6,317 research outputs found
A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS
The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system
Gender Is a Natural Kind with a Historical Essence
Traditional debate on the metaphysics of gender has been a contrast of essentialist and social-constructionist positions. The standard reaction to this opposition is that neither position alone has the theoretical resources required to satisfy an equitable politics. This has caused a number of theorists to suggest ways in which gender is unified on the basis of social rather than biological characteristics but is ârealâ or âobjectiveâ nonetheless â a position I term social objectivism. This essay begins by making explicit the motivations for, and central assumptions of, social objectivism. I then propose that gender is better understood as a real kind with a historical essence, analogous to the biologistâs claim that species are historical entities. I argue that this proposal achieves a better solution to the problems that motivate social objectivism. Moreover, the account is consistent with a post-positivist understanding of the classificatory practices employed within the natural and social sciences
Algebra, coalgebra, and minimization in polynomial differential equations
We consider reasoning and minimization in systems of polynomial ordinary
differential equations (ode's). The ring of multivariate polynomials is
employed as a syntax for denoting system behaviours. We endow this set with a
transition system structure based on the concept of Lie-derivative, thus
inducing a notion of L-bisimulation. We prove that two states (variables) are
L-bisimilar if and only if they correspond to the same solution in the ode's
system. We then characterize L-bisimilarity algebraically, in terms of certain
ideals in the polynomial ring that are invariant under Lie-derivation. This
characterization allows us to develop a complete algorithm, based on building
an ascending chain of ideals, for computing the largest L-bisimulation
containing all valid identities that are instances of a user-specified
template. A specific largest L-bisimulation can be used to build a reduced
system of ode's, equivalent to the original one, but minimal among all those
obtainable by linear aggregation of the original equations. A computationally
less demanding approximate reduction and linearization technique is also
proposed.Comment: 27 pages, extended and revised version of FOSSACS 2017 pape
A bounded-error approach to simultaneous state and actuator fault estimation for a class of nonlinear systems
This paper proposes an approach for the joint state and fault estimation for a class of uncertain nonlinear systems with simultaneous unknown input and actuator faults. This is achieved by designing an unknown input observer combined with a set-membership estimation in the presence of disturbances and measurement noise. The observer is designed using quadratic boundedness approach that is used to overbound the estimation error. Sufficient conditions for the existence and stability of the proposed state and actuator fault estimator are expressed in the form of linear matrix inequalities (LMIs). Simulation results for a quadruple-tank system show the effectiveness of the proposed approach.Peer ReviewedPostprint (author's final draft
A Human Driver Model for Autonomous Lane Changing in Highways: Predictive Fuzzy Markov Game Driving Strategy
This study presents an integrated hybrid solution to mandatory lane changing problem
to deal with accident avoidance by choosing a safe gap in highway driving. To manage
this, a comprehensive treatment to a lane change active safety design is proposed from
dynamics, control, and decision making aspects.
My effort first goes on driver behaviors and relating human reasoning of threat in
driving for modeling a decision making strategy. It consists of two main parts; threat assessment
in traffic participants, (TV s) states, and decision making. The first part utilizes
an complementary threat assessment of TV s, relative to the subject vehicle, SV , by evaluating
the traffic quantities. Then I propose a decision strategy, which is based on Markov
decision processes (MDPs) that abstract the traffic environment with a set of actions, transition
probabilities, and corresponding utility rewards. Further, the interactions of the TV s
are employed to set up a real traffic condition by using game theoretic approach. The question
to be addressed here is that how an autonomous vehicle optimally interacts with the
surrounding vehicles for a gap selection so that more effective performance of the overall
traffic flow can be captured. Finding a safe gap is performed via maximizing an objective
function among several candidates. A future prediction engine thus is embedded in the
design, which simulates and seeks for a solution such that the objective function is maximized
at each time step over a horizon. The combined system therefore forms a predictive
fuzzy Markov game (FMG) since it is to perform a predictive interactive driving strategy
to avoid accidents for a given traffic environment. I show the effect of interactions in decision
making process by proposing both cooperative and non-cooperative Markov game
strategies for enhanced traffic safety and mobility. This level is called the higher level
controller. I further focus on generating a driver controller to complement the automated
carâs safe driving. To compute this, model predictive controller (MPC) is utilized. The
success of the combined decision process and trajectory generation is evaluated with a set
of different traffic scenarios in dSPACE virtual driving environment.
Next, I consider designing an active front steering (AFS) and direct yaw moment control
(DYC) as the lower level controller that performs a lane change task with enhanced
handling performance in the presence of varying front and rear cornering stiffnesses. I propose
a new control scheme that integrates active front steering and the direct yaw moment
control to enhance the vehicle handling and stability. I obtain the nonlinear tire forces
with Pacejka model, and convert the nonlinear tire stiffnesses to parameter space to design
a linear parameter varying controller (LPV) for combined AFS and DYC to perform a
commanded lane change task. Further, the nonlinear vehicle lateral dynamics is modeled
with Takagi-Sugeno (T-S) framework. A state-feedback fuzzy Hâ controller is designed
for both stability and tracking reference. Simulation study confirms that the performance
of the proposed methods is quite satisfactory
Explaining Threshold Effects of Globalization on Poverty: An Institutional Perspective
institutions, poverty, globalization, social norms
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