1,777 research outputs found
Safety control of monotone systems with bounded uncertainties
Monotone systems are prevalent in models of engineering applications such as transportation and biological networks. In this paper, we investigate the problem of finding a control strategy for a discrete time positive monotone system with bounded uncertainties such that the evolution of the system is guaranteed to be confined to a safe set in the state space for all times. By exploiting monotonicity, we propose an approach to this problem which is based on constraint programming. We find control strategies that are based on repetitions of finite sequences of control actions. We show that, under assumptions made in the paper, safety control of monotone systems does not require state measurement. We demonstrate the results on a signalized urban traffic network, where the safety objective is to keep the traffic flow free of congestion.This work was partially supported by the NSF under grants CPS-1446151 and CMMI-1400167. (CPS-1446151 - NSF; CMMI-1400167 - NSF
Formal Synthesis of Control Strategies for Positive Monotone Systems
We design controllers from formal specifications for positive discrete-time
monotone systems that are subject to bounded disturbances. Such systems are
widely used to model the dynamics of transportation and biological networks.
The specifications are described using signal temporal logic (STL), which can
express a broad range of temporal properties. We formulate the problem as a
mixed-integer linear program (MILP) and show that under the assumptions made in
this paper, which are not restrictive for traffic applications, the existence
of open-loop control policies is sufficient and almost necessary to ensure the
satisfaction of STL formulas. We establish a relation between satisfaction of
STL formulas in infinite time and set-invariance theories and provide an
efficient method to compute robust control invariant sets in high dimensions.
We also develop a robust model predictive framework to plan controls optimally
while ensuring the satisfaction of the specification. Illustrative examples and
a traffic management case study are included.Comment: To appear in IEEE Transactions on Automatic Control (TAC) (2018), 16
pages, double colum
Ensuring successful introduction of Wolbachia in natural populations of Aedes aegypti by means of feedback control
The control of the spread of dengue fever by introduction of the
intracellular parasitic bacterium Wolbachia in populations of the vector Aedes
aegypti, is presently one of the most promising tools for eliminating dengue,
in the absence of an efficient vaccine. The success of this operation requires
locally careful planning to determine the adequate number of individuals
carrying the Wolbachia parasite that need to be introduced into the natural
population. The introduced mosquitoes are expected to eventually replace the
Wolbachia-free population and guarantee permanent protection against the
transmission of dengue to human.
In this study, we propose and analyze a model describing the fundamental
aspects of the competition between mosquitoes carrying Wolbachia and mosquitoes
free of the parasite. We then use feedback control techniques to devise an
introduction protocol which is proved to guarantee that the population
converges to a stable equilibrium where the totality of mosquitoes carry
Wolbachia.Comment: 24 pages, 5 figure
Reachability Analysis of Neural Networks with Uncertain Parameters
The literature on reachability analysis methods for neural networks currently
only focuses on uncertainties on the network's inputs. In this paper, we
introduce two new approaches for the reachability analysis of neural networks
with additional uncertainties on their internal parameters (weight matrices and
bias vectors of each layer), which may open the field of formal methods on
neural networks to new topics, such as safe training or network repair. The
first and main method that we propose relies on existing reachability analysis
approach based on mixed monotonicity (initially introduced for dynamical
systems). The second proposed approach extends the ESIP (Error-based Symbolic
Interval Propagation) approach which was first implemented in the verification
tool Neurify, and first mentioned in the publication of the tool VeriNet.
Although the ESIP approach has been shown to often outperform the
mixed-monotonicity reachability analysis in the classical case with
uncertainties only on the network's inputs, we show in this paper through
numerical simulations that the situation is greatly reversed (in terms of
precision, computation time, memory usage, and broader applicability) when
dealing with uncertainties on the weights and biases
Formal methods for resilient control
Many systems operate in uncertain, possibly adversarial environments, and their successful operation is contingent upon satisfying specific requirements, optimal performance, and ability to recover from unexpected situations. Examples are prevalent in many engineering disciplines such as transportation, robotics, energy, and biological systems. This thesis studies designing correct, resilient, and optimal controllers for discrete-time complex systems from elaborate, possibly vague, specifications.
The first part of the contributions of this thesis is a framework for optimal control of non-deterministic hybrid systems from specifications described by signal temporal logic (STL), which can express a broad spectrum of interesting properties. The method is optimization-based and has several advantages over the existing techniques. When satisfying the specification is impossible, the degree of violation - characterized by STL quantitative semantics - is minimized. The computational limitations are discussed.
The focus of second part is on specific types of systems and specifications for which controllers are synthesized efficiently. A class of monotone systems is introduced for which formal synthesis is scalable and almost complete. It is shown that hybrid macroscopic traffic models fall into this class. Novel techniques in modular verification and synthesis are employed for distributed optimal control, and their usefulness is shown for large-scale traffic management. Apart from monotone systems, a method is introduced for robust constrained control of networked linear systems with communication constraints. Case studies on longitudinal control of vehicular platoons are presented.
The third part is about learning-based control with formal guarantees. Two approaches are studied. First, a formal perspective on adaptive control is provided in which the model is represented by a parametric transition system, and the specification is captured by an automaton. A correct-by-construction framework is developed such that the controller infers the actual parameters and plans accordingly for all possible future transitions and inferences. The second approach is based on hybrid model identification using input-output data. By assuming some limited knowledge of the range of system behaviors, theoretical performance guarantees are provided on implementing the controller designed for the identified model on the original unknown system
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