefficients is introduced to effect the mode switching. This approach can deal with arbitrary noise distributions, nonlinear plant dynamics and arbitrary mode switching. For the affine systems focussed upon for illustration in this paper the approach has global stability. The theoretical architecture constructs are verified by validation on a simulation example

Probabilistic control for uncertain systems

In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Modelling, simulation and proportional integral control of a pneumatic motor

Researchers have shown a considerable amount of interest in the control of pneumatic drives over the past decade, for two main reasons, firstly, the response of the system is very slow and it is difficult to attain set points due to hysteresis and secondly, the dynamic model of the system is highly non-linear, which greatly complicates controller design and development. To address these problems, two streams of research effort have evolved and these are: (i) using conventional methods to develop a modelling and control strategy, (ii) adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling and control of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three regions, namely low speed, medium speed and high speed. The system is highly nonlinear in the low speed region, for which neuro-modelling, simulation and control strategies are developed

To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Design , Development and Performance Evaluation of Intelligence Sensors

Many electronic devices, instruments and sensors exhibit inherent nonlinear input-output characteristics. Nonlinearity also creeps in due to change in environmental conditions such as temperature and humidity. In addition, aging of the sensors also introduce nonlinearity. Due to such nonlinearities direct digital readout is not possible. As a result the devices or sensors are used only in the linear region of their characteristics. In other words the usable range of these devices gets restricted due to nonlinearity problem. The accuracy of measurement is also affected if the full range of the instrument is used. The nonlinearity present in the characteristics is usually time-varying and unpredictable as it depends on many uncertain factors stated above. Hence the prime objective of the thesis is to study the nonlinearity problem associated with these devices and suggest novel methods of circumventing these effects by suitably designing intelligent systems. In the present investigation,..

Development of an adaptive neurofuzzy controller

Master'sMASTER OF ENGINEERIN