Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently received increasing attention in recent yea...
Humans have the ability to deviate from their natural behavior when necessary, which is a cognitive process called response inhibition. Similar approaches have independently received increasing attention in recent years for ensuring the safety of control. Realized using control barrier functions or predictive safety filters, these approaches can effectively ensure the satisfaction of state constraints through an online adaptation of nominal control laws, e.g., obtained through reinforcement learning. While the focus of these realizations of inhibitory control has been on risk-neutral formulations, human studies have shown a tight link between response inhibition and risk attitude. Inspired by this insight, we propose a flexible, risk-sensitive method for inhibitory control. Our method is based on a risk-aware condition for value functions, which guarantees the satisfaction of state constraints. We propose a method for learning these value functions using common techniques from reinforcement learning and derive sufficient conditions for its success. By enforcing the derived safety conditions online using the learned value function, risk-sensitive inhibitory control is effectively achieved. The effectiveness of the developed control scheme is demonstrated in simulations.
In today’s interconnected world, securely sharing data between devices and systems is crucial. End-to-end big data sharing enables seamless communication and collaboration, but also presents data security and control...
In today’s interconnected world, securely sharing data between devices and systems is crucial. End-to-end big data sharing enables seamless communication and collaboration, but also presents data security and control challenges. To ensure data confidentiality and integrity, data usage control is important. We designed a control policy language to provide a unified way to describe data usage control policies. The language can embed common scripting languages for policy description and can describe policy rules for various access control models like ABAC and RBAC, as well as data usage obligations and conditions. By separating the policy description model rules and embedding them in the data, only a small set of rules needs to be transmitted for data using the same policy model, without retransmit data usage policies. We implemented a policy engine on Linux. Experiments show its transmission and storage overhead is reduced by 90% compared to directly attaching XACML to data.
We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs). INNs are a class of implicit learning models that use implicit equations as layers and have been shown to exhi...
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For reaction systems, the state variables (the number of moles) can be expressed using the concepts of extents of reaction and mass transfer for homogeneous and heterogeneous reaction systems. In this work, a general ...
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For reaction systems, the state variables (the number of moles) can be expressed using the concepts of extents of reaction and mass transfer for homogeneous and heterogeneous reaction systems. In this work, a general framework for designing asymptotic observers for homogeneous and gas-liquid reaction systems is presented using the concept of the extents. For gas-liquid reaction systems, it is shown that asymptotic observers can be designed using measurements in the gas-phase. The effect of noisy measurements on the estimation of unmeasured concentrations is also discussed. The proposed asymptotic observer approach is illustrated using an example of the chlorination of butanoic acid (gas-liquid reaction system).
Evolution of agents’ dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this pap...
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This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph w...
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This paper constructs a non-cooperative/cooperative stochasticdifferential game model to prove that the optimal strategies trajectory ofagents in a system with a topological configuration of a Multi-Local-Worldgraph would converge into a certain attractor if the system’s configuration isfixed. Due to the economics and management property, almost all systems aredivided into several independent Local-Worlds, and the interaction betweenagents in the system is more complex. The interaction between agents inthe same Local-World is defined as a stochastic differential cooperativegame;conversely, the interaction between agents in different Local-Worldsis defined as a stochastic differential non-cooperative game. We construct anon-cooperative/cooperative stochastic differential game model to describethe interaction between agents. The solutions of the cooperative and noncooperativegames are obtained by invoking corresponding theories, and thena nonlinear operator is constructed to couple these two solutions *** last, the optimal strategies trajectory of agents in the system is proven toconverge into a certain attractor, which means that strategies trajectory arecertainty as time tends to infinity or a large positive integer. It is concluded thatthe optimal strategy trajectory with a nonlinear operator of cooperative/noncooperativestochastic differential game between agents can make agentsin a certain Local-World coordinate and make the Local-World paymentmaximize, and can make the all Local-Worlds equilibrated;furthermore, theoptimal strategy of the coupled game can converge into a particular attractorthat decides the optimal property.
The study proposes new results on the set input-to-state stability (ISS) subject to a small input time delay for compact, invariant sets that contains the origin. First, using the nonlinear small-gain theory, we prove...
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This paper proposes a new approach for solving a structured nonsmooth nonconvex optimization problem with nonlinear equality constraints, where both the objective function and constraints are 2-blocks separable. Our m...
This paper proposes a new approach for solving a structured nonsmooth nonconvex optimization problem with nonlinear equality constraints, where both the objective function and constraints are 2-blocks separable. Our method is based on a 2-block linearized ADMM, where we linearize the smooth part of the cost function and the nonlinear term of the functional constraints in the augmented Lagrangian at each outer iteration. This results in simple subproblems, whose solutions are used to update the iterates of the 2 blocks variables. We prove global convergence for the sequence generated by our method to a stationary point of the original problem. To demonstrate its effectiveness, we apply our proposed algorithm as a solver for the nonlinear model predictive control problem of an inverted pendulum on a cart.
The primary objective of a navigation system is to continuously monitor the trajectory of a vehicle. Navigation for a land vehicle is implemented using different measurement systems, such as Inertial Navigation System...
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