This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. ADAPTIVE integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant ALGORITHM considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an ADAPTIVE rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control ALGORITHM has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

Backpropagation ALGORITHM performs gradient descent only in the weight space of a network with fixed topology. A very small network cannot learn the problem well, and a very larger network will lead to overfitting and poor generalization performance. ALGORITHMs that can find an appropriate network architecture automatically are thus highly desirable. The ALGORITHMs that are introduced by researchers can be classified into five major groups. Pruning ALGORITHMs, constructive ALGORITHMs, hybrid ALGORITHMs, evolutionary ALGORITHMs, and learning automata based ALGORITHMs. Meybodi and Beigy introduced the first learning automata based ALGORITHMs, called survival ALGORITHM. This ALGORITHM produces networks with low complexity and high generalization. Survival ALGORITHM by turning off and on the weights, tries to find the most important weights. At the beginning, all weights of the network are on and contribute to learning. The on weights, whose absolute values are less than a threshold value, are penalized and those, whose absolute value are larger than another threshold value, are rewarded. The on weights, whose absolute values lie between these two threshold values, neither rewarded, nor penalized. The values of these two thresholds are determinative and have considerable effect on the performance of the survival ALGORITHM. Determination of the values of these thresholds is not an easy task and usually is determined by trial and error or using past experience. In this paper, we propose a method for adaptation of these two threshold values. The proposed method have been tested on number of problems and shown through simulations that the network generated by the survival ALGORITHM when threshold values are adapted has lesser number of weights and neurons, comparing to the network generated by the first version of the ALGORITHM reported earlier. Experimentation shows that the ADAPTIVE survival ALGORITHM has nearly the same degree of generalization as the non-ADAPTIVE version.

Distance-based clustering methods categorize samples by optimizing a global criterion, finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most of these methods have several hyper-parameters, and their performance is highly dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD) approach was proposed to optimize local criteria in terms of distance and density properties of samples. GDD can find clusters with different shapes and sizes without any free parameters. However, it may fail to discover the appropriate clusters due to the interfering of clustered samples in estimating the density and distance properties of remaining unclustered samples. Here, we introduce ADAPTIVE GDD (AGDD), which eliminates the inappropriate effect of clustered samples by ADAPTIVEly updating the parameters during clustering. It is stable and can identify clusters with various shapes, sizes, and densities without adding extra parameters. The distance metrics calculating the dissimilarity between samples can affect the clustering performance. The effect of different distance measurements is also analyzed on the method. The experimental results conducted on several well-known datasets show the effectiveness of the proposed AGDD method compared to the other well-known clustering methods.

In the last two decades, many researchers have focused on the problem of automation of vehicles, and many research has been devoted to solving the challenges posed by this area. One of the important aspects in this area is the problem of localizing the vehicle and mapping the environment simultaneously in an unknown environment, which is briefly referred to as SLAM. So far, many methods have been proposed to solve this problem, but few of these researches have been implemented on the platform of collaborative robots. In this paper, SLAM problem is extended to multi robot platform by employing extended kalman filter. Due to lack of knowledge about the measurement noise covariance, the elements of this matrix adapted according to the actual data received from the sensor by employing particle swarm optimization technique. Then, to solve this problem in the dynamic environment, probability hypothesis density filter is used to track the dynamic objects in the field of view of sensors. Finally, the performance of the ALGORITHM is evaluated in a MATLAB environment.

In the structural software test, the test data generation is essential. The problem of generating the test data is a search problem, and for solving the problem, the search ALGORITHMs can be used. Genetic ALGORITHM is one of the most widely used ALGORITHMs in this field. Adjusting the genetic ALGORITHM parameters helps to increase the effectiveness of this ALGORITHM. In this paper, the ADAPTIVE genetic ALGORITHM is used in order to maintain the diversity of the population to the test data generation based on the path coverage criterion, which calculates the rate of recombination and mutation with the similarity between the chromosomes and the amount of chromosome fitness during and around each ALGORITHM. The experiments performed show that this method is faster for generating the test data than the other versions of genetic ALGORITHM used by the others.

A novel nonlinear controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear dynamics and the presence of parametric uncertainties and perturbations in this system, sliding mode control is employed. To generate a smooth control signal, dynamic sliding mode method is used. Uncertainties bound is not required in the suggested ALGORITHM, since the ADAPTIVE gain in the controller relation is used in this study. Convergence of the sliding variable to zero and ADAPTIVE gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed controller is evaluated in a comprehensive simulation on the BDFIG model. Moreover, output performance of the proposed control ALGORITHM is compared to the conventional and second-order sliding mode and proportional-integral-derivative (PID) controllers.

Combinatorial optimization is the procedure of optimizing an objective function over the discrete configuration space. A genetic ALGORITHM (GA) has been applied successfully to solve various NP-complete combinatorial optimization problems. One of the most challenging problems in applying GA is selecting mutation operators and associated probabilities for each situation. GA uses just one type of mutation operator with a specified probability in the basic form. The mutation operator is often selected randomly in improved GAs that leverage several mutation operators. While an effective GA search occurs when the mutation type for each chromosome is selected according to mutant genes and the problem landscape. This paper proposes an ADAPTIVE genetic ALGORITHM that uses Q-learning to learn the best mutation strategy for each chromosome. In the proposed method, the success history of the mutant in solving the problem is utilized for specifying the best mutation type. For evaluating ADAPTIVE genetic ALGORITHM, we adopted the traveling salesman problem (TSP) as a well-known problem in the field of optimization. The results of the ADAPTIVE genetic ALGORITHM on five datasets show that this ALGORITHM performs better than single mutation GAs up to 14% for average cases. It is also indicated that the proposed ALGORITHM converges faster than single mutation GAs.