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This paper peruses the attitude control problem of a rigid body, a task that is subjected to topological obstructions. These topological obstructions express that there is no globally asymptotically stable equilibrium point for a system evolving on a compact manifold. Therefore, there exist no continuous or discontinuous feedback control laws to globally stabilize the desired rigid body attitude. Thereafter, this paper presents quaternion-based hybrid feedback to defeat these topological obstacles. The proposed control law is derived from a new kind of centrally synergistic potential functions which is introduced in this paper. This function induces a gradient vector field that globally asymptotically stabilizes the desired attitude. Finally, a simulation study depicts the superior performance of the proposed quaternion-based hybrid feedback control technique.

This paper peruses the voltage control of a boost DC-DC converter. The hybrid model of the converter is utilized to introduce a new switching control law, which regulates the output voltage of the converter. In this switching control law an average dwell time constraint is considered. This suggestion guarantees the exponential stability of a desired output voltage value. Furthermore, in the proposed switching control law, there exist no Zeno solutions, since the existence of the average dwell time constraint. Moreover, in power converters, two significant concerns are switching loss and electromagnetic interference resulting from numerous rates of variations of voltage and current. Therefore, the salient features of the proposed controller are the mitigation of the switching rate as well as the regulation of the output voltage. Finally, simulation experiments are followed through on a boost converter to validate the effectiveness and superiority of the proposed control strategy.

This paper presents optimal control of uncertain switched systems in presence of the external disturbance and parametric uncertainty and generally every uncertainty due to modeling error. In this paper, the switching signal is selected a priori and control input is designed to minimize a given cost function. Discrete Linear Quadratic (DLQ) control has been efficiently applied to certain systems as an optimal control. There would seem to be some difficulties to apply discrete linear quadratic control to uncertain switched system. To overcome the problems, this paper presents an appropriate model. Then uncertainties of the uncertain switched system are compensated by robust time-delay controller. Then control input is designed by discrete linear quadratic control. The stability analysis and simulation verify effectiveness of the proposed control approach.

In this paper we discuss the issue of optimal control of piecewise affine systems based on discontinuous quadratic Lyapunov function, given the stability of the said system, first we calculate the upper bound of the cost function, then we consider the control input as the state feedback and finally, by minimizing the upper bound of the cost function, the state feedback coefficients are calculated. Note that if we minimize upper bound the cost function is minimized. It is worth noting that the optimization problem is turned into a semidefinite programming problem with bilinear constraints. These problems can be solved using numerical or any other methods. Finally, to check the effectiveness of the method, we have listed a numerical example.

In this paper, an immersion and invariance (I&I) adaptive fault tolerant satellite attitude tracking control scheme is proposed. The proposed controller is capable of track the desired trajectory in the presence of unknown actuator multiplicative faults and unknown inertial matrix. Also based on Lyapunov direct method, all closed loop signals are proven to be globally asymptotically stable. The main advantage of this controller is improving closed loop performance while maintaining stability in the presence of unknown actuator faults. This method does not rely on certainty equivalence principle so it can be used to control the transient response of overall closed loop system by means of controlling the parameter estimation behavior which is not possible in traditional adaptive control. Numerical simulations are performed to demonstrate the effectiveness of proposed control scheme.

In this paper we present chaotic synchronization of a Lorenz system using Unscented Kalman Filter (UKF). The UKF has shown to produce better results, than Extended Kalman Filter (EKF), without performing potentially ill-conditioned numerical calculations and linearly approximating the evolution of the state vector covariance. The chaotic synchronization is implemented in the presence of state and measurement noises. Simulation results reveal the superior performance of UKF over EKF for this case.

Extended Kalman Filter (EKF) has been widely used as an important tool in practical applications to estimate states of nonlinear systems. There are a number of deficiencies in EKF such as biased estimation, complexity in calculation and inefficacity in not being able to compute analytical derivatives affect its application in many fields. In this paper, Unscented Kalman Filter (UKF) is employed for estimation of the state variables of the chaotic dynamical system. The chaotic synchronization is implemented by the UKF in the presence of processing noise and measurement noise. The results of the simulation on the Rossler chaotic system by UKF and its comparison with EKF show that the UKF has more accuracy and efficiency than EKF.

In this paper, a novel Modal Series Representation (MSR) approach is proposed to solve the infinite horizon optimal control problem (OCP) of nonlinear interconnected large-scale systems. In this approach, the high order, coupled, nonlinear two-point boundary value problem (TPBVP) derived from the Pontryagin's maximum principle is transformed into a sequence of decoupled linear TPBVP's. By solving the proposed linear TPBVP sequence in a recursive manner, the optimal control law and the optimal trajectory are determined in terms of uniformly convergent series. Hence, to obtain the optimal solution, only the techniques of solving linear ordinary differential equations (ODE's) are employed. Another important factor is that the computational structure of the proposed technique can effectively utilize the parallel processing facilities, from which a significant reduction of computational time can be obtained. Besides, a control design algorithm with low computational complexity and fast convergence rate is presented. Through the finite iterations of the algorithm, a closed-form expression is obtained for the suboptimal control law. Finally, a numerical example is included to demonstrate the computational efficiency and high accuracy of the proposed technique.

In this paper, a new fuzzy fading memory (FFM) is developed in order to aid a modified input estimation (MIE) technique and enhance its performance in tracking high maneuvering targets. The MIE has been introduced recently and performs well in tracking low and medium maneuvering targets. However, due to some modeling errors, the accuracy of this tracker may be seriously degraded in presence of high maneuvers. To cope with this difficulty, an intelligent approach based on FFM is presented in this paper. Simulation results prove the efficiency of the proposed method in tracking high maneuvering targets.

FPGA implementation of artificial neural networks has been an active research line in past years. ANNs because of their high capabilities in pattern recognition, classification and parallel processing are extensively used in several fields. In this paper, a new method to FPGA implementation of a multi layer perceptron neural network is introduced. The obtained network is used to install a hardware system in order to detection anthelmintics resistant nematodes in sheep flocks. The implemented method results in a low-cost and flexible circuit.

In this paper, by use of the properties of matrix measure, a set of linear and quadratic conditions is obtained which is sufficient for stabilizability of switched linear systems. These conditions are easily applicable because of their special form. So they are suitable to apply specially for higher order systems with several subsystems. Moreover, different system performances can also be achieved by using an appropriate objective function subject to the proposed conditions. In this way, an optimization problem is also introduced to stabilize the system with sub-optimal convergence rate. Finally a numerical example is used to show the effectiveness of the proposed approach.

In this paper an intelligent self-tuning approach for high maneuvering target tracking is proposed. Recently, a new modified input estimation (MIE) method has been introduced. Although this method represents satisfactory performance in low and mild maneuvering situations, its ability to track the high maneuvering targets is still unacceptable. In this study we present an intelligent self-tuning approach based on a fuzzy forgetting factor in order to enjoy satisfactory tracking performance in low, Medium and high maneuvering target cases. Simulation results show the efficiency of the proposed method in comparison with the simple MIE.

Nonlinear system analysis and design have been attracting increasing adherents in recent decades. Different normal form techniques, approximation methods, and perturbation procedures have been used to analyze these systems. In recent years, a novel approach, called modal series method, has been introduced and used to study the behavior of autonomous nonlinear dynamic systems of the form x = g(x). In this paper, we extend this approach to a wide class of nonautonomous nonlinear systems of the form x = g(x,i). Using two textbook examples, we show the capability of this extension to find approximate solutions for nonautonomous nonlinear systems.

Permutation entropy (PE) is a new complexity measure which can extract important information from long, complex and high-dimensional time series. The advantages of this measure such as its fast calculation and robustness with respect to additive noise make it suitable for biomedical signal analysis. In this paper the ability of PE for characterizing the normal and epileptic EEG signals is investigated. Classification is performed using discriminant analysis. The effect of additive Gaussian noise on the discrimination performance is also studied and some parameters derived from PE are suggested to improve the classification accuracy when the signal is contaminated with noise. The results indicate that the proposed measures can distinguish normal and epileptic EEG signals with an accuracy of more than 97% for clean EEG and more than 85% for highly noised EEG signals.

Computational complexity is a major challenge in evolutionary algorithms due to their need for repeated fitness function evaluations. Here, we aim to reduce number of fitness function evaluations by the use of fitness granulation via an adaptive fuzzy similarity analysis. In the proposed algorithm, an individual's fitness is only computed if it has insufficient similarity to a queue of fuzzy granules whose fitness has already been computed. If an individual is sufficiently similar to a known fuzzy granule, then that granule's fitness is used instead as a crude estimate. Otherwise, that individual is added to the queue as a new fuzzy granule. The queue size as well as each granule's radius of influence is adaptive and will grow/shrink depending on the population fitness and the number of dissimilar granules. The proposed technique is applied to a set of 6 traditional optimization benchmarks that are for their various characteristics. In comparison with standard application of evolutionary algorithms, statistical analysis reveals that the proposed method will significantly decrease the number of fitness function evaluations while finding equally good or better solutions.

In the airborne vehicles, complex target tracking with more accurately is highly desirable. Conventional techniques based on augmented or batch algorithms are computationally expensive, which is the major drawback for the real time target parameter estimation. In contrast, other conventional input estimation techniques which overcome this problem, assume constant acceleration and therefore this is not a generalized modeling (T.C. Wang and P.K. Varshney, 1993). The proposed algorithm is developed to overcome these drawbacks by using a new generalized dynamic modeling of acceleration and acceleration rate (jerk) with reduction on the vector state dimension. The proposed modified Kalman filter algorithm is based on a generalized formulation including our earlier works. Results show the significant improvement for high maneuver target tracking problem.

Ship borne targets normally maneuver on circular paths which have lead to tracking filters on circular turns. In this paper, an innovation technique is presented to transform the tracking-maneuvering target problems from Polar coordinate to Cartesian coordinate, therefore a standard linear Kalman filter can be easily applied to them. Mathematical relation between measurement noise covariance in polar coordinate and the measurement noise covariance in Cartesian coordinate for Kalman implementation is obtained in this approach via a theorem.

Power system is inherently a nonlinear system. It is a general practice in small signal analysis to linearize the dynamic equations of this system and deduce dynamic behaviors and stability properties and design controllers based on linear system theory. Unlike linear dynamic systems there isn't any general solution for nonlinear ones. This seems to be impossible to obtain exact solution of nonlinear systems closed form response in general and so as for power systems. Therefore, for nonlinear systems the only way is using approximate closed form solutions. Modal series is a novel method of obtaining closed form solution of nonlinear systems. Our purpose in this paper is to estimate modal coefficients of power system zero input response which represents by Modal series, with a method without any complexity of analytical algorithms that satisfies our desire accuracy. The method has been applied on turbo-generator system output signal. First, modal contents of turbo-generator system response have been identified by Prony analysis then modal series coefficients evaluated with a method based on linear matrix inequalities (LMI). Simulation results for turbo-generator system are available to verify accuracy and effectiveness of our approach. The proposed method is general and simply could be extended applying on large scale power system.

Computational complexity is a prohibitive factor in evolutionary optimization of sufficiently large and/or complex problems. Much of this computational complexity is due to the fitness function evaluation that may either not exist or be computationally very expensive. Here, we investigate the use of fitness granulation via an adaptive fuzzy similarity analysis as applied to two different hardware design problems that are evaluated using finite element analysis. The first design problem is a relatively simpler 2-D truss frame design with 36 parameters while the second problem is piezoelectric voltage and pattern arrangement design for static shape control in which 200 parameters are optimized. In comparison with standard application of evolutionary algorithms, statistical analysis reveals that the proposed method significantly decreases the number of fitness function evaluations while finding equally good or better solutions. Additionally, this more improvement is indicated with higher problem complexity.

In this paper, a new fuzzy inference method which is suitable for systems with large number of rules, is proposed. As we know, there are two well-known fuzzy inference systems, Mamdani and TSK. Each one has its own drawbacks and advantages but both of them have been encountered with problem while tuning their parameters especially when there is large number of rules in the system. Mamdani type systems faced to a huge amount of calculation and TSK type faced to large number of parameters. In our proposed method a combination of these two systems, is used. So it has small number of parameters for tuning as Mamdani has and it is as fast as TSK. We called this system Extended TSK because it is based upon it.

Dynamic systems analysis and achieve closed form time response of these systems has been an attractive field of interest for mathematicians and engineers. Unlike linear dynamic systems there isn't any general solution for nonlinear ones. This seems to be impossible and nobody can proposed exact solution of obtaining nonlinear systems closed form response. Therefore, for nonlinear systems the only way is using approximate closed form solutions. Modal series is a novel method of obtaining closed form solution of nonlinear systems. Our purpose in this paper is to estimate modal coefficients of zero input response of nonlinear systems with a method without any complexity of analytical algorithms that satisfies our desire accuracy. The purpose is to obtain closed form response for nonlinear systems just from some observations and measurement values. In this research first modal contents of nonlinear system response was identified by Prony analysis then Modal series coefficients was evaluated with a new method based on linear matrix inequalities (LMI). Simulation results for a second order nonlinear system is available to verify the effectiveness of our approach.

Summary form only given. In this work an approach is devised to represent, and study the behavior of, nonlinear dynamic systems using general nonlinear modal representation. This approach is shown to compare favorably to the normal form of vector field technique, the other methodology used for this purpose, in that it is valid under resonance conditions and it does not require nonlinear transformation. By representing the system more accurately and in terms of its modes and their interactions, it would be better suited to be used for understanding and analyzing complex behavior of stressed power system and also in designing various controls for the system. This method can represent a nonlinear system with any order of nonlinearity and provides the solution to the system nonlinear differential equation employing Laplace transformation. The accuracy and robustness of the method has been validated using three systems two of which model realistic utility power systems under stressed conditions.

A discrete-time adaptive fuzzy sliding mode controller is proposed for an antilock braking system (ABS) of a 13th order two-wheel nonlinear model of a car. The model includes interaction of front and rear wheel subsystems. The presented controller aims to least depend on a mathematical model, only assuming certain upper and lower bounds of uncertainties. The controller is designed based on a hybrid combination of variable structure control, direct adaptive fuzzy control and linear control. Two fuzzy approximators are used to estimate nonlinear functions of the plant. The controller is global uniform Lyapunov stable. Simulation results show favorable output tracking performance despite poor knowledge of wheel dynamics and system disturbances such as road roughness.