مجله کنترل
سال سیزدهم شماره 2 (تابستان 1398)

Rehabilitation and assistive systems such as rotary series elastic actuators (RSEA) should provide the desired torque precisely. In this paper, to improve the life quality of those who suffer from weak knees, the control problem of a rotary series elastic actuator (RSEA) has been studied in order to generate soft human walking motion. These actuators produce the require torque, but the nonlinear resistive and inertia loads inherent in the actuators, set challenges to generate the desired torque accurately. The nonlinear resistive factors and uncertainties in plant dynamics which make the precise torque control difficult should be considered. In this paper, a robust controller based on an optimized control approach is designed to enhance control performance and provide the robustness for modeling uncertainties. The simulation is used to compare the output results of the proposed algorithm with the conventional methods such as sliding mode and adaptive-sliding mode controllers.

Induction motors with nonlinear dynamics are superior in terms of size, weight, motor inertia, maximum speed, efficiency, and cost than direct current machines, and hence their control is of great important. The main objective of this paper is to design a fuzzy sliding mode controller in order to control the position of the induction motor including parametric and non-parametric uncertainties by considering the stability issue. In fact, in this method, in order to increase the performance of the control system and to improve the tracking performance, a moving sliding surface is considered, in which it is adapted in accordance with the variations of the sliding surface. As a result, during the reaching phase, the system is not sensitive to parameter variations and external disturbances. Simulation results show that the proposed control method has good performance in the face of parametric and non-parametric uncertainties.

This paper studies the design of a robust output feedback controller subject to actuator saturation. For this purpose, a robust high-gain sliding mode observer is used to estimate the state variables. Moreover, the combination of Composite Nonlinear Feedback (CNF) and Integral Sliding Mode (ISM) controllers are used for robust output tracking. This controller consists of two parts, the CNF part which is taken into account to modify the transient responses and the ISM part which is implemented to reject the disturbances. The two important issues in this paper are: considering the actuator saturation and designing the robust observer-based control law. Moreover, a theorem is given and proved that guarantees even if the actuator saturation takes place, the closed-loop system is stable and the output asymptotically tracks the step reference input. Finally, in order to show the performance of the proposed controller, it is applied to the yaw control of a helicopter and the simulation results verify the theoretical results.

In this paper, a control system is designed for automatic car turning. At first, the necessary information of car turning that were collected from the traffic bylaw, car driving training centers and traffic police are explained. Then, car turning is studied experimentally on several streets with different widths. Afterward, a proper path is designed for the automatic car turning system considering traffic rules and nonholonomic constraint. Also, an appropriate sliding mode controller is designed and a novel fuzzy decision making system is proposed for the automatic car turning system. A car like mobile robot is designed and manufactured based on small scale parameters of a sedan car. Finally, the automatic car turning system is implemented on this car like mobile robot. Simulation and experimental results of the designed control system confirm the effectiveness of the proposed control system.

The delay associated with signal transmission through the wide-area measurement system reduces the functionality of the power oscillation damping control system. One of the important issues is the poor operation of the supplementary controller against delay existence, which limits the efficiency of damping of ancillary equipment, such as SVCs in a power system. This paper as a solution proposes a controller designed based on the stabilizing effect of delay. This controller applies to the SVC input the controlling signal with some delay. To determine the delay and controller parameters, an algorithm is proposed minimizing the rightmost real part of eigenvalues in the design stage. The stability analysis of the control system is performed with the eigenvalue tool. A four-machine power system is used to perform various simulations to assess the accuracy of the proposed control function and the feasibility of the proposed method. The simulation results show that the controller designed in a wide range of the measurement system delays, does not limit damping performance of SVC.

In this paper a new type of neural networks known as Least Squares Support Vector Machines which gained a huge fame during the recent years for identification of nonlinear systems has been used to identify DC motor with nonlinear dead zone characteristics. The identified system after linearization in each time span, in an online manner provide the model data for Model Predictive Controller of position and speed in order to tracking the desired references trajectory. In this method all the cascaded controllers including current, speed and position has been automatically tuned based on the identified model. The offered method has been tested on the servo-drive made specifically for this purpose, and all the results are practically examined and analyzed. The biggest advantage of this method is the self-tuning behavior which insulates the user for tuning any of the controller’s parameters. The online identification of the system provides the possibility to keep track of the changes in dynamics of the system as well as tackling the coulomb’s friction specifically in low speeds with accurate controlling of the speed and position for DC motors.

In this paper, implementation of roll angle and angular velocity estimation algorithm for a high-speed projectile using the fusion of the accelerometers output data is proposed. The reason for the use of accelerometers instead of gyros and magnetometer is the high error of the MEMS gyroscope for high speed and the low accuracy of the magnetometer due to the presence of Non-Earth magnetic fields and the effects of hard and soft iron. After expression of the proposed algorithm, the implementation process is explained. In this process, an electric motor is used to simulate the projectile roll and two accelerometers are used to measure angular velocity and acceleration. Two constant and variable velocity scenarios have been investigated in both online and offline modes. Both extended Kalman Filter and adaptive extended Kalman filter estimators have been used to estimate the rolling angular velocity. Finally, the comparison of these two methods for the rolling angular velocity and roll angle, indicates a better performance for the adaptive estimator.