جستجوی مقالات مرتبط با کلیدواژه « کنترل مقاوم تطبیقی » در نشریات گروه « مکانیک »

Piezoelectric actuators are the most common choice for position control with ultra-high precision. Despite the significant advantages, the linear and nonlinear dynamics of these actuators, such as hysteresis, could decrease the precision of the control system. In this research, a controller based on the sliding mode method is proposed for position control of piezoelectric actuator. Sliding mode control is a model-based and useful method in nanopositioning systems. In this research, Bouc-Wen model is used for description of the actuator’s behavior. In this model, the linear dynamic is modeled with mass, stiffness and damping terms, and the hysteresis is modeled by its nonlinear dynamics. Usually, there are mismatch and uncertainty between the physical system and mathematical model. For stability analysis of the prevalent sliding mode control, the upper bound of uncertainty must be known. But, in practical systems, this is not possible, simply. On the other hand, selecting the large values for this bound, increases the controller gain and distances it from the optimum value. The proposed adaptive robust control eliminates the dependency to the upper bound of uncertainty. This is done by introducing an online adaptive law for estimating this bound. Proposing this law, asymptotic stability of the closed-loop control system is proven. Implementing the presented method on the laboratory setup and simulator software, its effectiveness is shown by simulation and experimental results.

In this paper, an optimal adaptive sliding mode controller of an Omni-Directional Mobile Robot (ODMR) is proposed using harmony search algorithm. First, kinematic model of the robot is derived and then, governing equations of dynamic model have been obtained using linear and angular momentum equilibrium. Since the derived model is not an exact definition of the system, it includes some uncertainties. To compensate them, a tracking control method has been offered. The proposed controller consists of an approximately known inverse dynamic model output as the model-based part of the controller, an estimated uncertainty term to compensate for the un-modeled dynamics, external disturbances, and time-varying parameters to enhance closed-loop stability and account for the estimation error of the uncertainties. In order to compare the results of the proposed controller, an optimal feedback linearization and sliding mode controllers are designed and then, a cost function has been defined by combining the variation rate of control signal and the integral error index. This cost function has been minimized using harmony search algorithm, resulting in optimum control parameters. Finally, the performance of the designed controller in different conditions, such as in presence of disturbance and system parameter variation has been simulated and discussed.

In this paper, constraint equations are derived based on the kinematic model of the robot and Lagrange method is applied to derive the dynamic equations. In order to control the robot position on planned reference trajectories, in presence of uncertainties of the dynamic model, an adaptive robust controller with uncertainty estimator is designed which is robust against the uncertainties and induced noises. The proposed controller consists of an approximately known inverse dynamics model output as model-based part of the controller, an estimated uncertainty term to compensate for the un-modeled dynamics, external disturbances, and time-varying parameters, and also a decentralized PID controller as a feedback part to enhance closed-loop stability and account for the estimation error of uncertainties. Performance of the designed controller is simulated and evaluated in different conditions including the presence of noise and parameters variation. In this regard, a comparison has been made between the response of the proposed adaptive robust controller and response of a feedback linearization controller, indicating their capabilities in noise rejection and compensation of parameters variation. Also, the results show that the proposed sliding mode controller has a desirable performance in tracking the reference trajectories in presence of the model uncertainties and noises for this kind of parallel mechanism.