Fast Adaptive Robust Differentiator Based Robust-Adaptive Control of Grid-Tied Inverters with a New L Filter Design Method

In this research, a new nonlinear and adaptive state feedback controller with a fast-adaptive robust differentiator is presented for grid-tied inverters. All parameters and external disturbances are taken as uncertain in the design of the proposed controller without the disadvantages of singularity and over-parameterization. A robust differentiator based on the second order sliding mode is also developed with a fast-adaptive structure to be able to consider the time derivative of the virtual control input. Unlike the conventional backstepping, the proposed differentiator overcomes the problem of explosion of complexity. In the closed-loop control system, the three phase source currents and direct current (DC) bus voltage are assumed to be available for feedback. Using the Lyapunov stability theory, it is proven that the overall control system has the global asymptotic stability. In addition, a new simple L filter design method based on the total harmonic distortion approach is also proposed. Simulations and experimental results show that the proposed controller assurances drive the tracking errors to zero with better performance, and it is robust against all uncertainties. Moreover, the proposed L filter design method matches the total harmonic distortion (THD) aim in the design with the experimental result

Complex Dynamics and Synchronization of Delayed-Feedback Nonlinear Oscillators

We describe a flexible and modular delayed-feedback nonlinear oscillator that is capable of generating a wide range of dynamical behaviours, from periodic oscillations to high-dimensional chaos. The oscillator uses electrooptic modulation and fibre-optic transmission, with feedback and filtering implemented through real-time digital-signal processing. We consider two such oscillators that are coupled to one another, and we identify the conditions under which they will synchronize. By examining the rates of divergence or convergence between two coupled oscillators, we quantify the maximum Lyapunov exponents or transverse Lyapunov exponents of the system, and we present an experimental method to determine these rates that does not require a mathematical model of the system. Finally, we demonstrate a new adaptive control method that keeps two oscillators synchronized even when the coupling between them is changing unpredictably.Comment: 24 pages, 13 figures. To appear in Phil. Trans. R. Soc. A (special theme issue to accompany 2009 International Workshop on Delayed Complex Systems

Robust fault tolerant control of induction motor system

Research into fault tolerant control (FTC, a set of techniques that are developed to increase plant availability and reduce the risk of safety hazards) for induction motors is motivated by practical concerns including the need for enhanced reliability, improved maintenance operations and reduced cost. Its aim is to prevent that simple faults develop into serious failure. Although, the subject of induction motor control is well known, the main topics in the literature are concerned with scalar and vector control and structural stability. However, induction machines experience various fault scenarios and to meet the above requirements FTC strategies based on existing or more advanced control methods become desirable. Some earlier studies on FTC have addressed particular problems of 3-phase sensor current/voltage FTC, torque FTC, etc. However, the development of these methods lacks a more general understanding of the overall problem of FTC for an induction motor based on a true fault classification of possible fault types.In order to develop a more general approach to FTC for induction motors, i.e. not just designing specific control approaches for individual induction motor fault scenarios, this thesis has carried out a systematic research on induction motor systems considering the various faults that can typically be present, having either “additive” fault or “multiplicative” effects on the system dynamics, according to whether the faults are sensor or actuator (additive fault) types or component or motor faults (multiplicative fault) types.To achieve the required objectives, an active approach to FTC is used, making use of fault estimation (FE, an approach that determine the magnitude of a fault signal online) and fault compensation. This approach of FTC/FE considers an integration of the electrical and mechanical dynamics, initially using adaptive and/or sliding mode observers, Linear Parameter Varying (LPV, in which nonlinear systems are locally decomposed into several linear systems scheduled by varying parameters) and then using back-stepping control combined with observer/estimation methods for handling certain forms of nonlinearity.In conclusion, the thesis proposed an integrated research of induction motor FTC/FE with the consideration of different types of faults and different types of uncertainties, and validated the approaches through simulations and experiments

Artificial Tune of Fuel Ratio: Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable fuel ratio result and adjust. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of trajectory in presence of uncertainty torque load. DOI:http://dx.doi.org/10.11591/ijece.v3i2.209

Development of adaptive transducer based on biological sensory mechanism

textAn adaptive sensor concept and prototype has been developed based on a sensing element which is analogous to and inspired by the arrangement of outer hair cells and inner hair cells between the basilar membrane and tectorial membrane which form the organ of corti in mammalian cochlea. The bio-inspired design was supported by development of a bond graph model of the electromotility (active response) of outer hair cells. Outer hair cells perform like actuators and simulation results using this model are compared with physiological data found in the literature to verify its characteristic response. Insight gained from the model is used to develop a sensor structure analogous to the organ of corti and designed to measure acceleration. A piezoelectric bimorph was selected as the transducer basis, and a bond graph model of the bimorph in an accelerometer configuration was formulated to aid control design and simulation. There is no published data regarding the type of information transmitted among the inner hair cells, outer hair cells, and brain. Consequently, a controller intended to adjust the adaptation process similar to what might exist in the cochlear system has been developed for the sensor and based on a model referenced adaptive control algorithm. Simulations verify that the algorithm can successfully control and enhance performance of the sensor. Practicability of the design is evaluated by a series of experiments on a prototype. This study focused on using a controller structure that was programmed, implemented, and tested using programmable logic based on FPGA technology. The experiments evaluated how well the adaptive sensor could meet a specified performance requirement. Implementation issues that arise, such as the need for differentiators in the adaptive controller or internal propagation of vibration within the sensor structure, hinder the tuning ability. Nevertheless, the trends indicate that the algorithm can meet the desired performance if certain limitations can be overcome. Finally, recommendations have been made for expansion of the research in such fields as an alternative structure for tuning, sensor networking, and reference sensor configuration.Mechanical Engineerin

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

A wavelet-based CMAC for enhanced multidimensional learning

The CMAC (Cerebellar Model Articulation Controller) neural network has been successfully used in control systems and other applications for many years. The network structure is modular and associative, allowing for rapid learning convergence with an ease of implementation in either hardware or software. The rate of convergence of the network is determined largely by the choice of the receptive field shape and the generalization parameter. This research contains a rigorous analysis of the rate of convergence with the standard CMAC, as well as the rate of convergence of networks using other receptive field shape. The effects of decimation from state-space to weight space are examined in detail. This analysis shows CMAC to be an adaptive lowpass filter, where the filter dynamics are governed by the generalization parameter. A more general CMAC is derived using wavelet-based receptive fields and a controllable decimation scheme, that is capable of convergence at any frequency within the Nyquist limits. The flexible decimation structure facilitates the optimization of computation for complex multidimensional problems. The stability of the wavelet-based CMAC is also examined