There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...

Subspace model identification-Part 1. The output-error state-space model identification class of algorithms

Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach

A geometrically inspired matrix algorithm is derived for the identification of state space models for multivariable linear time-invariant systems and using possibly noisy input- output measurements data only. In this project, only a limited number of input and output data are required for the determination of the system matrices. The algorithm can be best described and also understood in the matrix formalism and consists in the following two steps. First step, a state vector sequence is realized as the intersection of the row spaces of two block Hankel matrices which is constructed by apply input - output data. Then, the system matrices are obtained at once from the least squares solution of a set of linear equations. When dealing with noisy data, this algorithm draws its excellent performance from repeated use of the numerically stable and accurate singular value decomposition. The algorithm is easily applied to slowly time-varying systems using windowing or exponential weighting.

On-and off-line identification of linear state space models

Ultrasound Computed Tomography for Material Inspection: Principles, Design and Applications

Multivariable Output Error State Space (MOESP) and Numerical algorithms for Subspace State Space System Identification (N4SID) algorithms are two well known subspace identification techniques discussed in this paper. Due to the use of robust numerical tools such as QR decomposition and singular value decomposition (SVD), these identification techniques are often implemented for multivariable systems. Subspace identification algorithms are attractive since the state space form is highly suitable to estimate, predict, filters as well as for control design. In literature, there are several simulation studies for MOESP and N4SID algorithms performed in offline and online mode. In this paper, order selection, validity and the stability for both algorithms for model identification of a glass tube manufacturing process system is considered. The weighting factor α, used in online identification is obtained from trial and error and particle swarm optimization (PSO). Utilizing PSO, the value of α is determined in the online identification and a more accurate result with lower computation time is obtained.

N4SID and MOESP subspace identification methods

This paper presents a single phase shunt active power filter based on instantaneous power theory. The active filter will be connected directly to utility in order to reduce THD of load current, in this case the utility is TNB. The instantaneous power theory also known as p-q theory is used for three phase active filter and this paper proves that the p-q theory can also be implemented for single phase active filter. Since the system has only single phase signal for both voltage and current, thus the dummy signal with 120 o different angels must be generated for input of the p-q theory. The p-q technique will generate six signals PWM for switching IGBT, but only two of the signals will be used to control the switching IGBT. The simulation results are on MATLAB/Simulink environment tools presented in order to demonstrate the performance of the current load on single phase shunt active power filter.

Simulation single phase shunt active filter based on p-q technique using MATLAB/Simulink development tools environment

Dual motor drives fed by single inverter are purposely designed to reduced sizes and cost with respect to single motor drives fed by single inverter. This paper presents the speed responses behavior of Dual Permanent Magnet Synchronous Motor (PMSM) driven base on two different PWM control schemes, which are Space Vector Pulse Width Modulation (SVPWM) and Hysteresis Current Controller. These two techniques are compared for wide range of speed and for variation of load. MATLAB/Simulink has been chosen as the simulation tools. The comparison between SVPWM controller and Hysteresis Current Controller for Dual PMSM fed by single inverter is presented. Both techniques show satisfactory speed regulation for a wide range of speed either with load, no load or variation of load.

Performance comparison of SVPWM and Hysteresis Current Control for Dual motor drives

Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive with exogenous input (ARX) and autoregressive moving average with exogenous input (ARMAX)]. The models were validated using experimental data from full-scale plants. Simulation results revealed that the performance of the NARX neural network is better compared to the ARMAX and ARX. The NARX neural network may serve as a valuable forecasting tool for the plants.

Comparison of NARX Neural Network and Classical Modelling Approaches

This paper depicts the development of backward automatic parallel parking system for nonholonomic mobile robot. The configuration of the system consists of ultrasonic sensor, rotary encoder, controller, and actuators. The path planning algorithm is developed based on the data acquired from the sensor. The proposed idea of the path planning is based on the geometrical equations in which the needed information is referring to the distance between the mobile robot and the adjacent object. The ultrasonic sensor and rotary encoder respectively used to detect parking area and measure the detected space. A PIC32MX360F512L microcontroller is used in order to generate the algorithm and control the movement of the mobile robot. System implementation is briefly described to depict the system as a whole. Experimental results are presented to demonstrate and validate effectiveness of the technique used.

Irma Wani Jamaludin

Papers

N4SID and MOESP subspace identification methods

Simulation single phase shunt active filter based on p-q technique using MATLAB/Simulink development tools environment

Performance comparison of SVPWM and Hysteresis Current Control for Dual motor drives

Comparison of NARX Neural Network and Classical Modelling Approaches

Development of an automatic parallel parking system for nonholonomic mobile robot