This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault Iterative Learning Observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the Iterative Learning Observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

The paper discusses the design of an Observer-based fault-tolerant control algorithm and active vibration control for attitude stailization of a flexible spacecraft (as a rigid-flexible system) subject to external disturbances. An Iterative Learning Observer has been developed in order to estimate the torque deviation caused by actuator faults. One of the main features of the proposed Observer is the consideration of external disturbances in its structure. Next, a fault-tolerant sliding mode control (SMC) law based on a proportional-integral-derivative (PID) structure with a time-varying switching gain is proposed in order to generate control signals with ideal performance. To minimize residual vibrations during and after the maneuver, the strain rate feedback (SRF) control algorithm is also activated simultaneously with fault-tolerant control. Using Lyapunov theory, the proposed control strategies guarantee global stability for the closed loop system. Numerical simulations as a comparative study have been used to demonstrate the effectiveness of the developed system compared to conventional algorithms, such as integral sliding mode control, when handling actuator failures, external disturbances, and flexible body excitations in rigid-flexible dynamic systems.

In this paper, a new type of Iterative Learning control systems with fractional order known as Iterative Learning control with fractional order derivative and Iterative Learning control with fractional proportional–derivative for linearized systems of single-link robot arm is introduced. First order derivative of classic Arimoto is used for tracking error in updating law of derivative Iterative Learning control. The suggested method in this paper implements tracking error for updating control law of Iterative Learning of fractional order. For the first time, nonlinear robot system is linearized by input feedback linearization. Then, convergence analysis of Iterative Learning control law of type PDa is studied. In the next step, we define criteria for parameters optimization of proposed controller by using Biogeography-based optimization algorithm. Both updating laws of fractional order Iterative Learning control (Da-type ILC and PDa-type ILC) are applied on linearized robot arm and performance of both controllers for different value of a is presented. For improving the performance of closed loop system, coefficient of fractional order Iterative Learning control (proportional kp and derivative kD coefficients and a) is optimized by BBO algorithm. Proposed Iterative Learning control is compared with common type of system.

This paper deals with the application of Iterative Learning control (ILC) to further improve performance of bilateral telerobotic systems based on Smith predictor. The aim is to achieve robust stability and optimal transparency for these systems simultaneously. The proposed control structure makes the slave manipulator follow the master in spite of uncertainties in time delays appeared in communication channel and model parameters of master-slave robots, called model mismatch. The time delays are considered to be large, unknown and asymmetric, but the upper bound of the delay is assumed to be known. The main aspect of the proposed controller is that a designer can use the classical controller like proportional-integrator-derivative (PID). However, one of its main difficulties is how to assign proper parameter values for the controller. In other words, the parameters of the controller are not unique and are chosen only to satisfy the stability condition. To solve this problem, in this paper, the local controller is also optimized by backtracking search optimization algorithm (BSA), which is a novel heuristic algorithm with a simple construction. Simulation results illustrate the appropriate performance of the proposed controller.

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.

This study aimed to evaluate the reliability factors, and identify causes of error in direct anthropometry method. After training three beginner anthropometrists and following the instructions of anthropometric standards, 48 body dimensions of 42 male students were measured three times. In other words, the physical dimensions of each subject were measured for 9 times. All participants were wearing uniforms during anthropometry, with bare feet. Differences in values of Repeated Measurement Test were explored using SPSS software version 11. The same software was employed to evaluate, through calculating ICC index, the correlation between anthropometrists. Inter-Observer repeated measurement test showed significant difference in the measurements taken in 3, 7 and 1 dimension (s) by the three anthropometrists. The average measurement was significantly different at 16 dimensions; this, however, showed no difference at 32 dimensions. Measurements taken by anthropometrist 1 had ICC values of 0.26 (Min) and 0.99 (Max); these values were 0.48 (Min) and 1.00 (Max) for anthropometrist 2 and 0.23 (Min) and 0.98 (Max) for antropometrist 3. The maximum and minimum values of ICC index in all three anthropometrists were respectively close to and above 0.98, and lower than 0.5. High value of ICC in the measured dimensions indicated high reliability of repeated measurements. The decreasing value of some indexes can be attributed to such factors as random error, poor design of measurements tool (which in turn leads to random error), the long time devoted to measurement process, high number of dimension measured, changes in posture of subjects and deviation from the standard position.