Nonlinear optimized adaptive trajectory control of helicopter

This paper attempts to develop an optimized adaptive trajectory control system for helicopters based on the dynamic inversion method. This control algorithm is implemented by three time-scale separation architectures. Pseudo control hedging (PCH) is used to protect the adaptive element from actuator saturation nonlinearities and also from the inner-outer-loop interaction. In addition, to augment the attitude control system, two online adaptive architectures that employ a neural network are used. By tuning the neural network based on the system model, a better and faster learning will be achieved, but this is a frustrating and time consuming process. Due to complexity in accurate tuning of neural network, this paper introduces a non-dominated sorting genetic algorithm II (NSGA-II) for off-line optimization of the neural network. Thus, in the proposed method, the neural network can compensate model inversion error caused by the deficiency of full knowledge of helicopter dynamics more accurately. The effectiveness of proposed method is demonstrated by numerical simulations.     

A NEW AGENT MATCHING SCHEME USING AN ORDERED FUZZY SIMILARITY MEASURE AND GAME THEORY

In this paper, an agent matching method for bilateral contracts in a multi-agent market is proposed. Each agent has a hierarchical representation of its trading commodity attributes by a tree structure of fuzzy attributes. Using this structure, the similarity between the trees of each pair of buyer and seller is computed using a new ordered fuzzy similarity algorithm. Then, using the concept of Stackelberg equilibrium in a leader–follower game, matchmaking is performed among the sellers and buyers. The fuzzy similarities of each agent with others in its personal viewpoint have been used as its payoffs in a bimatrix game. Through a case study for bilateral contracts of energy, the capabilities of the proposed agent-based system are illustrated.     

A new hybrid control technique for operation of DC microgrid under islanded operating mode

This study proposes a novel combined primary and secondary control approach for direct current microgrids, specifically in islanded mode. In primary control, this approach establishes an appropriate load power sharing between the distributed energy resources based on their rated power. Simultaneously, it considers the load voltage deviation and provides satisfactory voltage regulation in the secondary control loop. The proposed primary control is based on an efficient droop mechanism that only deploys the local variable measurements, so as to overcome the side effects caused by communication delays. In the case of secondary control, two different methods are devised. In the first, low bandwidth communication links are used to establish the minimum required data transfer between the converters. The effect of communication delay is further explored. The second method excludes any communication link and only uses local variables. Accordingly, a self-sufficient control loop is devised without any communication requirement. The proposed control notions are investigated in MATLAB/Simulink platform to highlight system performance. The results demonstrate that both proposed approaches can effectively compensate for the voltage deviation due to the primary control task. Detailed comparisons of the two methods are also provided.     

Development of degradation model for urban asphalt pavement

There is a lack of a profound understanding of urban pavement deterioration pattern. This is due to the complexity of traffic conditions and the variety of pavement structures in urban roads. The lack of a suitable deterioration model for the urban pavements limits the possibility of making any scientific and cost-effective repair and maintenance strategy. There is a need for a better understanding of the long-term behaviour of urban pavements by which predictive pavement condition models can be derived and consequently a suitable maintenance management system can be built. In response to this need, a comprehensive field study was performed in three Iranian cosmopolitan cities. Pervasive pavement damages were defined and an urban pavement condition index was established. A deterioration model was developed by monitoring and analysing the conditions of road pavements in a period of four years. This model varies as the structural and loading conditions of the pavement change. The efficiency and practicability of the model in predicting the conditions of the pavements were illustrated.     

An Optimal Solution for Test Case Generation Using ROBDD Graph and PSO Algorithm

Software testing is one of the most important techniques to examine the behavior of the software products to assure their quality. An effective and efficient testing approach must balance two important but conflicting requirements. One of them is the accuracy that needs a large number of test cases for testing, and the second one is reducing the time and cost, which requires a few test cases. Even for small software, the number of possible test cases is typically very large, and exhaustive testing is impractical. Hence, selecting appropriate test suite is necessary. Cause–effect graph testing is a common black‐box testing technique, which is equivalently representing Boolean relations between input parameters. However, the other traditional black‐box strategies cannot identify the relations that it may result in loss of some of the important test cases. Although the cause–effect graph is regarded very promising in specification testing, it is observed that most of the proposed approaches using the graph are complex or generate impossible and a large number of test cases. This observation has motivated our research to propose an efficient strategy to generate minimal test suite that simultaneously achieves high coverage of input parameters. To do so, at first, we identify major effects from the cause–effect graph using reduced ordered binary decision diagram (ROBDD). ROBDD makes the related Boolean expression of the graph concise and obtains a unique representation of the expression. Using the ROBDD, it is possible to reduce the size of the generated test suite and to perform testing faster. After that, our proposed method utilizes particle swarm optimization (PSO) algorithm to select the optimal test suite, which covers all pairwise combinations of input parameters. The experimental results show that our approach simultaneously achieves high efficacy and reduces cost of testing by selecting appropriate test cases, respectively, to both test size and coverage size. Also, it outperforms some existing state‐of‐the‐art strategies in the black‐box testing. Copyright © 2015 John Wiley & Sons, Ltd.     

Effect of High‐Energy Ball Milling on Mechanical Properties of the Mg–Nb Composites Fabricated through Powder Metallurgy Process

New biocompatible and biodegradable Mg–Nb composites used as bone implant materials are fabricated through powder metallurgy process. Mg–Nb mixture powders are prepared through mechanical milling and manual mixing. Then, the Mg–Nb composites are fabricated through cold press and sintering processes. The effect of mechanical milling and Nb particles as reinforcements on the microstructures and mechanical properties of Mg–Nb composites are investigated. The mechanical milling process is found to be effective in reducing the size of Mg and Nb particles, distributing the Nb particles uniformly in the Mg matrix and obtaining Mg–Nb composite particles. The Mg–Nb composite particles can be bound together firmly during the sintering process, result in Mg–Nb composite structures with no intermetallic formation, lower porosity, and higher mechanical properties compared to composites prepared through manual mixing. Interestingly, the mechanical properties of manually mixed Mg–Nb composites appear to be even lower than that of pure Mg.

     

ARO: A new model free optimization algorithm for real time applications inspired by the asexual reproduction

This paper presents a new individual based optimization algorithm, which is inspired from asexual reproduction known as a remarkable biological phenomenon, called as asexual reproduction optimization (ARO). ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. ARO adaptive search ability along with its strength and weakness points are fully described in the paper. Furthermore, the ARO convergence to the global optimum is mathematically analyzed. To approve the effectiveness of the ARO performance, it is tested with several benchmark functions frequently used in the area of optimization. Finally, the ARO performance is statistically compared with that of an improved genetic algorithm (GA). Results of simulation illustrate that ARO remarkably outperforms GA.     

Model updating of multistory shear buildings for simultaneous identification of mass,stiffness and damping matrices using two different soft-computing methods

In this research, two novel methods for simultaneous identification of mass–damping–stiffness of shear buildings are proposed. The first method presents a procedure to estimate the natural frequencies, modal damping ratios, and modal shapes of shear buildings from their forced vibration responses. To estimate the coefficient matrices of a state-space model, an auto-regressive exogenous excitation (ARX) model cooperating with a neural network concept is employed. The modal parameters of the structure are then evaluated from the eigenparameters of the coefficient matrix of the model. Finally, modal parameters are used to identify the physical/structural (i.e., mass, damping, and stiffness) matrices of the structure. In the second method, a direct strategy of physical/structural identification is developed from the dynamic responses of the structure without any eigenvalue analysis or optimization processes that are usually necessary in inverse problems. This method modifies the governing equations of motion based on relative responses of consecutive stories such that the new set of equations can be implemented in a cluster of artificial neural networks. The number of neural networks is equal to the number of degree-of-freedom of the structure. It is shown the noise effects may partially be eliminated by using high-order finite impulse response (FIR) filters in both methods. Finally, the feasibility and accuracy of the presented model updating methods are examined through numerical studies on multistory shear buildings using the simulated records with various noise levels. The excellent agreement of the obtained results with those of the finite element models shows the feasibility of the proposed methods.     

Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise

In the present paper, the identification and estimation problem of a single-input–single-output (SISO) fractional order state-space system will be addressed. A SISO state-space model is considered in which parameters and also state variables should be estimated. The canonical fractional order state-space system will be transformed into a regression equation by using a linear transformation and a shift operator that are appropriate for identification. The identification method provided in this paper is based on a recursive identification algorithm that has the capability of identifying the parameters of fractional order state-space system recursively. Another subject that will be addressed in this paper is a novel fractional order Kalman filter suitable for the systems with coloured measurement noise. The promising performance of the proposed methods is verified using two stable fractional order systems.