Journal of Artificial Intelligence in Electrical Engineering
Volume:4 Issue: 15, Autumn 2015

In this paper, a robust tracking control method for automatic take-off and trajectory tracking of a quadrotor helicopter is presented. The designed controller includes two parts: a position controller and an attitude controller. The attitude controller is designed by using the sliding mode control (SMC) method to track the desired pitch and roll angles, which are the output of position controller. The position controller is also a design using SMC and the attitude controller is faster than position controller.

An important issue in power generation and petrochemical industries is monitoring pipe wall thickness and corrosion/erosion rate. In this thesis, a combination of signal processing techniques are used to estimate the corrosion rate estimates based on MBE. Corrosion rate is estimated based on ultrasonic pipe wall thickness data is collected over a short period of time using MBE model. This technique is based on data collected from the speedometer applied for thinning and both indicate that they were able to estimate the rate of corrosion in short periods of time and with good accuracy.

Although LCL filters are used widely in the grid connected inverters to reduce high-order harmonics, such a system increases system order and therefore sustainable design of closed-loop controller system will be complicated. Recently, the single-loop control strategy has been suggested for L or LC filter based grid-connected inverters. However, the use of single-loop control directly in LCL filter-based inverters may cause instability. In this paper, a new double-loop control strategy is presented which includes a voltage external loop and an internal loop of filter capacitor current. The external loop controls the input voltage of the grid-connected inverter. The internal loop improves system stability margin and removes the resonance of LCL filter. To obtain the transfer function of system, single-phase instantaneous power theory is used. The computer simulation has proved the feasibility of the proposed control.

A bearingless induction machine has combined characteristics of induction motor and magnetic bearings. Therefore, the advantages are small size and low-cost. In the magnetic suspension of the bearingless motors, suspension forces are generated based on the feedback signals of displacement sensors detecting the movement of the rotor shaft. The suspension forces are generated taking an advantage of the strong flux distribution of a revolving magnetic field in the air gap between the stator and rotor. Thus, information of the instantaneous orientation and amplitude of the revolving magnetic field is required in a controller of the bearingless motor. Therefore, vector control methods are necessary for transient conditions. For control improvement of vector control, a PID controller can be used in horizontal and vertical force paths. Radial positions x and y are detected by displacement sensors.

Automatic facial recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each image. Two well-known machine learning methods, template matching and support vector machine, are used for classification using the ORL female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance-based feature descriptors. Entropy LDP SVM is as an improved algorithm for facial recognition than previous presented methods that improves recognition rate by features extraction of images. Test results showed that Entropy LDP SVM, method presented in this paper, is fast and efficient. Innovation proposed in this paper is the use of entropy operator before applying LDP feature extraction method. The test results showed that the application of this method on ORL database images causes 3 percent increases in comparison with not using entropy operator.

Extraction methods of facial expression characteristics have disadvantages according to Classical LBP such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. In this paper, we introduce an improved LBP algorithm to solve these problems that utilizes Fast PCA algorithm for reduction of vector dimensions of extracted features. In other words, proffer method (Fast PCAⰓ) is an improved LBP algorithm that is extracted from classical LBP operator. In this method, first circular neighbor operator is used for features extraction of facial expression. Then, an algorithm of Fast PCA is used for reduction of feature vector dimensions. Simulation results show that the proposed method in this paper in terms of accuracy and speed of recognition, has had a better performance compared with the same algorithm.