Robust Differentiable Predictive Control with Safety Guarantees: A Predictive Safety Filter Approach

In this paper, we propose a novel predictive safety filter that is robust to bounded perturbations and is combined with a learning-based control called differentiable predictive control (DPC). The proposed method provides rigorous guarantees of safety in the presence of bounded perturbations and implements DPC so long as the DPC control satisfies the system constraints. The approach also incorporates two forms of event-triggering to reduce online computation. The approach is comprised of a robust predictive safety filter that extends upon existing work to reject disturbances for discrete-time, time-varying nonlinear systems with time-varying constraints. The safety filter is based on novel concepts of robust, discrete-time barrier functions and can be used to filter any control law. Here we use the safety filter in conjunction with DPC as a promising policy optimization method. The approach is demonstrated on a single-integrator, two-tank system, and building example.Comment: Submitted to Automatic

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach

We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems. In DPC, the predictive control policy parametrized by a neural network is optimized offline via direct policy gradients obtained by automatic differentiation of the MPC problem. The proposed approach exploits a new form of sampled-data barrier function to enforce offline and online safety requirements in DPC settings while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation.Comment: Accepted to IEEE Conference on Decision and Control Conference 202

Energy-Aware Path Planning for UAS Persistent Sampling and Surveillance

The focus of this work is to develop an energy-aware path planning algorithm that maximizes UAS endurance, while performing sampling and surveillance missions in a known, stationary wind environment. The energy-aware aspect is specifically tailored to extract energy from the wind to reduce thrust use, thereby increasing aircraft endurance. Wind energy extraction is performed by static soaring and dynamic soaring. Static soaring involves using upward wind currents to increase altitude and potential energy. Dynamic soaring involves taking advantage of wind gradients to exchange potential and kinetic energy. The path planning algorithm developed in this work uses optimization to combine these soaring trajectories with the overarching sampling and surveillance mission. The path planning algorithm uses a simplified aircraft model to tractably optimize soaring trajectories. This aircraft model is presented and along with the derivation of the equations of motion. A nonlinear program is used to create the soaring trajectories based on a given optimization problem. This optimization problem is defined using a heuristic decision tree, which defines appropriate problems given a sampling and surveillance mission and a wind model. Simulations are performed to assess the path planning algorithm. The results are used to identify properties of soaring trajectories as well as to determine what wind conditions support minimal thrust soaring. Additional results show how the path planning algorithm can be tuned between maximizing aircraft endurance and performing the sampling and surveillance mission. A means of trajectory stitching is demonstrated to show how the periodic soaring segments can be combined together to provide a full solution to an infinite/long horizon problem

Robust Object Manipulation for Fully-Actuated Robotic Hands

© 2019 Wenceslao Eric Shaw CortezObject manipulation is the ability to rotate/translate an object held within a grasp. Humans have exploited this ability to effectively use tools and interact with the environment. Over the past decades, robotics research has worked to translate object manipulation capabilities to robotic hands. Applications of object manipulation for robotic hands include autonomous manipulation, teleoperation in extreme environments, and prosthetics. Despite advancements made, robotic hand research has not yet progressed to handle uncertainties found in the real world. Many existing grasp methods to control robotic hands require a priori information and high fidelity sensors typically restricted to laboratory settings. The objective of this thesis is to develop robust means of object manipulation for robotic hands. This thesis focuses on the concept of tactile-based blind grasping to address robustness concerns in real-world applications. In tactile-based blind grasping, the robotic hand only has access to proprioceptive (joint angle) and tactile measurements. No a priori information about the object is known. This reflects real-world applications, such as prosthetics, where disturbances in the form of uncertain object models are part of everyday use. In this dissertation, novel object manipulation control methods are developed for robotic hands in tactile-based blind grasping. The first method ensures stability of the hand-object system to a desired object pose despite uncertain object weight, shape, center of mass, and contact locations. The second method is an extension of the first, but also ensures the contact points do not slip during the manipulation motion. The final control addresses all grasp conditions that must be satisfied, including slip, to ensure the grasp does not fail during manipulation. This final control is applicable not only to the control methods presented here, but to most manipulation controllers developed in the literature. The proposed controllers are presented with associated stability guarantees and validated in simulation and hardware

High-order Barrier Functions : Robustness, Safety and Performance-Critical Control

In this paper, we propose a notion of high-order (zeroing) barrier functions that generalizes the concept of zeroing barrier functions and guarantees set forward invariance by checking their higher order derivatives. The proposed formulation guarantees asymptotic stability of the forward invariant set, which is highly favorable for robustness with respect to model perturbations. No forward completeness assumption is needed in our setting in contrast to existing high order barrier function methods. For the case of controlled dynamical systems, we relax the requirement of uniform relative degree and propose a singularity-free control scheme that yields a locally Lipschitz control signal and guarantees safety. Furthermore, the proposed formulation accounts for ``performance-critical" control: it guarantees that a subset of the forward invariant set will admit any existing, bounded control law, while still ensuring forward invariance of the set. Finally, a non-trivial case study with rigid-body attitude dynamics and interconnected cell regions as the safe region is investigated.QC 20220317</p

Adaptive Cooperative Manipulation with Rolling Contacts

In this paper we present a novel adaptive cooperative manipulation controller for multiple mobile robots with rolling contacts. Our approach exploits rolling effects of passive end-effectors and does not require force/torque sensing. Moreover, the proposed scheme is robust to uncertain dynamics of the object and agents including object center of mass, inertia, weight, and Coriolis terms. In addition, we present a novel closed-form internal force controller that guarantees no slip throughout the manipulation task. The adaptive controller design ensures boundedness of the estimated model parameters in predefined sets. Numerical simulations validate the effectiveness of the proposed approach.QC 20210401</p

A Distributed, Event-Triggered, Adaptive Controller for Cooperative Manipulation With Rolling Contacts

We present a distributed, event-triggered, and adaptive control algorithm for cooperative object manipulation withrolling contacts and unknown dynamic parameters. Whereasconventional cooperative manipulation methods require rigidcontact points, our approach exploits rolling effects of passiveend-effectors and does not require force/torque sensing. Theremoval of rigidity allows for more modular grasping, increasedapplication to more object types, and online adjustment ofthe grasp. The proposed control algorithm exhibits the following properties. Firstly, it is distributed, in the sense thatthe robotic agents calculate their own control signal, under anevent-triggered communication scheme. Such a scheme reducesthe inter-agent communication requirements with respect tocontinuous communication schemes. Secondly, it uses an onlineadaptation mechanism to accommodate for unknown dynamicparameters of the object and the agents. Finally, it adaptsexisting internal force controllers to guarantee no slip throughoutthe manipulation task despite the event-triggered nature of thecommunication scheme. Hardware implementation validates theeffectiveness of the proposed approach.QC 20230511</p