Verification Guided Refinement of Flight Safety Assessment and Management System for Takeoff

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140668/1/1.i010408.pd

Correct-By-Construction Control Synthesis for Systems with Disturbance and Uncertainty

This dissertation focuses on correct-by-construction control synthesis for Cyber-Physical Systems (CPS) under model uncertainty and disturbance. CPSs are systems that interact with the physical world and perform complicated dynamic tasks where safety is often the overriding factor. Correct-by-construction control synthesis is a concept that provides formal performance guarantees to closed-loop systems by rigorous mathematic reasoning. Since CPSs interact with the environment, disturbance and modeling uncertainty are critical to the success of the control synthesis. Disturbance and uncertainty may come from a variety of sources, such as exogenous disturbance, the disturbance caused by co-existing controllers and modeling uncertainty. To better accommodate the different types of disturbance and uncertainty, the verification and control synthesis methods must be chosen accordingly. Four approaches are included in this dissertation. First, to deal with exogenous disturbance, a polar algorithm is developed to compute an avoidable set for obstacle avoidance. Second, a supervised learning based method is proposed to design a good student controller that has safety built-in and rarely triggers the intervention of the supervisory controller, thus targeting the design of the student controller. Third, to deal with the disturbance caused by co-existing controllers, a Lyapunov verification method is proposed to formally verify the safety of coexisting controllers while respecting the confidentiality requirement. Finally, a data-driven approach is proposed to deal with model uncertainty. A minimal robust control invariant set is computed for an uncertain dynamic system without a given model by first identifying the set of admissible models and then simultaneously computing the invariant set while selecting the optimal model. The proposed methods are applicable to many real-world applications and reflect the notion of using the structure of the system to achieve performance guarantees without being overly conservative.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145933/1/chenyx_1.pd

Data-driven methods for statistical verification of uncertain nonlinear systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 277-290).Due to the increasing complexity of autonomous, adaptive, and nonlinear systems, engineers commonly rely upon statistical techniques to verify that the closed-loop system satisfies specified performance requirements at all possible operating conditions. However, these techniques require a large number of simulations or experiments to exhaustively search the set of possible parametric uncertainties for conditions that lead to failure. This work focuses on resource-constrained applications, such as preliminary control system design or experimental testing, which cannot rely upon exhaustive search to analyze the robustness of the closed-loop system to those requirements. This thesis develops novel statistical verification frameworks that combine data-driven statistical learning techniques and control system verification. First, two frameworks are introduced for verification of deterministic systems with binary and non-binary evaluations of each trajectory's robustness. These frameworks implement machine learning models to learn and predict the satisfaction of the requirements over the entire set of possible parameters from a small set of simulations or experiments. In order to maximize prediction accuracy, closed-loop verification techniques are developed to iteratively select parameter settings for subsequent tests according to their expected improvement of the predictions. Second, extensions of the deterministic verification frameworks redevelop these procedures for stochastic systems and these new stochastic frameworks achieve similar improvements. Lastly, the thesis details a method for transferring information between simulators or from simulators to experiments. Moreover, this method is introduced as part of a new failure-adverse closed-loop verification framework, which is shown to successfully minimize the number of failures during experimental verification without undue conservativeness. Ultimately, these data-driven verification frameworks provide principled approaches for efficient verification of nonlinear systems at all stages in the control system development cycle.by John Francis Quindlen.Ph. D

Flight Safety Assessment and Management.

This dissertation develops a Flight Safety Assessment and Management (FSAM) system to mitigate aircraft loss of control risk. FSAM enables switching between the pilot/nominal autopilot system and a complex flight control system that can potentially recover from high risk situations but can be hard to certify. FSAM monitors flight conditions for high risk situations and selects the appropriate control authority to prevent or recover from loss of control. The pilot/nominal autopilot system is overridden only when necessary to avoid loss of control. FSAM development is pursued using two approaches. First, finite state machines are manually prescribed to manage control mode switching. Constructing finite state machines for FSAM requires careful consideration of possible exception events, but provides a computationally-tractable and verifiable means of realizing FSAM. The second approach poses FSAM as an uncertain reasoning based decision theoretic problem using Markov Decision Processes (MDP), offering a less tedious knowledge engineering process at the cost of computational overhead. Traditional and constrained MDP formulations are presented. Sparse sampling approaches are also explored to obtain suboptimal solutions to FSAM MDPs. MDPs for takeoff and icing-related loss of control events are developed and evaluated. Finally, this dissertation applies verification techniques to ensure that finite state machine or MDP policies satisfy system requirements. Counterexamples obtained from verification techniques aid in FSAM refinement. Real world aviation accidents are used as case studies to evaluate FSAM formulations. This thesis contributes decision making and verification frameworks to realize flight safety assessment and management capabilities. Novel flight envelopes and state abstractions are prescribed to aid decision making.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133348/1/swee_1.pd

Specification-Based Task Orchestration for Multi-Robot Aerial Teams

As humans begin working more frequently in environments with multi-agent systems, they are presented with challenges on how to control these systems in an intuitive manner. Current approaches tend to limit either the interaction ability of the user or limit the expressive capacity of instructions given to the robots. Applications that utilize temporal logics provide a human-readable syntax for systems that ensures formal guarantees for specification completion. By providing a modality for global task specification, we seek to reduce cognitive load and allow for high-level objectives to be communicated to a multi-agent system. In addition to this, we also seek to expand the capabilities of swarms to understand desired actions via interpretable commands retrieved from a human. In this thesis, we first present a method for specification-based control of a quadrotor. We utilize quadrotors as a highly agile and maneuverable application platform that has a wide variety of uses in complex problem domains. Leveraging specification-based control allows us to formulate a specification-based planning framework that will be utilized throughout the thesis. We then present methods for creating systems which allows us to provide task decomposition, allocation and planning for a team of quadrotors defined as task orchestration of multi-robot systems. Next, the task allocation portion of the task orchestration work is extended in the online case by considering cost agnostic sampling of trajectories from an online optimization problem. Then, we will introduce learning techniques where temporal logic specifications are learned and generated from a set of user given traces. Finally, we will conclude this thesis by presenting an extension to the Robotarium through hardware and software modifications that provides remote users access to control aerial swarms.Ph.D

A Scalable Safety Critical Control Framework for Nonlinear Systems

There are two main approaches to safety-critical control. The first one relies on computation of control invariant sets and is presented in the first part of this work. The second approach draws from the topic of optimal control and relies on the ability to realize Model-Predictive-Controllers online to guarantee the safety of a system. In the second approach, safety is ensured at a planning stage by solving the control problem subject for some explicitly defined constraints on the state and control input. Both approaches have distinct advantages but also major drawbacks that hinder their practical effectiveness, namely scalability for the first one and computational complexity for the second. We therefore present an approach that draws from the advantages of both approaches to deliver efficient and scalable methods of ensuring safety for nonlinear dynamical systems. In particular, we show that identifying a backup control law that stabilizes the system is in fact sufficient to exploit some of the set-invariance conditions presented in the first part of this work. Indeed, one only needs to be able to numerically integrate the closed-loop dynamics of the system over a finite horizon under this backup law to compute all the information necessary for evaluating the regulation map and enforcing safety. The effect of relaxing the stabilization requirements of the backup law is also studied, and weaker but more practical safety guarantees are brought forward. We then explore the relationship between the optimality of the backup law and how conservative the resulting safety filter is. Finally, methods of selecting a safe input with varying levels of trade-off between conservatism and computational complexity are proposed and illustrated on multiple robotic systems, namely: a two-wheeled inverted pendulum (Segway), an industrial manipulator, a quadrotor, and a lower body exoskeleton

Towards a safe and responsive control framework for human-centered robots

Human-centered robots are a specific kind of service robot, which interact with humans physically or cognitively and help humans with tasks in uncertain environments. They can be humanoid robots, exoskeletons, or manipulators and mobile platforms that provide us good services. However, human-centered robots are still not perfect enough for us to use nowadays. On the one hand, human-centered robots are still slow and inefficient for their tasks because the human inputs and dynamics that they react to are uncertain, immeasurable, or even completely unknown. On the other hand, human-centered robots face much more complicated safety requirements than other kinds of robots because humans are dynamic and vulnerable during physical human-robot interaction. To resolve these issues of human-centered robots, the work in this dissertation explores new models for reducing human uncertainty and new control algorithms for improving safety warranty. The first half of this dissertation introduces a complex stiffness model for describing the uncertain human impedance. The discovery of this new model is motivated to explain the observation of a consistent damping ratio of a human versus different environmental dynamics. It replaces the linear damping term in a conventional mass-spring-damping model with a hysteretic damping term, an imaginary value in the frequency domain. Because of the correlation between the stiffness term and the newly discovered hysteretic damping term in the complex stiffness model, we can significantly reduce the human impedance uncertainty. Based on the complex stiffness model, we can adopt nonlinear control strategies for improving the responsiveness and the human-friendliness of human-centered robots. The second half of this dissertation introduces the concept of a barrier pair, which consists of a barrier function and a controller for the safety verification and warranty of a human-centered robot. We obtain a barrier pair by solving an optimization problem subject to a series of linear matrix inequalities representing the state-space, input, and stability constraints of a human-centered robot. By incorporating sampling-based methods into the synthesis of barrier pairs, human-centered robots can guarantee safe operation with non-convex state-space constraints. The sampling-based barrier pair method helps us construct a control framework of human-robot shared autonomy. A human-centered robot in this control framework uses an inference of a human's objective to figure out how to assist the human and prevent the human from potential accidents.Mechanical Engineerin