We propose a hybrid controller ensuring robust global asymptotic stability of a desired spherical orientation in a kinematic setting-a task that is impossible by continuous or memoryless discontinuous state-feedback d...
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ISBN:
(纸本)9781424477456
We propose a hybrid controller ensuring robust global asymptotic stability of a desired spherical orientation in a kinematic setting-a task that is impossible by continuous or memoryless discontinuous state-feedback due to topological obstructions. The hybrid control structure relies on hysteretically switching between locally stabilizing control laws that are generated by a "synergistic" family of potential functions. We propose a parametrized diffeomorphism as a means of generating such a synergistic family. This construction is accomplished by composing an existing potential function with the diffeomorphism invoked with different parameters, thus generating new potential functions that have different critical behavior. A salient feature of the construction is that it can produce a synergistic family of potential functions from a single potential function.
In this paper, a simple neuro-controller for power systems is presented. The controller performs the function of the terminal voltage control. By representing the proposed neuro-controller in s-domain, its parameters ...
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Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method fo...
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We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is...
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We propose Hamiltonian quantum generative adversarial networks (HQuGANs) to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of classical generative adversarial networks in learning high-dimensional distributions. The quantum optimal control approach not only makes the algorithm naturally adaptable to the experimental constraints of near-term hardware, but also offers a more natural characterization of overparameterization compared to the circuit model. We numerically demonstrate the capabilities of the proposed framework to learn various highly entangled many-body quantum states, using simple two-body Hamiltonians and under experimentally relevant constraints such as low-bandwidth controls. We analyze the computational cost of implementing HQuGANs on quantum computers and show how the framework can be extended to learn quantum dynamics. Furthermore, we introduce a cost function that circumvents the problem of mode collapse that prevents convergence of HQuGANs and demonstrate how to accelerate the convergence of them when generating a pure state.
Today's multiagent systems have grown too complex to rely on centralized controllers, prompting increasing interest in the design of distributed algorithms. In this respect, game theory has emerged as a valuable t...
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ISBN:
(数字)9781728113982
ISBN:
(纸本)9781728113999
Today's multiagent systems have grown too complex to rely on centralized controllers, prompting increasing interest in the design of distributed algorithms. In this respect, game theory has emerged as a valuable tool to complement more traditional techniques. The fundamental idea behind this approach is the assignment of agents' local cost functions, such that their selfish minimization attains, or is provably close to, the global objective. Any algorithm capable of computing an equilibrium of the corresponding game inherits an approximation ratio that is, in the worst case, equal to its price-of-anarchy. Therefore, a successful application of the game design approach hinges on the possibility to quantify and optimize the equilibrium *** this end, we introduce the notion of generalized smoothness, and show that the resulting efficiency bounds are significantly tighter compared to those obtained using the traditional smoothness approach. Leveraging this newly-introduced notion, we quantify the equilibrium performance for the class of local resource allocation games. Finally, we show how the agents' local decision rules can be designed in order to optimize the efficiency of the corresponding equilibria, by means of a tractable linear program.
Crane control is a difficult problem for conventional control methods because of the highly nonlinear equations that must be satisfied. Usually the necessary conditions for solving an optimal control problem require f...
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High speed networks have characteristics of high bandwidth, long queuing delay, and high burstiness which make it difficult to address issues such as fairness, low queuing delay and high link utilization. Current high...
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We state results on the existence of smooth Lyapunov functions for hybrid systems whose solutions satisfy a class-KLL estimate with respect to two measures. The class-KLL estimate, a natural extension of class-KL esti...
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We state results on the existence of smooth Lyapunov functions for hybrid systems whose solutions satisfy a class-KLL estimate with respect to two measures. The class-KLL estimate, a natural extension of class-KL estimate, is in terms of the elapsed time and the number of jumps that have occurred. The main result is that a smooth Lyapunov function exists if and only if the class-KLL estimate is robust. In turn, sufficient conditions for robustness are given. Special cases include systems with compact attractors. Most of the results parallel, and unify, what has been developed previously for differential inclusions and difference inclusions.
This paper develops a new trivariate hierarchical spline scheme for volumetric data representation. Unlike conventional spline formulations and techniques, our new framework is built upon a novel parametric domain cal...
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This paper presents a control structure featuring an operator Q driven by the residual signal, which indicates the difference between the measurement output and the estimated output from an observer. The form of this ...
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