Shraddha Barawkar

Bio: Shraddha Barawkar is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Computer science & Admittance. The author has an hindex of 1, co-authored 3 publications receiving 7 citations.

Fuzzy Logic based Variable Damping Admittance Control for Multi-UAV Collaborative Transportation

Abstract: This paper implements a variable damper admittance control in a multi UAV system. Two Unmanned Aerial Vehicles (UAVs) are considered in this work for simplicity, to collaboratively transport a common payload. A leader-follower architecture is used. The leader UAV uses a traditional Proportional, Integral and Derivative (PID) control whereas the follower UAV makes control decision by a force feedback admittance controller. The admittance controller simulates a virtual spring mass damper system to implement a force feedback controller for the follower UAV. It ensures effective force compliance via proper choice of admittance parameters, which are stiffness, mass and damping of a virtual spring mass damper system. However, the performance of the controller can be improved by following a variable damping admittance strategy that allows adaptation of the damping coefficient based on the interaction contact forces and their rates, acting on the follower, due to leaders motion. Calculation of variable damping coefficient is proposed to be carried out using Fuzzy Logic (FL) that utilizes heuristic and intuitive knowledge for calculations. The proposed scheme provides effective performance in comparison to a constant damping admittance scheme, which is validated through the results provided in this paper.

Collaborative Transportation of a Common Payload using Two UAVs Based on Force Feedback Control

Abstract: This research presents a novel approach to perform the task of collaborative transportation by using multiple quadcopter Unmanned Aerial Vehicles (UAVs). Collaborative transportation of a common payload would allow bulky, heavy payloads to be carried via multiple small-sized UAVs enabling their applications such as in emergency evacuations. However, from a control perspective, physical interactions between the UAVs and the payload during collaborative transportation present challenges in terms of stability and accurate trajectory tracking. In this paper, a leader-follower approach is implemented. The leader UAV uses a Proportional, Integral and Derivative (PID) controller to reach the desired goal point or follow a predefined trajectory. Traditionally, a Position Feedback Controller (PFC) has been used in literature to control the follower UAV. PFC takes the feedback of leader UAVs position to obtain the desired trajectory for the follower UAV which is then tracked using a traditional PID controller. Such control schemes have been shown in literature to work effectively in indoor environments using reliable and accurate positional information obtained from motion tracking cameras. However, the research focuses on outdoor application, that requires usage of Global Positioning System (GPS) to receive the positional information of the leader UAV. GPS has inherent errors of order of magnitude that can destabilize the system. The control scheme proposed in this research addresses this major limitation. In this research, a Force Feedback Controller (FFC) is used to control the follower UAV. The FFC provides control based on the interaction forces and torques acting at the follower UAV due to leader UAVs motion. Two control schemes are implemented to develop this FFC. They are Fuzzy Logic (FL) and admittance control respectively. FL emulates human behavior during such collaborative lift. Admittance controller simulates a virtual spring mass damper system, to generate a desired trajectory for the follower UAV. This generated trajectory complies with the contact forces acting on the follower UAV and it is then tracked by a traditional PID controller. With the proposed control schemes, the follower UAV can be controlled without using leaders positional feedback and the system can be implemented for real-world applications. Results of numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking are presented. The results are compared to the benchmarked PFC implemented for the system.

Adaptive control of a UAV with continuously time varying center of gravity

Abstract: In this paper we apply the concept of adaptive control to an Unmanned Aerial Vehicle (UAV) with time varying center of gravity (CG). Continuous change of payload mass and CG's location happens in several real-world applications, e.g., transportation of a package which has moving parts in it or a UAV spraying disinfectant or pesticide. Control of UAVs with varying CG location is challenging since the changed dynamics require adaptation in controller parameters for stable and effective flight. The research carried out in literature have focused on solution for an offset (but fixed) CG or for CG varying at certain time instants. However, for scenarios in which the CG continuously varies with time, existing schemes are ineffective. We first derive the dynamics of the entire system with time varying CG. Following this, the proposed feedback linearization and adaptive control strategies are presented. Extensive results from numerical simulation for trajectory tracking and waypoint navigation are provided for constant CG versus time varying CG cases. The results demonstrate the effectiveness of the proposed work over existing work on constant CG case.

A Flying Inverted Pendulum With Unknown Length

Abstract: Balancing an inverted pendulum on an unmanned aerial vehicle has been a topic of interest in recent literature. For example, a recent study [1] uses an LQR controller to balance the inverted pendulum on a quadrotor drone. However, these studies consider the length of the pendulum to be known a priori. Indeed, in certain applications this assumption might not hold true. For example, consider a quadrotor hoverboard being used by people of different heights. In such cases, an approach is required to estimate the length of the pendulum. This paper analyzes the linearized dynamics of the combined system of quadrotor and inverted pendulum. It is found that unknown length of pendulum causes the system to fall in the category of unmatched uncertain systems where the control input cannot be used to cancel the uncertainty. This paper formulates the problem in such a manner that the system is still controllable in presence of this unmatched uncertainty. A concurrent learning adaptive controller, which avoids the use of persistently exciting signals, is then utilized to estimate the unmatched uncertainty and hence the length of the pendulum. Simulation results validate the effectiveness of the adaptive controller for the proposed problem.

Fuzzy Logic based Variable Damping Admittance Control for Multi-UAV Collaborative Transportation

Abstract: This paper implements a variable damper admittance control in a multi UAV system. Two Unmanned Aerial Vehicles (UAVs) are considered in this work for simplicity, to collaboratively transport a common payload. A leader-follower architecture is used. The leader UAV uses a traditional Proportional, Integral and Derivative (PID) control whereas the follower UAV makes control decision by a force feedback admittance controller. The admittance controller simulates a virtual spring mass damper system to implement a force feedback controller for the follower UAV. It ensures effective force compliance via proper choice of admittance parameters, which are stiffness, mass and damping of a virtual spring mass damper system. However, the performance of the controller can be improved by following a variable damping admittance strategy that allows adaptation of the damping coefficient based on the interaction contact forces and their rates, acting on the follower, due to leaders motion. Calculation of variable damping coefficient is proposed to be carried out using Fuzzy Logic (FL) that utilizes heuristic and intuitive knowledge for calculations. The proposed scheme provides effective performance in comparison to a constant damping admittance scheme, which is validated through the results provided in this paper.

Modeling and Control of an Aerial Multi-Cargo System: Robust Acquiring and Transport Operations

Abstract: In this paper, we introduce the concept of a multilink unmanned aerial system (ML-UAS) designed for multi-cargo transportation tasks. Such system features three links who are actuated by four flying robots. We present in detail the dynamics modeling based on the Euler-Lagrange formulation. A preliminary control scheme based on robust Adaptive Integral Sliding Mode Control (AISMC) is applied considering the ML-UAS model uncertainties and external disturbances. The control objective is to follow/track avian-inspired acquiring patterns for acquiring operations. The effectiveness of the proposed strategy is validated by numerical simulations.

Disturbances and Coupling Compensation for Trajectory Tracking of a Multi-link Aerial Robot

Abstract: The actual paper presents the concept of a multi-link unmanned aerial system (ML-UAS) intended to transport multi-cargo payload. The mathematical model is obtained through the Euler-Lagrange energy-based while the controller relies on a classical linear scheme. The system is composed of three rotorcrafts attached by two bar like links. As the system is highly coupled, due to its inherent dynamics and cargo influences, a dynamic extension of the equations of motion to apply a Linear Kalman filter is proposed to meet the trajectory tracking specification. The suggested state observer is validated via close to reality numerical simulations.

Nonlinear Control of a Multilink Aerial System and ASEKF-Based Disturbances Compensation

Abstract: The actual article presents the modeling and control of a multilink unmanned aerial system whose dynamics is computed by means of the Euler–Lagrange approach. The aforementioned system is subjected to lumped disturbances, which comprise external disturbances and parametric uncertainties. An augmented-state extended Kalman filter intended to estimate endogenous and exogenous uncertainties is conceived and a trajectory-tracking controller fulfilling Lyapunov asymptotic stability is synthesized. A simulation stage is conducted to validate the effectiveness of the proposal.

Robotic Assistance for Physical Human–Robot Interaction Using a Fuzzy RBF Hand Impedance Compensator and a Neural Network Based Human Motion Intention Estimator

Abstract: This paper proposes a robotic assistance control scheme for intuitive teaching tasks by integrating the motion intention of human and real-time hand impedance compensation based upon a fuzzy RBF (Radial Basis Function) compensator. The motion intention of a human is estimated using a feedforward neural network. The parameters of the proposed fuzzy RBF hand impedance compensator are adjusted by considering hand impedance and robot dynamics. Three robotic assistance control schemes are compared: 1) robot without an impedance compensator; 2) robot with a constant-parameter impedance compensator; 3) robot with a fuzzy RBF hand impedance compensator. Several experiments have been conducted to verify the effectiveness of the proposed approach by comparing the contour error, exerted force and task time spent on teaching tasks. Experimental results indicate that the proposed fuzzy RBF hand impedance compensator has the best assistance results among the tested robotic assistance control schemes.