hadi delavari

At present, with the significant growth of energy consumption, increase of greenhouse gases and environmental pollutants, more attention is directed toward renewable energies. Renewable energies include geothermal, wind, photovoltaic energy and etc. Among the advantages of photovoltaic energy, its wide range and easy access, helping to preserve the environment, compatibility with distributed power networks, low noise, quick installation and lower cost compared to other energies can be noted. Important challenges facing photovoltaic systems are changing climatic conditions and parameters variation that affect the performance of the system. In this paper, to track the maximum power point in a photovoltaic system, a fuzzy fractional order sliding mode controller based on disturbance observer and uncertainty estimator using neural network is designed. The sliding mode control is used to reduce chattering, neural network to estimate the system uncertainties, fuzzy system to estimate the coefficient of the signum function in the control law and disturbance observer to approximate the disturbances in the system. Also, the stability of the system has been proven using the Lyapunov method. The simulation results of the photovoltaic system confirm the effectiveness of the proposed method and shows satisfactory performance.

These days, one of the biggest challenges in the world is dealing with the outbreak of the Covid 19 virus. Recently, new variants of this virus have been identified that have a much higher rate of transmission. To effectively control and manage the spread of the disease, a clear understanding of its transmission dynamics and effective control techniques to reduce or inhibit the spread of the virus is necessary. Although vaccine production and distribution are currently underway, Non-Pharmacological Interventions (NPI) continue to be an important and fundamental strategy for controlling the spread of the virus in various countries around the world. In this paper, Covid 19 dynamics is modeled using four well-known categories (SEIR): Susceptible-Exposed-Infected-Recovered. Since the parameters of the model have uncertainty, a robust control method should be designed. In this paper, using fractional calculus and fuzzy logic, a robust fuzzy fractional-order sliding mode controller (FOFSMC) for Covid 19 dynamics is proposed, which aims to control the prevalence of the disease using NPI. The proposed method is implemented both on the integer and fractional order model. Finally, the performance of the proposed controller on the new variant of the Covid 19 virus with a faster disease transmission rate will be evaluated.

Coronavirus, or Covid 19, is a contagious disease caused by the coronavirus and is a threat to the health and economy of countries. Although vaccine production and distribution are currently underway, but non-pharmacological interventions are still being implemented as an important and fundamental strategy to control the spread of the virus in countries around the world. Now, according to the existing conditions, having a suitable dynamic model of this disease will provide information to the relevant authorities about the behavior, prevalence, speed of transmission, and other parameters. Various mathematical modeling methods have been proposed to analyze the transmission patterns of this new disease. In this paper, using fractional calculus, the dynamics of Covid 19 will be investigated. One of the major advantages of fractional calculus, which can be very effective in modeling and controlling epidemics, is its long-term memory property. With a dynamic model of virus transmission and prevalence, focusing on a control strategy based on non-pharmacological interventions can be important. In this paper, a new adaptive fractional order sliding mode controller is proposed for non-pharmacological decisions. The proposed method in this paper for controlling non-pharmacological interventions is an adaptive fractional order active sliding mode control, which can have a good performance due to its robustness against parameter uncertainty and system disturbances.

Rehabilitation and assistive robots have drawn a large amount of interest, related to the increase of the elderly and the increase in diseases such as stroke and spinal cord injuries as well as the high cost of rehabilitation. In this paper, a fractional order adaptive fuzzy terminal sliding mode control is proposed for a knee joint orthosis. A model integrating the human lower-limb and orthosis based on the Lagrange equations is used. To overcome the uncertainties and external disturbances, a fractional order terminal sliding mode control is designed, then in order to remove the undesirable chattering phenomenon in control signal, a fractional order adaptive fuzzy controller is designed. To improve the precision and speed of tracking and to decrease the effect of the uncertainties in muscular torque modeling on the system control, a nonlinear disturbance observer is combined with fractional order terminal sliding mode control. The stability of closed loop system is proved by new fractional order extention of Lyapunov theorem. The PSO algorithm is used to determine the coefficients of the adaptive fuzzy terminal sliding mode control and the fuzzy membership functions. Finally, the performance of the proposed controller is compared with conventional sliding mode control and PID control.

In this paper, by combining fractional calculus and sliding mode control theory, a new fractional order adaptive terminal sliding mode controller is proposed for the maximum power point tracking in a solar cell. To find the maximum power point, the incremental conductance method has been used. First, a fractional order terminal sliding mode controller is designed in which the control law depends on knowing the upper bound of uncertainty in the system, but in practical application it is difficult or in some cases impossible to calculate this upper limit. In this paper, an adaptive law is given for online calculating of this parameter. The stability proof of the sliding surface, as well as the proof of finite time convergence of closed-loop system, are investigated using the Lyapunov theory. Finally, the performance of the proposed controller is evaluated both in normal and partial shading conditions. For a better comparison of the proposed controller, the performance of this controller is compared in the presence of load variations and the variations of system parameters with the conventional (integer order) terminal sliding mode control.

Energy saving, low robot mass to carried mass ratio, more ability to work in various environments, easier delivery of parts and lower production costs in flexible robots make these robots more attractive than rigid robots to many researchers and industries. But due to nonlinearities in flexible robot system and high vibration in operation points and also more sensitivity against external disturbances, control of these robots is more difficult and complex. In this paper a controller for a flexible link manipulator based on fractional calculus is practically implemented. At first the dynamic model of a single flexible-link robot is introduced. Then various controllers such as fuzzy control, PID control, and fractional order PID torque control are practically implemented on a single flexible-link robot made in laboratory, and then the performance of each controllers in decreasing of arm vibration in final desired point and tracking error reduction are investigated. Further, to compare the robustness of the designed controllers, a same constant disturbance is applied to all controllers and their performance are compared. Finally, the simulation results and experimental results show that the fractional order PID torque controller has the best results among the implemented controllers.

Magnetic levitation systems are widely used in various industries. These kind of systems are usually open-loop unstable and are described by highly nonlinear differential equations which present additional difficulties in controlling these systems in the presence of disturbance and sensor noise. We consider the stabilization and the tracking problems of a magnetic levitation system. In this paper an adaptive fractional order Backstepping sliding mode control schemes is proposed. Backstepping algorithm is based on the Lyapunov theory. The proposed controller in this paper is designed by a combination of a Backstepping algorithm, sliding mode control and fractional calculus to make more degree of freedom and robustness. The stability of the closed loop system is investigated by using the Lyapunov stability theorem and the new extension of Lyapunov stability theorem for fractional order systems. Simulations are performed to confirm the theoretical results of the proposed controller for the magnetic levitation system. The proposed controller is able to reject the sensor noise and disturbance with a chattering free control law. Finally the simulation results of the proposed controller are compared with the adaptive fast terminal sliding mode control.

Chaos is an unpredictable phenomenon that is considered in the nonlinear dynamical systems. In this paper, the chaos control in the nonlinear dynamic gear transmission system is considered. At first, the complex dynamics characters of gear transmission system are studied. Then, the existence of chaos in this nonlinear dynamic system is studied by phase portrait. The design process of sliding-mode control is divided into two steps: The first step is designing a sliding surface so that the plant restricted to the sliding surface has a desired system response, and the second step is constructing a switched feedback gains necessary to drive the plant’s state trajectory to the sliding surface. The stability of the proposed sliding surface is studied by the first theorem in this paper. Chaos control and stabilization of the gear transmission system states is studied based on the Lyapunov stability theorem. Also the stability of the closed loop system is investigated. The effects of the unknown controller parameters, system uncertainty, external disturbance and nonlinearity in the control input are fully taken into account. Appropriate adaptation laws are obtained to undertake the unknown parameters of the controller. Finally, simulation results demonstrate the feasibility and robustness of the proposed controller.

In this paper, a new robust controller based on geometric homogeneity and adaptive integral sliding mode is proposed for a class of second order systems. The upper bound of the system disturbances is not required. Fully unknown parameters have been considered in the described model and its finite–time convergence to zero equilibrium point is proved. Moreover, the controller is developed in the presence of control singularity and unknown non-symmetric input saturation. The finite time stability of the proposed controller has been proved via classical Lyapunov criteria. In order to tune the control parameters, all the positive constant gains are optimized by ant colony optimization algorithm during the offline input-output training data. Two polar robots are introduced to show the performance of the designed controller. The robustness and error accuracy are proved in simulation results. Moreover, the effects of input nonlinearity such as input saturation have been considered in the simulation.

In this paper, the synchronization of a new hyperchaotic complex system based on T-S fuzzy model is proposed. First, the considered hyperchaotic system is represented by T-S fuzzy model equivalently. Then, by using the parallel distributed compensation (PDC) method and by applying linear system theory and exact linearization (EL) technique, a fuzzy controller is designed to realize the synchronization. Finally, simulation results are carried out to demonstrate the performance of our proposed control scheme, and also the robustness of the designed fuzzy controller to uncertainties.

In this paper, under the existence of system uncertainties, external disturbances, and input nonlinearity, global finite time synchronization between two identical attractors which belong to a class of second-order chaotic nonlinear gyros are achieved by considering a method of continuous smooth second-order sliding mode control (HOAMSC). It is proved that the proposed controller is robust to mismatch parametric uncertainties. Also it is shown that the method have excellent performance and more accuracy in comparison with conventional sliding mode control. Based on Lyapunov stability theory, the proposed controller and some generic sufficient conditions for global finite time synchronization are designed such that the errors dynamic of two chaotic behaviour satisfy stability in the Lyapunov sense. The numerical results demonstrate the efficiency of the proposed scheme to synchronize the chaotic gyro systems using a single control input.