The problem of the Segway robot and wheeled inverse pendulum is one of the most important classic issue and well-known example of nonlinear Control problem in engineering Control. The main purpose of the Control in this paper is achieving the cart body to the desired angle and the speed of the body as quickly as demanded velocity by Controlling of the applied torque. This paper presents an intelligent Control method for Segway robot, in which the downstream fuzzy Controller using the calculated error from the plant output, provides the proper input to the dynamic system. But, there is a learning mechanism in the upstream Controller that compares the output of the Controller VS the Reference Model and then improves it using fuzzy inverse Controller. it makes method more robust and efficient. Dynamic equations of the system are extracted by the Kane method. The design of the fuzzy Controller, based on the experience of the expert human for such a system, can perfectly overcome the instability and nonlinear nature of the system. Therefore, the use of the superviser Controller, due to automatic and intelligent adjustment, can help simpler design of this Controller. The performance of the proposed Control algorithm on Segway robot case study is evaluated and demonstrated by simulation in MATLAB. Also, considering the system's uncertainty in design and disturbances, the Controllers are robust against the uncertainties and disturbances in the problem. Figures and results are evidence of this issue.

Quadrotor unmanned aerial vehicles (UAVs) have been highly regarded in various military, research, scientific and entertainment fields due to their vast applications. In this paper, the design Control of the direct Reference Model for updating the Control parameters in the presence of noise and disturbance has been analyzed to find the optimal and desired amount in these micro-UAVs. Then, by choosing a Lyapunov function, the systematic stability of the system is examined and after proof of stability, an adaptive law has been extracted to update the systemic parameters. The simulations results show the Controller proper function to reduce the error. Utilizing the mean square error method, a comparison was made between the results of the proposed scheme and the results contained in another References, which shows an improvement in Controller performance of the proposed scheme.

The purpose of this paper is to develop a comprehensive maturity Model for command and Control. For this purpose, the inductive research method has been used to design the maturity Model. Thus, firstly, the most prominent maturity Models in command and Control, including the conceptual maturity Model, the headquarters maturity Model, the NATO Model, and the maturity Model of integrated command and Control centers (ICCCs) were analyzed. Among the studied Models, the NATO Model is more comprehencive, so we chose it as a basis for developing our Model. Then, the stages and variables of maturity were analyzed using the focus group method. The findings of the research show that some important characteristics of the command and Control maturity  have been neglected in the basic Model. Therefore, the innovation of this research is to add new features to achieve a more comprehensive Model. The result is a Model containing 5 levels and 20 variables. Validation of the Model has been done through comparative study and focus group method, which should be further strengthened by case studies and survey researches.

Unfalsified adaptive Control is a new approach in supervisory Control that ensures the selection of a stabilizing Controller from a Control set based on the system input-output data. A prerequisite for ensuring stability is the existence of a pre-designed Controller set that contains a stabilizing Controller. The supervisor selects the Controller based on the cost function calculated with the system input-output data. In this method, the Control system performance is restricted to the Controllers of the Control set. In this paper, the Controller set update is performed by introducing the concept of performance falsification along with the stability falsification of the active Controller. To falsify the performance of the Controller set, the structure of the Model Reference is proposed to evaluate the performance of the Control system. In case of performance falsification, a new Controller is designed and added to the Controller set based on system data and without using any Model. To design the Controller, a linear matrix inequality problem is solved. In this paper, no system Model is used, and the presented method is completely Model-free and data-oriented. The simulation results show the performance improvement of the proposed method compared to other methods in a standard robust adaptive benchmark system.

Lyapunov's direct method is a primary tool for designing Model Reference Adaptive Control (MRAC) and robust MRAC schemes. In general, Lyapunov function candidates contain two categories of quadratic terms. The first category includes the system tracking error quadratic terms or, in some cases, consist of the system state quadratic terms. The second consists of the parameter estimation error quadratic terms. To design MRAC and Robust MRAC systems, researchers have used a limited variety for choosing quadratic terms. In this study, we consider a general form for the tracking error quadratic terms. We consider a strictly increasing function that belongs to the class of c1, which is a function of state tracking error quadratic terms. It yields a general structure for stable adaptive laws for updating Controller parameters. For the MRAC scheme, the global asymptotic stability of the closed-loop system and stability and uniform bounded tracking of robust MRAC schemes are guaranteed. To evaluate the performance of the designed Controllers, we consider the single DOF wing rock dynamics.

In this paper a new Control scheme is proposed to ensure stability and performance of the teleoperation systems while a wide range of time delay of transmission line is allowed. For this mean, time delay is estimated and used to predict the plant output. A Model Reference adaptive Controller (MRAC) is designed for the master site using the predicted output of the plant. The proposed Control system indicates good stability and force tracking performance. For the slave site, an independent MRAC is designed and it is shown that a good tracking for the position and velocity signals is achieved.

In this paper, a new sensorless Model Reference adaptive method is used for direct Control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral (PI) blocks are optimized by using Krill Herd algorithm. In the second method, the fuzzy logic Control method is applied in the estimator structure instead of PI Controllers. The simulation results for the proposed methods illustrate that the estimated speed perfectly matches the actual speed of the DFIG. In addition, the desired slip value is achieved due to the accurate response. On the other hand, the active and reactive power responses have fast dynamics and relatively low oscillations. Moreover, the fuzzy Controller shows more robustness against the variations of machine parameters.

This article presents the design of an adaptive Proportional-Integral-Derivative (PID) Controller for a Mass-Spring-Damper mechanical system. The Modeling of the system is conducted using Newton's second law, from which the transfer function is derived. Given the uncertainties associated with the system parameters, an adaptive method based on a Reference Model is employed to adjust the Controller coefficients. The adaptation laws are formulated based on the MIT rule. The results from simulations and applications of the designed Controller demonstrate its effectiveness in tracking the Reference input. Under both nominal conditions and in the presence of uncertainties, the system output follows the Reference input without steady-state error and at an appropriate speed

The purpose of designing and manufacturing prosthetic organs is to create their maximum behavioral similarity to human organs. The aim of this paper is to improve the robotic arm Control via Model Reference Adaptive System (MRAS) based on Lyapunov theory using EMG data classification. In this paper, human arm is Modeled with a robot with two degrees of freedom. The proposed Control method is MRAS. The outcome of this research is a robotic arm with MRAS, using the classification of electromyogram (EMG) data recorded from human arm movements, results in proper tracking of the Reference signal, less overshoot and steady-state error compared to the conventional PI Controller. For this purpose, using two electrodes, EMG data is collected from the anterior deltoid and middle deltoid muscles of the arm of five female athletes and by performing two movements of abduction and flexion of the arm. Then, after eliminating noise, integral of absolute value (IAV), zero crossing (ZC), variance (VAR) and median frequency (MF) are extracted. Then, classification is done by linear discriminant analysis (LDA) method to detect movements based on data characteristics. Finally, the proposed Controller and Model are designed according to the EMG characteristics to achieve the proper Control response and the appropriate command signal is sent to the Controller to perform the corresponding movement. The results and the values of the obtained errors show the conformity of the Model and Controller behavior with the predefined movement pattern.