Boby, Riby Abraham

Dielectric elastomers (DEs) have significant potential in many applications, particularly in the realm of soft robotics, owing to their capability of undergoing large deformation. The viscoelastic creep response exhibited by dielectric elastomers when subjected to electrostatic input is an inherent characteristic of DEs that hinders their long-term performance and applicability. This behavior leads to time-dependent deformation during actuation, which limits their use as actuators in applications such as robotic grasping. This work presents control techniques that have been developed to minimize viscoelastic creep in dielectric elastomer actuators (DEAs). To simulate the viscoelastic creep behavior of DEAs under dynamic loading, a dynamic modeling framework is presented. The equations derived from the dynamic model are subsequently subjected to the two different control strategies using Linear Quadratic Regulator and Model Predictive Control. Control strategies have been devised to minimize creep and decrease the reaction time. The key aspect of both methods involve the development of an objective function, which is then optimized to determine the ideal control input voltage. The suggested control methods involve applying the fundamental optimal control concepts to an actuator. The actuator response on the application of a single-step input is observed. This principle is then extended to include the actuator response which is a multi-step signal using the proposed control strategy. The results suggest that the control methods are capable of efficiently dealing with the viscoelastic properties of the actuator to achieve different desired equilibrium conditions and hence optimize the performance of the actuator. The simulation results demonstrate that both the control strategies reduce the response time of the DEA by about 98% and also improve the steady-state response. This paper’s findings have the potential to be applied to the development of a control system for the mitigation of viscoelastic creep in DEAs.

Incremental sheet metal forming is a highly versatile die-less forming process for manufacturing complex sheet metal components. Robot-assisted incremental sheet forming, or roboforming, allows a wider range of tool motion, providing the capability to shape intricate components. This makes roboforming the most flexible variant of the incremental forming method. However, the serial arrangement of links and joints in a robotic manipulator results in low positional accuracy under forming loads due to insufficient structural stiffness. The stiffness of the machine frame and tool directly impacts the accuracy of the final formed profile. The impact of machine compliance on component shape in incremental sheet forming is substantial in roboforming. This work presents a methodology for systematic analysis of the factors contributing to the errors in the geometric shape of robot-based forming. Experimental and numerical methods are used to estimate the material springback, tool/tool holder deflections, and errors due to machine compliance. The CNC machine frame is relatively stiffer than the industrial robots, such that material springback is estimated based on the experimental trials on CNC for cone and variable wall angle cone profiles. Tool and tool holder deflections are estimated using finite element simulations. The analytical method using the Virtual Joint Model is used to model the joint stiffness, and consequently, the robot Cartesian stiffness is estimated to predict path deviation contributing to geometric shape errors. The proportional contribution of each factor in the overall deviation in the Roboforming is also quantified.

The automated operation of Unmanned Aerial Vehicles (UAVs) has become increasingly important, and one key step in the procedure is the accurate landing of such vehicles. A major problem is that when trying to design a control algorithm for the landing procedure, it is difficult to accurately measure the 3D pose of the vehicle with respect to the local environment. Three dimensional pose estimation and subsequent corrections in velocity to design the control system was omitted by using an image-based visual servoing controller. Another important development is choosing the appropriate visual features which ensures convergence to desired position. The proposed controller with image moments as features was able to converge and land the vehicle accurately using only the image moments as the visual features from two fiducial markers without any local position or global position information. The proposed method has been verified using simulations considering an existing model of UAV.