مجله کنترل
سال نهم شماره 3 (پاییز 1394)

This study intends to investigate a new control structure using a model reference fuzzy controller based on state feedback integral control. Motivated by the fact that most of the controlled systems are nonlinear and subject to uncertainty. Hence, designing control structures which can satisfy these cases is essential. The state feedback control is one of the control methods however this method does not show an appropriate performance when the non-zero disturbance is applied to the system. To cope with the mentioned drawback, using an integral control with state feedback would be an effective way. The classic state feedback integral control is considered as a beneficial way to control of linear or weakly nonlinear systems. For this reason, this paper concerns state feedback integral control using a model reference fuzzy controller. At the first stage of designed control approach, the nonlinear system is modeled by T-S fuzzy. At the second stage, control method is discussed. The proposed controller is applicable to a broad class of nonlinear systems subject to non-zero disturbance. The simulation results applied to control of inverted pendulum, magnetic levitation and Chua's circuit systems, verify the high control accuracy and appropriate performance of the proposed controller.

I most psycho-biological processes, great deal of nonlinear and dynamically complex behaviors are recorded which arise from interactions among numerous subsystems and processes –mostly with unknown behaviors-. Poincare section is one of the tools used not only for analysis of these systems, but also to control nonlinear systems; chaotic and uncertain. Albeit it has been long ago before introduction of Poincare section, but yet its stages are carried out artistically and heuristically.
Yet, it is not possible to “machine learn” based on Poincare section due to unstructured implementation method and problems like uncertain structures and modeling parameters. In this paper, first, modeling -using Poincare section- is explained, then based on the occurred events and the concepts “information” and “relativism”, variations in brain pattern of Autistic children will be diagnosed using Poincare section and informative approach.
Autism spectrum disorder is a revolutionary stage of brain diagnosed by deficiency in communications and social interactions as well as repetitive patterns in behavior, favorites and activities, called Autism in short. Autism in our point of view has an informative essence, in other words the main problem in Autism spectrum disorder origins from communication networks in brain that can lead to secondary problems over time. Is this paper, a new representations called “developed complementary representation” is introduced.
The most important characteristic of this representation is its special attention to signal phase as latent information in signal and effortlessness of energy. All the newly introduced concepts are implemented on electroencephalograph signal of Autistic children which has always been cumbersome to be recorded. This paper has focused on 45 case including 30 Autistic children and 15 healthy ranging from 3-10 years in three different stages; asleep, opened eyes and a state-of-art recording procedure based on brain dynamics and the proposed protocol presented by authors which is destitute of well-known problems of other recording procedures. Results show the presence of a common dynamic pattern in Autistic cased which is entirely different from healthy cases. This pattern variation is introduced in 16 different characteristics which are not related to electroencephalograph’s energy but to arrangement of section point on Poincare section (called “event”).

In this paper, an intelligent integral terminal sliding mode control method with a new sliding surface is proposed based on adaptive neural-fuzzy inference. First, a terminal sliding mode controller using a novel sliding surface is designed based on Lyapunov’s stability theorem for controlling a class of chaotic systems in presence of uncertainty and disturbance. The proposed sliding surface is a combination of the conventional terminal sliding surface and integral of a nonlinear function of the states of the system. The purpose of choosing this surface includes achieving appropriate response speed, removing chattering and robustness against external disturbance. Then, we assume that a nonlinear part of the system is unknown and only input-output data is available. Therefore, an adaptive neuro-fuzzy inference system is used to approximate the unknown part of the system dynamics. Finally, in order to enhance the performance of the proposed method, the honey bee algorithm is utilizd for selecting the coefficients of integral terminal sliding mode controller. The simulation results show the effectiveness of the controller due to the improved speed, removed chattering, appropriate transient response and satisfactory performance in the presence of uncertainties in the system model.

A fault tolerant attitude determination system for a three axis satellite has been suggested in this paper. For this purpose, the sun sensor and magnetometer data are only available. Using the designed algorithm, it is expected to have a reliable and continues process for computing the Euler angles. In other words, the faulty sensors should be determined and their faulty data are corrected automatically. The solution presented in this paper is a non-model based method that is based on on derivation of all possible rotations between reference and body frames. Using the Euler angles provided by these rotations, some variance measures are computed which utilized as an analytical tool for fault detection. With the development of this idea, it is shown that not only the faulty sensors but also their fault sources could be isolated. These sources are categorized into four levels that each level shows the fault extent. This paper also suggests some solutions for correcting the fault effects. So, the proposed approach is a non-model based methodology for fault management that is not affected by the dynamic variations or the uncorrect performance of the actuators. This suggestion also does not impose any additional hardware. Finally, the presented algorithms are validated using the simulations in different scenarios. The numerical results verify the expected outputs.

A robust attitude tracking control algorithm is suggested in this paper in the presence of environmental disturbances and fault occurrence in the actuators. For this, it is assumed that the moments of inertia and the upper bounds of disturbances are unknown. Also, there is no data about the fault type. The presented solution is a novel idea in which the unknown parameters and the constant or slow changing disturbances (including the environmental effects and the actuators faults) are obtained using the adaptive updating law. The variable part of disturbances is compensated by the sliding mode control. The suggested control algorithm is continuous and has no unwinding and singularity problems. Also, the unstable equilibrium point and the ambiguity problem in the display of attitude determination outputs (the quaternions) have been solved. To develop this methodology, a three stages scenario is presented to calculate the satellite moment of inertia, the disturbances and the fault detection thresholds. This algorithm can also determine the faulty actuator. Using this feature, the fault extent in the wheels are determined and based on, the suitable correction actions are selected. In this paper, the stability of all designed algorithms is proved. At the end, different simulations are conducted to validate the algorithms. The results of these simulations verify the expected performance.

This paper presents a novel delay-dependent consensus algorithm within the linear matrix inequality (LMI) framework to solve consensus problem of linear multi-agent systems with time-varying communication and input delays using fixed-order dynamic output feedback controller. The proposed scheme is decentralized in the sense that each agent updates its state according to the output information of itself and its neighbors. To guarantee consensus in this method, first based on graph theory and by proper system transformations, the consensus problem is converted to the stability problem of an equivalent state-delayed linear system. Then, by considering a suitable Lyapunov-Krasovskii function and applying special conditions on symmetric positive definite matrices, new delay-dependent consensus criteria in LMI form and the unknown controller coefficients are obtained for the system under fixed interconnection topology which can be easily solved by various effective optimization algorithms. As a main feature of the proposed approach, the order of decentralized controllers can be chosen arbitrarily according to the system conditions and limitations. Finally, a numerical example is presented to show the applicability and effectiveness of the proposed method.