ardashir mohammadzadeh

Parallel robots are widely used in many industrial and medical applications. Reconfigurable parallel robots could be defined as a group of parallel robots that can have different geometries, thus obtaining different degrees of freedom derived from the basic structure. These robots have some disadvantages like having erratic workspace and singular points in the workspace. These limitations should be studied for proper usage of parallel manipulators. This paper presents the kinematics and workspace analysis of a 3DOF parallel reconfigurable robot. This robot has two different configurations. The first configuration is a Tricept robot (3UPS-PU) and the second is a fully Spherical robot (3UPS-S). The kinematic equations are derived based on the geometry of the system and then Jacobian matrices are determined via velocity loop closure analysis. The kinematic model is verified by the results obtained from robot simulation in ADAMS software. Then, the workspace of the robot is determined by considering the kinematic constraints.

The goal of the present research is to propose a novel adaptive fractional order PID (AFOPID) controller whose parameters are tuned online by five exclusive multilayer perceptron (MLP) neural networks using the extended Kalman filter (EKF). An MLP neural network that is trained using the Back Propagation (BP) error algorithm is considered to identify the structural system and estimate the plant. The Jacobian of the model estimated online is utilized to apply to the controller. Considering the adaptive interval type-2 fuzzy neural networks (IT2FNN) and this issue that the compensator is tunned by EKF and feedback error learning strategy (FEL), the stability and robustness of this controller are increased against the estimation error, seismic disturbances, and some unknown nonlinear functions. In order to validate, the performance of the proposed controller is investigated on a 3-story nonlinear benchmark building equipped with semi-active dampers under far and near field earthquakes. In order to evaluate the effectiveness of the proposed controller equipped with a compensator in reducing seismic responses, the evaluation indices were discussed and compared with previous studies. The numerical results represent the substantial efficiency of the proposed adaptive controller (AFOPID) over the previous controllers such that J2 in the Hachinohe and Northridge earthquakes enhanced by up to 35% and more than 40%, respectively. In general, all indices ( J3  to J6 ) have experienced a considerable enhancement using the proposed method.

In the present research, design of a strong and online adaptive controller in the active cable control system is discussed to overcome the earthquake vibrations of multi-story buildings. Considering all variables as unknown, this study introduces a new type 2 adaptive neuro-fuzzy controller. Using the MLP neural network (multi-layer perceptrons), Jacobian and the structural system estimation are extracted. This estimated structural system model is implemented into the online controller system in the next step. Adaptive controllers are tuned using a post-propagation algorithm and Extended Kalman Filter and are thus able to control and tune the controllers and the cable system. In this method, a PID controller is also used, which increases the strength and stability of the adaptive neural-fuzzy controller system two against earthquake vibrations. The superiority of the proposed controller system over an online simple adaptive controller is also demonstrated. This controller is utilized as an implicit reference model. In this proposed method, Extended Kalman Filter is innovatively used to tune online controllers. In this research, the performance of both controllers is investigated under the far and near fault field pressures. Based on the numerical results, the adaptive neural-fuzzy controller performs about 21% better than the online simple adaptive controller in minimizing the seismic responses of the structure during an earthquake and reaching the control criteria when the parametric characteristics of the structure change.

One of the main tasks of the control systems in the wind turbines is to maintain the power of the wind when its wind speed proceed its nominal value. Because the failure to maintain the power in its nominal value in the region of the turbine curve damages the turbine and increases the mechanical stress. This object is obtained by controlling the pitch angle in the third region of the turbine curve. In this study, a fuzzy fractional-order PID controller is designed to regulate pitch angle in the wind turbines and its efficiency is compared with conventional PID and fractional-order PID controllers. The proposed control method is applied to a 100kW wind turbine under a variable wind speed. The simulations results show that the value of the root mean squre error for the proposed control method is less than the conventional PID and fractional order PID controllers, and also the proposed control scheme results in smoother and better control signals, output power and generator speed in third region of performance.

This paper proposes an adaptive control method based on the feedback linearization technique and a proposed neural network,  for tracking and position control of an industrial manipulator. At first, it is assumed that the dynamics of the system are known and the control signal is constructed  by the feedback linearization method. Then to eliminate the effects of the uncertainties and external disturbances, the parameters of the proposed neural network are learned based on the Lyapunov method such that the sliding condition to be satisfied. The performance of the proposed method is compared with the sliding mode technique in presence of external disturbance, delay and uncertainties. The simulation results verify that the proposed method is effective and can be used in many applications.