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Invited Paper Hybrid system theory lies at the intersection of the fields of engineering control theory and computer science verification. It is defined as the modeling, analysis, and control of systems that involve the interaction of both discrete state systems, represented by finite automata, and continuous state dynamics, represented by differential(More)
We address systems which have multiple objectives: broadly speaking, these objectives can be thought of as safety and performance goals. Guaranteeing safety is our rst priority, satisfying performance criteria our second. In this paper, we compute the system's safe operating space and represent it in closed form, and then, within this space, we compute(More)
—Hybrid systems combine discrete state dynamics which model mode switching, with continuous state dynamics which model physical processes. Hybrid systems can be controlled by affecting both their discrete mode logic and continuous dynamics: in many systems, such as commercial aircraft, these can be controlled both automatically and using manual control. A(More)
Modern commercial aircraft have extensive automation which helps the pilot by performing computations, obtaining data, and completing procedural tasks. The pilot display must contain enough information so that the pilot can correctly predict the aircraft's behavior, while not overloading the pilot with unnecessary information. Human-automation interaction(More)
While a number of Lagrangian algorithms to approximate reachability in dozens or even hundreds of dimensions for systems with linear dynamics have recently appeared in the literature, no similarly scalable algorithms for approximating viable sets have been developed. In this paper we describe a connection between reachability and viability that enables us(More)
In sampled data systems the controller receives periodically sampled state feedback about the evolution of a continuous time plant, and must choose a constant control signal to apply between these updates; however, unlike purely discrete time models the evolution of the plant between updates is important. In this paper we describe an abstract algorithm for(More)
We present a connection between the viability kernel and maximal reachable sets. Current numerical schemes that compute the viability kernel suffer from a complexity that is exponential in the dimension of the state space. In contrast, extremely efficient and scalable techniques are available that compute maximal reachable sets. We show that under certain(More)
— The continual reachability set, the set of initial states of a constrained dynamical system that can reach a target at any desired time, is introduced. The properties of this set are investigated and its connection with maximal reachability constructs is examined. Owing to this connection, efficient and scalable maximal reachability techniques can be used(More)
UNLABELLED When faced with visual uncertainty during motor performance, humans rely more on predictive forward models and proprioception and attribute lesser importance to the ambiguous visual feedback. Though disrupted predictive control is typical of patients with cerebellar disease, sensorimotor deficits associated with the involuntary and often(More)