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The main purpose of the iterative learning control (ILC) method is to reduce the trajectory tracking error caused by an inaccurate model of the robot's dynamics. It estimates the tracking error and applies a learning operator to the output control signals to correct them. Today's ILC researchers are suggesting strategies for increasing the ILC's overall performance and minimizing the number of iterations required. When a payload (or a different end effector) is attached to a robotic manipulator, the dynamics of the robot change. When a new payload is added, even the most accurately approximated model of the dynamics will be altered. This will necessitate changes to the dynamics estimates, which may be avoided if the ILC process is used to control the system. When robotic manipulators are considered, this study analyses how the payload affects the dynamics and proposes ways to improve the ILC process. The study looks at the dynamics of a SCARA-type robot. Its inertia matrix is determined by the payload attached to it. The results show that the ILC method can correct for the estimated inertia matrix inaccuracy caused by the changing payload but at the cost of more iterations. Without any additional data of the payload's properties, the suggested technique may adjust and fine-tune the learning operator. On a preset reference trajectory, the payload adaptation process is empirically tested. When the same payload is mounted, the acquired adaptation improvements are then utilized for another desired trajectory. A computer simulation is also used to validate the suggested method. The suggested method increases the overall performance of ILC for industrial robotic manipulators with a set of similar trajectories but different types of end effectors or payloads.

Robotics is an interdisciplinary field and there exist several well-known approaches to represent the dynamics model of a robot arm. The robot arm is an open kinematic chain of links connected through rotational and translational joints. In the general case, it is very difficult to obtain explicit expressions for the forces and the torques in the equations where the driving torques of the actuators produce desired motion of the gripper. The robot arm control depends significantly on the accuracy of the dynamic model. In the existing literature, the complexity of the dynamic model is reduced by linearization techniques or techniques like machine learning for the identification of unmodelled dynamics. This paper proposes a novel approach for deriving the equations of motion and the actuator torques of a robot arm with an arbitrary number of joints. The proposed approach for obtaining the dynamic model in closed form employs graph theory and the orthogonality principle, a powerful concept that serves as a generalization for the law of conservation of energy. The application of this approach is demonstrated using a 3D-printed planar robot arm with three degrees of freedom. Computer experiments for this robot are executed to validate the dynamic characteristics of the mathematical model of motion obtained by the application of the proposed approach. The results from the experiments are visualized and discussed in detail.

The industrial robotic manipulators have to execute trajectory movements repetitively and with high trajectory tracking precision. In most cases the model parameters of the dynamical model of the robotic system cannot be calculated precisely and there is always an uncertainty in the model of its dynamics. This requires that the robotic control system combines multiple control strategies in order to achieve high trajectory tracking precision. This research investigates how genetic algorithm (GA) can be applied for estimation of the parameters of the mathematical model of a robotic manipulator. The GA requires definition of a solution domain and a fitness function for evaluation of the solution domain. The Constrained Output Iterative Learning Control (COILC) is a biologically inspired method which uses consecutive iterative executions of the same trajectory and uses a learning operator in order to reduce the overall trajectory tracking error. It is proven to be robust and to generate a convergent process when the correct estimation of the dynamics model parameters of the robot are incorporated within the ILC learning operator. The COILC method can be applied safely to robotic manipulators with state space constraints even if the learning operator is randomly chosen. This allows the set of possible dynamics model parameters to be selected as the solution domain and the COILC method to be used as a fitness function for the GA. This leads to an algorithm for estimation of the model parameters of the robot which are best suitable for using an ILC method. This approach is validated through a computer simulation.

The Iterative Learning Control (ILC) can be applied within the control of industrial robots with uncertainties in their mathematical model for accomplishing precise trajectory tracking. The method calculates the tracking error and corrects the output control signals in accordance with a predefined learning operator. This research will focus on the influence of the ILC learning operator over the performance of the ILC. The proposed in this preliminary study algorithm for Self-adaptive Constrained Output ILC (SCOILC) will try to analyze how the learning operator can be optimized for the given task and afterwards to use this analysis in order to enhance the ILC performance when the task is slightly changed. The algorithm extends the convergent Constrained Output ILC (COILC) method. SCOILC adds an initial step of regression analysis of the executions of the COILC for selected trajectory with different learning operators. This allows the ILC procedure to self-adapt to the specific type of trajectories needed for execution of a specific domain of tasks. The conducted experiments led to a conclusion that this initial adaptation process reduces the overall convergence time of the ILC when it is applied to a modification of the trajectory which was used during the adaptation step.

This paper considers the hard constraints in the movement of the joints of robotic manipulators executing repetitive tasks in the presence of measurement noise in the dynamic model. In our research work we have established that subject to these conditions the Iterative Learning Control (ILC) is one of the best methods performing such tasks with high precision. The existing applications of ILC don’t take in consideration the inherent nonlinearity of the dynamic model of a robotic manipulator neither the hard constraints in state space. This paper presents a robust and convergent Constrained Output ILC (COILC) method for non-linear hard constrained systems like industrial robots. Unlike known ILC methods the COILC employs a novel algorithm to cancel the currently executing iteration before the occurrence of a violation in any of the state space hard constraints. This way COILC resolves both the hard constraints in the state space and the transient growth problem, which is a major obstacle in applying ILC to non-linear systems. The convergence and the performance of the proposed numerical procedure are experimentally evaluated through computer simulation of the well-known SCARA-type robot TT-3000. The obtained results are discussed and demonstrate that COILC is suitable for solving hard constrained state space problems of non-linear systems in robotics with certain inaccuracies in the dynamic model.

The purpose of our study is to research the usability of a tele-controlled service robot by performing real tests of the robot with the elderly. Robot control is based on a multichannel system for data distribution from external devices, such as – joystick, virtual joystick, microphone, Kinect, and Leap Motion to provide effective assistance of the elderly for their different needs. In the paper, there are described the functionalities of the robot ROBCO 17 and the principles of the performing of the experiments. Finally, we present the results of the impressions of the elderly and their recommendations for future upgrades of the service robot.