More Like this

1. A hybrid feedback control strategy for autonomous waypoint transitioning and loitering of unmanned aerial vehicles

   Smith, Dean A; Sanfelice, Ricardo G (November 2017, Nonlinear analysis. Hybrid systems)

   We consider the problem of autonomously controlling a fixed-wing aerial vehicle to visit a neighborhood of a pre-defined waypoint, and when nearby it, loiter around it. To solve this problem, we propose a hybrid feedback control strategy that unites two state-feedback controllers: a transit controller capable of steering or transitioning the vehicle to nearby the waypoint and a loiter controller capable of steering the vehicle about a loitering radius. The aerial vehicle is modeled on a level flight plane with system performance characterized in terms of the aerodynamic, propulsion, and mass properties. Thrust and bank angle are the control inputs. Asymptotic stability properties of the individual control algorithms, which are designed using backstepping, as well as of the closed-loop system, which includes a hybrid algorithm uniting the two controllers, are established. In particular, for this application of hybrid feedback control, Lyapunov functions and hybrid systems theory are employed to establish stability properties of the set of points defining loitering. The analytical results are confirmed numerically by simulations. 

   more » « less
2. Learning Approximate Forward Reachable Sets Using Separating Kernels

   Thorpe, Adam; Ortiz, Kendric; and Oishi, Meeko M. (January 2021, Proceedings of Machine Learning Research)

   null (Ed.)

   We present a data-driven method for computing approximate forward reachable sets using separating kernels in a reproducing kernel Hilbert space. We frame the problem as a support estimation problem, and learn a classifier of the support as an element in a reproducing kernel Hilbert space using a data-driven approach. Kernel methods provide a computationally efficient representation for the classifier that is the solution to a regularized least squares problem. The solution converges almost surely as the sample size increases, and admits known finite sample bounds. This approach is applicable to stochastic systems with arbitrary disturbances and neural network verification problems by treating the network as a dynamical system, or by considering neural network controllers as part of a closed-loop system. We present our technique on several examples, including a spacecraft rendezvous and docking problem, and two nonlinear system benchmarks with neural network controllers. 

   more » « less
3. Flow-Based Rendezvous and Docking for Marine Modular Robots in Gyre-Like Environments

   Knizhnik, G.; Li, P.; Yim, M.; Hsieh, M. A. (April 2023, IEEE International Conference on Robotics and Automation (ICRA2023))

   Modular self-assembling systems typically assume that modules are present to assemble. But in sparsely observed ocean environments, modules of an aquatic modular robotic system may be separated by distances they do not have the energy to cross, and the information needed for optimal path planning is often unavailable. In this work we present a flow-based rendezvous and docking controller that allows aquatic robots in gyre-like environments to rendezvous with and dock to a target by leveraging environmental forces. This approach does not require complete knowledge of the flow, but suffices with imperfect knowledge of the flow's center and shape. We validate the performance of this control approach in both simulations and experiments relative to naive rendezvous and docking strategies, and show that energy efficiency improves as the scale of the gyre increases. 

   more » « less
4. Verisig: verifying safety properties of hybrid systems with neural network controllers

   https://doi.org/10.1145/3302504.3311806  

   Ivanov, Radoslav; Weimer, James; Alur, Rajeev; Pappas, George J.; Lee, Insup (April 2019, HSCC 2019)

   This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. We focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid system. By composing the network's hybrid system with the plant's, we transform the problem into a hybrid system verification problem which can be solved using state-of-the-art reachability tools. We show that reachability is decidable for networks with one hidden layer and decidable for general networks if Schanuel's conjecture is true. We evaluate the applicability and scalability of Verisig in two case studies, one from reinforcement learning and one in which the neural network is used to approximate a model predictive controller. 

   more » « less
5. A Hybrid PID Design for Asymptotic Stabilization with Intermittent Measurements

   https://doi.org/10.1109/CDC.2018.8619150  

   Lavell, Daniel; Phillips, Sean; Sanfelice, Ricardo G. (December 2018, 2018 IEEE Conference on Decision and Control)

   In this paper, we propose a modeling and design technique for a proportional-integral-derivative (PID) controller in the presence of aperiodic intermittent sensor measurements. Using classical control design methods, PID controllers can be designed when measurements are available periodically, at discrete time instances, or continuously. Unfortunately, such design do not apply when measurements are available intermittently. Using the hybrid inclusions framework, we model the continuous-time plant to control, the mechanism triggering intermittent measurements, and a hybrid PID control law defining a hybrid closed-loop system. We provide sufficient conditions for uniform global asymptotic stability using Lyapunov set stability methods. These sufficient conditions are used for the design of the gains of the hybrid PID controller. Also, we propose relaxed sufficient conditions to provide a computationally tractable design method leveraging a polytopic embedding approach. The results are illustrated via numerical examples. 

   more » « less