جستجوی مقالات مرتبط با کلیدواژه « کنترل مد لغزشی » در نشریات گروه « فنی و مهندسی »

In this paper, an adaptive fault-tolerant control based on modified nonsingular fast terminal sliding mode control is developed for attitude tracking of a satellite with three magnetorquers and one reaction wheel. The proposed approach is designed to be robust in the presence of actuator faults, external disturbances, and inertia uncertainties and preserve the acceptable performance of system. The adaptive law is designed to estimate the upper bound of uncertain expressions, increase the tracking accuracy, and improve the performance of system. This parameter with a coefficient of sliding surface variable are used in the reaching phase of control law to achieve the chattering-free phenomenon. Stability and finite-time convergence of attitude variables is proved by the extended Lyapunov condition. To increase the tracking accuracy and compensate the required torque, a reaction wheel is used as a redundancy. Also, for increasing the control accuracy, the dynamics of this actuator is considered as well as the constraints of magnetorquers and reaction wheel. The simulations are performed and compared with the similar control method under the mentioned conditions to evaluate the performance of the proposed method. The results show the finite-time convergence, increasing the tracking accuracy, smoothing of satellite attitude changes, and generating the chattering-free control signals.

In this paper, formation control based on the virtual structure for the non-holonomic mobile robot system with two models of certain and uncertain kinematic equations is discussed. First, the formation equations of a certain model are calculated and then it is proved that it is possible to create a geometric shape and maintain that state by using the sliding model control theory for any two moving mobile robots. Then, after deriving the formation equations of the uncertain model, a sliding model controller is designed that is able to control the uncertain model provided that the uncertainty range of the kinematic equation is present. For each design, the stability of the system is guaranteed using the Lyapunov stability theorem. Finally, in order to compare the performance of the designed controllers, a pre-designed back-stepping controller is introduced and the results will be presented in the form of simulations. The simulation results show the effective performance of the designed controllers.

This article aims to control the group flight of unmanned helicopters. After presenting the dynamic equations using the Newton-Euler method, the dynamic inversion control has been applied to control the position and attitude of helicopters. generalized predictive control was also used to control the position and attitude of the helicopter. And the results of the simulation of these two controllers were compared, which showed the better performance of the predictive control. Therefore, in the design of the formation control, the independent control of the helicopters was done using the predictive control method. To control the formation, the method based on the leader-follower model has been used, An integral sliding mode controller is also used to control the dynamics of the formation error. The results of the simulation showed the success of the tracking by increasing the number of helicopters and the linear and spiral paths in the control commands.

Brushless direct current motors have been considered by many researchers due to their many advantages over other direct current motors. The upper band is unknown and aims to converge in a limited time. For this purpose, by rewriting the motor model in the presence of modeling uncertainties and limited external perturbations, the terminal slip mode control law is designed to stabilize the system and converge the output speed and motor flow to the desired values ​​in a finite time, using the development theorem. In this control law, the high bandwidth of total uncertainties and external disturbances is estimated online using an adaptive law. Finally, while calculating the system convergence finite time, an idea to reduce the umbrella in using the terminal slip mode control is presented. The results of the numerical simulations performed show the accuracy of the designed controllers.

In this paper, an optimal robust nonlinear control approach based on the state-dependent Riccati equation (SDRE) is presented for a class of fractional-order nonlinear systems. The SDRE control is a method for the solution of nonlinear systems optimal problems, which has some disadvantages, such as it is not robust against disturbances and uncertainties and has not finite time convergence ability. Sliding mode control (SMC) is also a common method for nonlinear systems control, although it is not an optimal control method, it has great advantages such as robustness which has made it very popular. In the proposed method, a combination of SMC and SDRE methods is used for optimal and robust nonlinear control, which is suitable for a class of fractional-order nonlinear systems. The proposed control method has a robust structure against mismatched disturbances. The closed loop system stability is guaranteed by the Lyapunov theory and simulation examples show the performance improvement of the proposed method.

Parallel manipulators are of interest in various industries due to their high precision, rigidity, high speed and low inertia. Controlling these types of systems faces challenges due to their complex and non-linear dynamics. Among the many methods of controlling the path of parallel manipulators, computed torque and sliding mode methods are the famous methods that are proposed. In practical applications, when the speed of the robot increases, adjusting the controller parameters is very difficult and depends on the working conditions of the robot, so the robot cannot work properly with fixed and predetermined coefficients under any condition. The type of path, the speed of the robot along the path, the initial conditions of the end effector of the robot in relation to the path, and even the sampling time are factors that affect the accuracy of the controller, and by changing each of them, it may be necessary to redefine the parameters of the control system and change the control coefficients. In this article, a method is presented which is based on the sliding mode method and the coefficients of the control system are adjusted appropriately by changing the sliding surface and sliding speed using the fuzzy method. The performance of this method has been investigated in two ways: modeling in MATLAB software and real time applying it to a planar parallel robot.

Due to its simplicity of implementation, the sliding mode control (SMC) method has been increasingly used in converters in the field of power electronics in recent years. The equivalent control approach can be leveraged to make the switching frequency stable in the sliding mode control of converters. In the SMC approach, most of the controller parameters are tuned by trial and error, and as a result, it is not possible to guarantee the stability of the controller in a wide range of operation. To cope with this challenge, a novel approach for designing the sliding mode controller and adjusting its parameters in a Z-source converter is proposed in this paper. To evaluate the proposed design, various important criteria including steady-state error and controller robustness against variations in the reference voltage, load, and input voltage have been investigated. The efficiency of the proposed method has been proved with the experiments through extensive MATLAB/Simulink simulations.

The model-based controllers need a mathematical model of the system, whereas the data-driven controllers operate based on measuring the input-output data. Nowadays, considering complexity of the industrial systems and unavailability of an accurate mathematical model of the system, scientists try to reduce dependency of the controllers on the mathematical model. In this paper, an adaptive sliding-mode data-driven controller for a class of unknown multi-input multi-output nonlinear discrete-time systems is proposed. Because the chattering phenomenon is the main challenge of the sliding-mode controllers, an adaptive sliding-mode controller is used to solve this problem. In addition, to solve the dependencies of the controller on the mathematical model, the proposed adaptive sliding-mode controller is combined with a data-driven controller. Next, the new adaptive laws for the switching gain and the Pseudo Jacobian Matrix (PJM) are calculated. In addition, the closed-loop stability based on the Lyapunov theory is investigated. To show performance of the controller, the proposed method is applied to a three-tank system. The proposed controller has some advantages in comparison with similar methods in references, such as reducing the conservatism and complexity in the controller design and simplifying the closed-loop stability analysis. The simulated results show that the proposed method better tracks the reference signals and improves rejection of the external disturbances. Furthermore, the chattering phenomenon is considerably reduced.

In this study, in order to enhance the accuracy of tracking repetitive maneuvers in Unmanned Aerial Vehicles (UAVs), a learning-based control scheme is proposed. At the outset, the controller is designed based on the sliding mode control (SMC) technique. In addition, the offline PD-type memory-based iterative learning control (ILC) is used along with SMC. The purpose of using ILC method is to reduce the effect of system uncertainty on the controller and decrease repetitive errors by adjusting the input control signal to dynamics and thus, to increase the reliability of following the desired path. In the ILC scheme, the error of states is saved during the maneuvers which will be used in the subsequent iteration. Also, in order to increase flexibility of the new control structure, ILC-SMC, a multilayer perceptron (MLP) has been developed. This network is designed to extend the control signal, generated by ILC, to similar maneuvers. The inputs of this neural network are the initial conditions for starting the maneuver and the output of the neural network is a gain that is multiplied by the stored control signal ILC and produces a new control signal. This generated signal will be suitable for similar maneuvers. The Levenberg–Marquardt (LM) algorithm has been used to train the multilayer perceptron artificial neural network. This method was then used in loop maneuvers. In this simulation, the difference between the maneuvers was in the acceleration of the maneuver, the radius of the maneuver, and the initial speed of the maneuver. This reduced the tracking error for similar maneuvers without performing the training process for the ILC control component. The presented control scheme is applied to a quadrotor aerial vehicle for tracking desired trajectories and it is shown that the vehicle is able to follow the desired trajectory better than the conventional SMC in the presence of uncertainties.

In this paper, a robust dynamic slip mode controller for an electrical robot manipulator is presented. The control law calculates the motor voltage based on the voltage control strategy. Uncertainties are estimated using the Fourier series expansion and the cutting error is compensated. Fourier coefficients are adjusted based on stability analysis. Also in this paper is the design of a robust controller using a new adaptive Fourier series extension. Compared to previous related works based on the Fourier series expansion, the advantage of this paper is that it provides a matching law for the main frequency of the Fourier series expansion and thus eliminates the need for trial and error in its regulation. A case study of a Scara robot powered by DC magnet electric motors. The effect of uncertainty estimation based on the Fourier series expansion is studied instead of using the sign function. The proposed method is also compared with Legendre polynomials. The simulation results confirm the robust and satisfactory performance of the proposed controller.

In this paper, a new adaptive fractional-order sliding mode controller is designed for a Permanent Magnet Synchronous Generator (PMSG) to track the maximum power point. The controller objective is to track the desired generator speed to extract the maximum power from the wind turbine system in the presence of parametric uncertainty and external disturbances. First, a new fractional order sliding surface is defined. To ensure the stability of the closed-loop system in the sliding model controller it is required to know the upper bounds of uncertainties and disturbances, where it is difficult to calculate these bounds for practical applications such as wind turbines. Therefore, the control signal parameters are estimated online by the proposed adaptive laws, in order to increase the convergence rate of the state variables to the reference value and reduce the chatting phenomenon, also to increase the system robustness against external disturbances and parametric uncertanity. On the other hand, due to the unknown disturbance dynamics, a disturbance observer is designed to estimate external disturbances and parametric uncertainty. Then, the stability of the general closed-loop system together with the disturbance observer is performed using Lyapunov's theory. Finally, the simulation results considering two different scenarios; first for step wind changes with external disturbance, second for changes in sine wind speed with parametric uncertainty. The results are compared with conventional sliding mode controller and results show the effective performance of the proposed controller in tracking the reference value, increasing its robustness against uncertainty and disturbance and reducing the chatting phenomenon.

In this article, various methods have been introduced regarding the movement of robots and the advantages and disadvantages of these methods. By combining the methods of potential function, virtual structure and leader tracking, an algorithm is obtained that, while having the merits of the mentioned methods, also overcomes the disadvantages and challenges of these methods to a large extent. The resulting algorithm is used to design the desired movement path of the robots, and a sliding mode controller acts as a useful tool to force the robots to follow these desired paths. Finally, to evaluate the performance of the designed system, the movement of a group of six non-holonomic robots that surround a target in a triangular formation has been simulated in MATLAB software. The obtained results is compared with the works done by other researchers. It was found that the introduced algorithm has important advantages over previous works, such as reducing the volume of calculations, removing the local minima of potential functions, reducing the range of input changes, the reduction of the number of weight coefficients to adjust the potential functions. It should be noted that the resulting advantages make this method more suitable for practical applications.

Multiphase (more than three phases) drives possess several advantages over conventional three-phase drives, such as reducing the amplitude and increasing the frequency of torque pulsations, reducing the rotor harmonic currents, reducing the current per phase without increasing the voltage per phase, lowering the dc-link current harmonics, and higher reliability. By increasing the number of phases it is also possible to increase the power /torque per ampere for the same volume machine. Another important feature of multi-phase drives is ensuring optimal and acceptable performance in unbalanced supply conditions due to interruption of one or even two phases, which has increased the acceptance of this type of drive in various industries. In this paper, speed control without mechanical sensor of six-phase induction machines in single-phase interruption conditions is presented using the second-order sliding mode control method. In this control method, the reference model theory is used to estimate the machine speed in order to improve the drive performance. The simulation results show that the method has an acceptable performance under unbalanced machine supply conditions and speed control will be performed well.

This paper proposes an integral sliding mode direct power control (ISM-DPC) strategy for brushless doubly fed induction generators. Two widely applied control strategies are available for this type of generators: hysteresis-based direct power control and vector control. Direct power control suffers from high power ripples and current distortions produced by variable switching frequency. Moreover, the tuning issues of PI controller, which are highly reliant on machine parameters and operating conditions, and necessity of a phase-locked-loop for frame alignment are accounted as limitation of these methods. The proposed integral sliding mode strategy directly controls active and reactive power to provide fast dynamic response and zero steady-state error. This method is developed in the control winding reference frame to avoid the application of PLL. A large-scale brushless doubly fed induction generator (BDFIG) is simulated to validate the effectiveness and robustness of the proposed ISM-DPC method in comparison with widely applied methods, vector control and direct power control.

In this paper, a model-independent adaptive sliding mode control method based on time-delay estimation (TDE) for a three degrees of freedom (3-DOF) helicopter in the presence of external disturbances and types of uncertainties is presented. In this approach, the switching gain of sliding mode is determined adaptively in order to increase the efficiency in tracking the reference path and reduce the chattering. In the adaptation law which causes rapid convergence to the sliding surface, a small and arbitrary neighborhood of sliding mode polynomials is used. In this neighborhood, it is considered that the derivatives of the adaptive gain are inversely proportional to the sliding variables. In this approach, to avoid using the dynamic model that is accompanied by modeling error, the time delay estimation approach has been used. In the time delay estimation, creating a time delay signal eliminates system dynamics and uncertainties. The uniformly ultimately bounded (UUB) stability of the closed-loop system is also shown. Finally, the efficiency of the proposed approach is investigated by studying the simulation on a 3-DOF helicopter.

Considering that the traction system is main factor of energy consumption in the train, and amount of force required for moving the train is directly related to its speed, therefore, the optimal profile selection for the speed of the train movement, in the path between the stations and the design of the controller for accurate tracing The optimal profile of the train speed, can be a determinant of the amount of energy consumed. Control of a train includes the non-linear, uncertainty and external disturbances terms that should be considered in the design of control rules. In this thesis, we present a control strategy for tracking optimal profile of train speed, based on the fuzzy sliding mode control (FSMC). The main motive of use the Sliding Mode Control (SMC), in non-linear systems, its resistance to parametric uncertainties, unmodeled dynamics, external disturbances and as well as the simplicity of its design. However, the occurrence of chattering phenomenon is the most important limiting factors the use of this control method. In this thesis to remove the chattering, fuzzy control is used to estimate the reaching control signal and the stability of the entire system is also guaranteed on the basis of the Lyapunov stability theorem Numerical simulations using the nonlinear dynamics model of the train, despite the uncertainty and disturbance, and comparing it with the conventional SMC, show the effectiveness of the FSMC method in tracing the optimal train speed profile.

In this paper, a fairly comprehensive comparative study has been done on online identification methods for the dynamic model of an aircraft system. To this aim, first, the existing algorithms in this field are introduced. Then, some of these approaches including the recursive least square, the recursive extended least square, the recursive instrumental variable, the extended matrix, the radial basis function neural network, and the multilayer perceptron neural network are utilized to identify the aircraft model. To carry out the simulations, and train the neural networks, the linearized model and online data of the Boeing 747 aircraft controlled by a sliding mode controller on an arbitrary reference trajectory are employed. Finally, the efficiency of each of the above-mentioned methods is evaluated and compared to the other approaches. According to the obtained results of this research, the radial basis function neural network method has a significantly superior performance over the other algorithms due to dynamic noise estimation, independence from the system model, rejecting the linear model of the system, and higher accuracy while maintaining the appropriate speed.

In this paper, a control system based on adaptive sliding mode theory is designed to track active and reactive powers in a wind turbine with a brushless doubly-fed induction generator. The dynamics of this type of turbine are nonlinear and include parametric uncertainties and perturbations such as wind speed changes. Therefore, to control this nonlinear and uncertain system, sliding model theory has been used. Due to the unknown of the bound of various uncertainties in this system, the adaptive type of sliding mode is used. In this method, the controller gain is considered as a variable with time and is designed to converge to the uncertainties upper bound. The proposed designed controller in a complete simulation in MATLAB software, on the model of a brushless doubly-fed wind turbine, is evaluated and the good performance of this proposed method in comparison with standard sliding model methods and the classical PID controller is shown.

This paper details a new method for model-based discrete sliding mode control of a spark ignition (S.I.) engine cold start considering nonlinear dynamic effects, modeling uncertainty and multiple switched dynamic configurations. Control algorithm deals with stabilization and tracking problems that correspond to crank-shaft velocity, air-to-fuel ratio (AFR) and catalyst temperature in cold start operating mode. To this end, an individual sliding function is assigned to each tracking problem which is subsequently used in construction of associated Lyapunov function. Crank-shaft velocity is regulated using air mass flow rate into intake manifold considering associated dynamic equations describing air mass flow. To ensure precise AFR control, the sliding control model is constructed considering the previously calculated air mass flow rate which leads to nonlinear expressions that are subsequently employed in calculation of appropriate fuel mass flow rate. Stabilization of catalyst temperature is conducted through definition of a virtual control input corresponding to spark angle. As catalyst temperature is described using logarithmic mathematical expressions, additional steps have to be considered to ensure feasibility of control algorithm in various operating conditions. The designed control was numerically applied to validated mathematical model of Toyota 2AZ-FE engine so as to render efficiency and precision of feedback performance.

Owing to an increasing need of control community for providing a precise and integrated model of natural and practical structures switching systems have attracted much attention. On the other hand, the multi-model inherent in many practical systems has increased the importance of reviewing these types of systems. In this paper, the problem of adaptive fault tolerant finite-time control of a class of nonlinear switching systems in the presence of actuator fault, external disturbances and dead-zone input nonlinearity is investigated. The boundary of the uncertain terms of the system is assumed to be unknown and adaptive rules are used to eliminate the destructive effects of these terms on the system response. The subsystems of switching system are considered as nonlinear systems with a canonical structure. This paper sets no restrictive assumption on the switching logic of the system. Therefore, the purpose is to propose a controller that works under any desired switch signal and can overcome the actuator fault, disturbances and dead-zone input nonlinearity. To achieve this purpose, after providing a smooth sliding manifold, an adaptive control input is developed such that the system trajectories approach the prescribed sliding mode dynamics in finite-time sense. Finally, by using the Lyapunov stability theory, it is proved that the origin is the finite-time stable equilibrium point of the overall closed-loop system. The simulation results provided by MATLAB software show the performance of the proposed controller.