Publications

Abstract not provided.

Robot manipulation of the environment often uses force feedback control approaches such as impedance control. Impedance controllers can be designed to be passive and work well while coupled to a variety of dynamic environments. However, in the presence of a high gear ratio and compliance in manipulator links, non-passive system properties may result in force feedback instabilities when coupled to certain environments. This necessitates an approach that ensures stability when using impedance control methods to interact with a wide range of environments. We propose a method for improving stability and steady-state convergence of an impedance controller by using a deep neural network to map a damping impedance control parameter. In this paper, a dynamic model and impedance controlled simulated system are presented and used for analyzing the coupled dynamic behavior in worst case environments. This simulation environment is used for Nyquist analysis and closed-loop stability analysis to algorithmically determine updated impedance damping parameters that secures stability and desired performance. The deep neural network inputs utilized present impedance control parameters and environmental dynamic properties to determine an updated value of damping that improves performance. In a data set of 10,000 combinations of control parameters and environmental dynamics, 20.3% of all the cases result in instability or do not meet convergence criterion. Our deep neural network improves this and reduces instabilities and failed control performance to 2.29%. The design of the network architecture to achieve this improvement is presented and compared to other architectures with their respective performances.

The broad dissemination of unmanned aerial vehicles (UAV s), specifically quadrotor aircraft, has accelerated their successful use in a wide range of industrial, military, and agricultural applications. Research in the growing field of aerial manipulation (AM) faces many challenges but may enable the next generation of UAV applications. The physical contact required to perform AM tasks results in dynamic coupling with the environment, which may lead to instability with devastating consequences for a UAV in flight. Considering these concerns, this work seeks to determine whether off-the-shelf flight controllers for quadrotor UAV s are suitable for AM applications by investigating the passivity and coupled-stability of quad rotors using generic cascaded position-attitude (CPA) and PX4 flight controllers. Using a planar 3-degree of freedom (DOF) linearized state-space model and two high fidelity 6-DOF models with the CPA and PX4 closed-loop flight controllers, passivity is analyzed during free flight, and stability is analyzed when the UAV is coupled to environments with varying degrees of stiffness. This analysis indicates that quadrotors using the CPA and PX4 flight controllers are non-passive (except for the PX4 controller in the vertical direction with certain vehicle parameters) and may become unstable when the UAV is coupled with environments of certain stiffnesses. Similarities between the results from the linearized 3-DOF model and nonlinear 6-DOF models in the passivity analysis suggest that using an analytical, linear approach is sufficient and potentially useful for vehicle geometry and controller design to improve stability for AM applications.

Wellbore integrity is a significant problem in the U.S. and worldwide, which has serious adverse environmental and energy security consequences. Wells are constructed with a cement barrier designed to last about 50 years. Indirect measurements and models are commonly used to identify wellbore damage and leakage, often producing subjective and even erroneous results. The research presented herein focuses on new technologies to improve monitoring and detection of wellbore failures (leaks) by developing a multi-step machine learning approach to localize two types of thermal defects within a wellbore model, a prototype mechatronic system for automatically drilling small diameter holes of arbitrary depth to monitor the integrity of oil and gas wells in situ, and benchtop testing and analyses to support the development of an autonomous real-time diagnostic tool to enable sensor emplacement for monitoring wellbore integrity. Each technology was supported by experimental results. This research has provided tools to aid in the detection of wellbore leaks and significantly enhanced our understanding of the interaction between small-hole drilling and wellbore materials.

Abstract not provided.

Artificial muscles (AMs) traditionally rely on pneumatic sources of fluid power. The use of hydraulics can increase the power and force to weight and volume ratios of AM actuators. This paper develops a control-centric third-order single-input single-output (SISO) lumped-parameter dynamic model and sliding mode position controller based on Filippov's principle of equivalent dynamics for a braided hydraulic artificial muscle (HAM) actuator. The model predicts the nonlinear behavior of the HAM free contraction and captures the fluid and actuator nonlinear dynamic interactions in addition to the braid deformation. Model simulations are compared to experimental results for quasi-static pressurization, isometric pressurization, and open-loop square wave commands at 0.25, 0.5, and 1 Hz. Experiments of sine wave tracking at 0.25, 0.5, and 1 Hz and continuous square wave tracking at 0.067 Hz are conducted using a sliding mode controller (SMC) derived from the model. The SMC achieves a steady-state error of 6 lm at multiple setpoints within the actuator's 17 mm stroke. Compared to a proportional-integral-derivative (PID) controller, the SMC root-mean-square (RMS) error, mean error, and absolute maximum error are reduced on average by 53%, 61%, and 44%, respectively, demonstrating the benefit of model-based approaches for controlling HAMs.

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Abstract not provided.

We describe the development and benchtop prototype performance characterization of a mechatronic system for automatically drilling small diameter holes of arbitrary depth, to enable monitoring the integrity of oil and gas wells in situ. The precise drilling of very small diameter, high aspect ratio holes, particularly in dimensionally constrained spaces, presents several challenges including bit buckling, limited torsional stiffness, chip clearing, and limited space for the bit and mechanism. We describe a compact mechanism that overcomes these issues by minimizing the unsupported drill bit length throughout the process, enabling the bit to be progressively fed from a chuck as depth increases. When used with flexible drill bits, holes of arbitrary depth and aspect ratio may be drilled orthogonal to the wellbore. The mechanism and a conventional drilling system are tested in deep hole drilling operation. The experimental results show that the system operates as intended and achieves holes with substantially greater aspect ratios than conventional methods with very long drill bits. The mechanism enabled successful drilling of a 1/16" diameter hole to a depth of 9", a ratio of 144:1. Dysfunctions prevented drilling of the same hole using conventional methods.

Abstract not provided.

Abstract not provided.