Iterative learning-based robotic controller with prescribed human-robot interaction force. IEEE Transactions on Automation Science and Engineering.
The present study was conducted to evaluate the toxic potential of five indigenous plant oils: black pepper (Piper nigrum), Chinese cinnamon (Cinnamomum cassia), garlic (Allium sativum), river red gum (Eucalyptus camaldulensis), and yellow oleander (Thevetia peruviana), against laboratory reared Sitophilus oryzae adults. The bioassays were done by the diet incorporation method with concentrations ranging from 50 ppm to 500 ppm. Based on lethal concentrations to kill 50% (LC50) of the subjected weevils, T. peruviana proved to be the most toxic having the lowest LC50 values, 414.58, 201.94, and 129.52 ppm, after 7, 14, and 21 days of exposure, respectively, followed by E. camaldulensis (475.51, 366.65, and 251.28 ppm, respectively). The rest of the plant oils also showed toxic potential, but these were less toxic compared with T. peruviana and E. camaldulensis. With respect to the time taken to cause 50% mortality (LT50) of the exposed weevils, T. peruviana had LT50 at 14.54 days followed by P. nigrum (22.09 days), E. camaldulensis (24.29 days), and C. cassia (28.71 days). Whereas, A. sativum took the longest time (44.47 days) to cause 50% mortality of the exposed weevils. In conclusion, the result revealed toxic potential of tested plant oils, and suggests further studies under simulated‐field conditions should be included in the management plan for S. oryzae.
In robot-assisted rehabilitation, the performance of robotic assistance is dependent on the human user’s dynamics, which are subject to uncertainties. In order to enhance the rehabilitation performance and in particular to provide a constant level of assistance, we separate the task space into two subspaces where a combined scheme of adaptive impedance control and trajectory learning is developed. Human movement speed can vary from person to person and it cannot be predefined for the robot. Therefore, in the direction of human movement, an iterative trajectory learning approach is developed to update the robot reference according to human movement and to achieve the desired interaction force between the robot and the human user. In the direction normal to the task trajectory, human’s unintentional force may deteriorate the trajectory tracking performance. Therefore, an impedance adaptation method is utilized to compensate for unknown human force and prevent the human user drifting away from the updated robot reference trajectory. The proposed scheme was tested in experiments that emulated three upper-limb rehabilitation modes: zero interaction force, assistive and resistive. Experimental results showed that the desired assistance level could be achieved, despite uncertain human dynamics.
One promising function of interactive robots is to provide a specific interaction force to human users. For example, rehabilitation robots are expected to promote patients' recovery by interacting with them with a prescribed force. However, motion uncertainties of different individuals, which are hard to predict due to the varying motion speed and noises during motion, degrade the performance of existing control methods. This paper proposes a method to learn a desired reference trajectory for a robot based on dynamic motion primitives (DMPs) and iterative learning (IL). By controlling the robot to follow the generated desired reference trajectory, the interaction force can achieve a desired value. In our proposed approach, DMPs are first employed to parameterize the demonstration trajectories of the human user. Then a recursive least square (RLS)-based estimator is developed and combined with the Adam optimization method to update the trajectory parameters so that the desired reference trajectory of the robot is iteratively obtained by resolving the DMPs. Since the proposed method parameterizes the trajectories depending on the phrase variable, it removes the essential assumption of traditional IL methods where the iteration period should be invariant, and thus has improved robustness compared with the existing methods. Experiments are performed using an interactive robot to validate the effectiveness of our proposed scheme.
Unmanned Aero Vehicles (UAV) has become a useful entity for quite a good number of industries and facilities. It is an agile, cost effective and reliable solution for communication, defense, security, delivery, surveillance and surveying etc. However, their reliability is dependent on the resilient and stabilizes performance based on control systems embedded behind the body. Therefore, the UAV is majorly dependent upon controller design and the requirement of particular performance parameters. Nevertheless, in modern technologies there is always a room for improvement. In the similar manner a UAV lateral control system was implemented and researched in this study, which has been optimized using Proportional, Integral and Derivative (PID) controller, phase lead compensator and signal constraint controller. The significance of this study is the optimization of the existing UAV controller plant for improving lateral performance and stability. With this UAV community will benefit from designing robust controls using the optimized method utilized in this paper and moreover this will provide sophisticated control to operate in unpredictable environments. It is observed that results obtained for optimized lateral control dynamics using phase lead compensator (PLC) are efficacious than the simple PID feedback gains. However, for optimizing unwanted signals of lateral velocity, yaw rate, and yaw angle modes, PLC were integrated with PID to achieve dynamical stability.
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