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Jet engines consume a lot of power at start time which makes it necessary to use a ground power supply unit (GPU). The GPU must pro vide the aircraft's demanded power at the start point only. One of the most important factors in supplying the required power for jet engines is to provide the necessary quality for the transmission of electricity to the aircraft. There are a lot of standards to check the quality of the starter among them MIL-STD-704 military Standard is one of the most frequently used ones. This Standard governs two principles of the power quality and transmitted power to flying vehicles. A GPU system is designed and implemented in this paper to meet the requirements of MIL-STD-704. This paper proposes a new approach to implement a starter system for turbo-shaft engines. The transformer tap changer mechanism is used for voltage control and the parameters of the transformer are also optimized using Maxwell software to reduce losses. The power quality value is achieved based on an adaptive smart filtering system implemented at the output terminal of the starter system. This smart filter is designed to be controlled based on a support vector machine (SVM) machine learning scheme. Experimental results are provided to illustrate the successful and high-quality supply of power needed to start a helicopter.

Due to the nonlinear behavior of grid-connected cascaded multilevel inverters (GCCMI), the use of nonlinear controllers can guarantee system stability over a wide range of operation. Therefore, state-space modeling is required to design nonlinear controllers. In this manuscript, a comprehensive method is proposed for the general state-space modeling of an n-level GCCMI with LCL coupling. To validate the accuracy of obtained state-space model, an experimental setup of a cascaded multilevel inverter including two H-bridges has been implemented. The outputs of the state-space model are compared with the simulation and experimental results of the GCCMI. This shows that the proposed model is compatible with a real closed-loop system. The simulations were performed using EMTDC/PSCAD software. In the following, the designed general model is used to develop a nonlinear controller based on the Lyapunov stability criteria for a multilevel shunt active power filter (SAPF). Results show that the designed controller is stable and robust in a wide range of operating point changes.

Because of high speed, efficiency, robustness and flexibility of multi-agent systems, in recent years there has been an increasing interest in the art of these systems. Artificial market mechanisms are one of the well-known negotiation multi-agent protocols in multi-agent systems. In this paper artificial capital market as a new variant of market mechanism is introduced and employed in a multi-robot foraging problem. In this artificial capital market, the robots are going to benefit via investment on some assets, defined as doing foraging task. Each investment has a cost and an outcome. Limited initial capital of the investors constrains their investments. A negotiation protocol is proposed for decision making of the agents. Qualitative analysis reveals speed of convergence, near optimal solutions and robustness of the algorithm. Numerical analysis shows advantages of the proposed method over two previously developed heuristics in terms of four performance criteria.

Object manipulation is a basic task in robotics and automation. Active manipulation by grasp is conventional approach in object manipulation. However, in many cases, grasp-less manipulation can be beneficial in terms of cost, minimalism and extension of workspace. On the other hand passive mechanisms are advantageous from the energy saving viewpoint. In this paper we combine these ideas to develop a dynamic passive object manipulation mechanism to achieve manipulation in more than one dimension and simultaneously change position and orientation of the object. In developed mechanism the manipulation platform is a simple inclined surface. The object is composed of two wheels with different radiuses and an axle connecting the wheels to each other. The object moves passively along a circular path on the platform. Kinematic equations of the motion are devised, dynamic analyses are performed and no-slippage conditions are extracted. Modelling in CATIA and simulations in MSC.ADAMS are performed and experimental set up is built to verify the analysis.

The process of empowering muscles in order to make them to a normal and common value is an expensive and prolonged work, in common available methods. There are some commercial exercise machines used for this purpose called rehabilitation systems. However, due to their insufficient motion freedom and prospect of being expensive, these machines have limited usage. Hence, it is clearly necessary that Mechatronic technologies should be used in this area. In this paper, an algorithm and an improved rule are presented for controlling a rehabilitation system of lower limbs which is implemented on a 6-Degree Of Freedom (DOF) Stewart parallel robot. Impedance control and adaptive control are used for this purpose. Estimation and optimization of control parameters will be done by artificial neural networks and genetic algorithms, respectively (intelligent strategy). Safety is guaranteed since some of controller parameters can be adapted under the stability conditions given by using Routh stability theory. Thereafter, the results of simulations are presented by defining a physiotherapy standard mode on a desired trajectory. MATLAB/SIMULINK is used for simulations. Finally, a comparative discussion between this strategy and common methods is devised.

Three-DOF manipulators were employed for juggling of polygonal objects in order to have full control over object's configuration. Dynamic grasp condition is obtained for the instances that the manipulators carry the object on their palms. Manipulation problem is modeled as a nonlinear optimal control problem. In this optimal control problem, time of free flight is used as a free parameter to determine throw and catch times. Cost function is selected to get maximum covered horizontal distance using minimum energy. By selecting third-order polynomials for joint motions, the problem is changed to a constrained parameter selection problem. Adaptive particle swarm optimization method is consequently employed to solve the optimization problem. Effectiveness of the optimization algorithm is verified by a set of simulations in MSC. ADAMS.

By changing the electricity market in smart grids, the consumers will be able to react to the electricity price. As close correlation of price and load, the density of this reaction can affect to demand curve and shift it in market. For this purpose, an accurate prediction model is demanded for optimal operation as well as planning in power system. For this purpose, we proposed a new hybrid forecast model based on dual-tree complex wavelet transform and multi-stage forecast engine (MSFE). In this model at first, the signal entered to proposed wavelet transform and then, it is filtered by new feature selection. After that, the signal predicted by proposed MSFE in three steps. An intelligent algorithm is applied to the forecast engine to increase its ability and prediction accuracy during the process. Finally, the improved fusion algorithm gather the outputs of MSFE. Effectiveness of the proposed method has been implemented over Australia’s and New England electricity market data. Obtained results compared with several prediction models which demonstrate the validity of proposed model.

This article proposed a new control strategy based on Takagi–Sugeno fuzzy model for deceasing the power system oscillation. This controller is based on the parallel distributed compensation structure, the stability of the whole closed‐loop model is provided using a general Lyapunov‐Krasovski functional. Also, in this article, a new objective function has been considered to test the proposed Fuzzy Power System Stabilizer in different load conditions which increase the system damping after the system undergoes a disturbance. So, for testing the effectiveness of the proposed controller, the damping factor, damping ratio, and a combination of the damping factor and damping ratio were analyzed and compared with the proposed objective function. The effectiveness of the proposed strategy has been used over 16 machine 68 bus power system. The eigenvalue analysis and nonlinear time domain simulation results proof the effectiveness of the proposed method. © 2015 Wiley Periodicals, Inc. Complexity 21: 288–298, 2016

A polynomial trajectory is a time-traveled distance function used to describe trajectory of the robot. Optimal high-degree polynomial trajectories considering initial and the final velocity conditions besides the acceleration constraints are desired. In this paper, a trajectory optimization problem aiming travel maximum distance for a robot that follows an arc based path is formulated. Along the path, the robot requires observing initial and final zero velocity conditions as well as certain acceleration limits. A high-degree polynomial equation along the trajectory is proposed inside of the optimization problem. The closed-form solution of the problem had been obtained analytically. The solution includes the coefficients of the any high-degree trajectory polynomial equation where the coefficients are obtained in closed-form. Simulations several experiments show that the resulting high-degree trajectories satisfy the initial and final zero velocity conditions as well as acceleration constraint.

The genus Trichoderma acts as an important antagonist against phytopathogenic fungi. This paper proposes a software-based identification tool for recognition of different species of Trichoderma. The method uses the morphological features for identification. Morphological-based species recognition is common method for identifying fungi, but regarding the similarity of morphological features among different species, their manual identification is difficult, time-consuming and may bring about faulty results. In this paper it is intended to identify different species of Trichoderma by means of neural network. For this purpose, 14 characteristics are used including 5 macroscopic and 9 microscopic characteristics. After quantifying qualitative features and training a multilayer perceptron neural network with quantified data, 25 species of Trichoderma are recognized by using the network. Totally, identification of Trichoderma species as one useful fungus is achieved by using the trained network.