130 research outputs found
Leader-following Consensus of Multi-agent Systems over Finite Fields
The leader-following consensus problem of multi-agent systems over finite
fields is considered in this paper. Dynamics of each agent is
governed by a linear equation over , where a distributed control
protocol is utilized by the followers.Sufficient and/or necessary conditions on
system matrices and graph weights in are provided for the
followers to track the leader
Passivity Degradation In Discrete Control Implementations: An Approximate Bisimulation Approach
In this paper, we present some preliminary results for compositional analysis
of heterogeneous systems containing both discrete state models and continuous
systems using consistent notions of dissipativity and passivity. We study the
following problem: given a physical plant model and a continuous feedback
controller designed using traditional control techniques, how is the
closed-loop passivity affected when the continuous controller is replaced by a
discrete (i.e., symbolic) implementation within this framework? Specifically,
we give quantitative results on performance degradation when the discrete
control implementation is approximately bisimilar to the continuous controller,
and based on them, we provide conditions that guarantee the boundedness
property of the closed-loop system.Comment: This is an extended version of our IEEE CDC 2015 paper to appear in
Japa
Safe Control of Euler-Lagrange Systems with Limited Model Information
This paper presents a new safe control framework for Euler-Lagrange (EL)
systems with limited model information, external disturbances, and measurement
uncertainties. The EL system is decomposed into two subsystems called the proxy
subsystem and the virtual tracking subsystem. An adaptive safe controller based
on barrier Lyapunov functions is designed for the virtual tracking subsystem to
ensure the boundedness of the safe velocity tracking error, and a safe
controller based on control barrier functions is designed for the proxy
subsystem to ensure controlled invariance of the safe set defined either in the
joint space or task space. Theorems that guarantee the safety of the proposed
controllers are provided. In contrast to existing safe control strategies for
EL systems, the proposed method requires much less model information and can
ensure safety rather than input-to-state safety. Simulation results are
provided to illustrate the effectiveness of the proposed method.Comment: Accepted to IEEE CDC 2023 and this is the extended versio
Safety Verification of Neural Feedback Systems Based on Constrained Zonotopes
Artificial neural networks (ANNs) have been utilized in many feedback control
systems and introduced new challenges regarding the safety of the system. This
paper considers the problem of verifying whether the trajectories of a system
with a feedforward neural network (FNN) controller can avoid unsafe regions,
using a constrained zonotope-based reachability analysis approach. FNNs with
the rectified linear unit activation function are considered in this work. A
novel set-based method is proposed to compute both exact and over-approximated
reachable sets for linear discrete-time systems with FNN controllers, and
linear program-based sufficient conditions are presented to certify the safety
of the neural feedback systems. Reachability analysis and safety verification
for neural feedback systems with nonlinear models are also considered. The
computational efficiency and accuracy of the proposed method are demonstrated
by two numerical examples where a comparison with state-of-the-art methods is
also provided.Comment: 8 pages, 4 figure
Reachability Analysis and Safety Verification of Neural Feedback Systems via Hybrid Zonotopes
Hybrid zonotopes generalize constrained zonotopes by introducing additional
binary variables and possess some unique properties that make them convenient
to represent nonconvex sets. This paper presents novel hybrid zonotope-based
methods for the reachability analysis and safety verification of neural
feedback systems. Algorithms are proposed to compute the input-output
relationship of each layer of a feedforward neural network, as well as the
exact reachable sets of neural feedback systems. In addition, a sufficient and
necessary condition is formulated as a mixed-integer linear program to certify
whether the trajectories of a neural feedback system can avoid unsafe regions.
The proposed approach is shown to yield a formulation that provides the
tightest convex relaxation for the reachable sets of the neural feedback
system. Complexity reduction techniques for the reachable sets are developed to
balance the computation efficiency and approximation accuracy. Two numerical
examples demonstrate the superior performance of the proposed approach compared
to other existing methods.Comment: 8 pages, 4 figure
Immersion and Invariance-based Disturbance Observer and Its Application to Safe Control
When the disturbance input matrix is nonlinear, existing disturbance observer
design methods rely on the solvability of a partial differential equation or
the existence of an output function with a uniformly well-defined disturbance
relative degree, which can pose significant limitations. This note introduces a
systematic approach for designing an Immersion and Invariance-based Disturbance
Observer (IIDOB) that circumvents these strong assumptions. The proposed IIDOB
ensures the disturbance estimation error is globally uniformly ultimately
bounded by approximately solving a partial differential equation while
compensating for the approximation error. Furthermore, by integrating IIDOB
into the framework of control barrier functions, a filter-based safe control
design method for control-affine systems with disturbances is established where
the filter is used to generate an alternative disturbance estimation signal
with a known derivative. Sufficient conditions are established to guarantee the
safety of the disturbed systems. Simulation results demonstrate the
effectiveness of the proposed method
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