448 research outputs found
Reduction-Based Robustness Analysis of Linear Predictor Feedback for Distributed Input Delays
Lyapunov-Krasovskii approach is applied to parameter- and delay-robustness
analysis of the feedback suggested by Manitius and Olbrot for a linear
time-invariant system with distributed input delay. A functional is designed
based on Artstein's system reduction technique. It depends on the norms of the
reduction-transformed plant state and original actuator state. The functional
is used to prove that the feedback is stabilizing when there is a slight
mismatch in the system matrices and delay values between the plant and
controller
A mosaic of eyes
Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties
Optimal PMU Placement for Power System Dynamic State Estimation by Using Empirical Observability Gramian
In this paper the empirical observability Gramian calculated around the
operating region of a power system is used to quantify the degree of
observability of the system states under specific phasor measurement unit (PMU)
placement. An optimal PMU placement method for power system dynamic state
estimation is further formulated as an optimization problem which maximizes the
determinant of the empirical observability Gramian and is efficiently solved by
the NOMAD solver, which implements the Mesh Adaptive Direct Search (MADS)
algorithm. The implementation, validation, and also the robustness to load
fluctuations and contingencies of the proposed method are carefully discussed.
The proposed method is tested on WSCC 3-machine 9-bus system and NPCC
48-machine 140-bus system by performing dynamic state estimation with
square-root unscented Kalman filter. The simulation results show that the
determined optimal PMU placements by the proposed method can guarantee good
observability of the system states, which further leads to smaller estimation
errors and larger number of convergent states for dynamic state estimation
compared with random PMU placements. Under optimal PMU placements an obvious
observability transition can be observed. The proposed method is also validated
to be very robust to both load fluctuations and contingencies.Comment: Accepted by IEEE Transactions on Power System
Speed Observation and Position Feedback Stabilization of Partially Linearizable Mechanical Systems
The problems of speed observation and position feedback stabilization of mechanical systems are addressed in this paper. Our interest is centered on systems that can be rendered linear in the velocities via a (partial) change of coordinates. It is shown that the class is fully characterized by the solvability of a set of partial differential equations (PDEs) and strictly contains the class studied in the existing literature on linearization for speed observation or control. A reduced order globally exponentially stable observer, constructed using the immersion and invariance methodology, is proposed. The design requires the solution of another set of PDEs, which are shown to be solvable in several practical examples. It is also proven that the full order observer with dynamic scaling recently proposed by Karagiannis and Astolfi obviates the need to solve the latter PDEs. Finally, it is shown that the observer can be used in conjunction with an asymptotically stabilizing full state-feedback interconnection and damping assignment passivity-based controller preserving asymptotic stability.</p
Inference of complex biological networks: distinguishability issues and optimization-based solutions
<p>Abstract</p> <p>Background</p> <p>The inference of biological networks from high-throughput data has received huge attention during the last decade and can be considered an important problem class in systems biology. However, it has been recognized that reliable network inference remains an unsolved problem. Most authors have identified lack of data and deficiencies in the inference algorithms as the main reasons for this situation.</p> <p>Results</p> <p>We claim that another major difficulty for solving these inference problems is the frequent lack of uniqueness of many of these networks, especially when prior assumptions have not been taken properly into account. Our contributions aid the distinguishability analysis of chemical reaction network (CRN) models with mass action dynamics. The novel methods are based on linear programming (LP), therefore they allow the efficient analysis of CRNs containing several hundred complexes and reactions. Using these new tools and also previously published ones to obtain the network structure of biological systems from the literature, we find that, often, a unique topology cannot be determined, even if the structure of the corresponding mathematical model is assumed to be known and all dynamical variables are measurable. In other words, certain mechanisms may remain undetected (or they are falsely detected) while the inferred model is fully consistent with the measured data. It is also shown that sparsity enforcing approaches for determining 'true' reaction structures are generally not enough without additional prior information.</p> <p>Conclusions</p> <p>The inference of biological networks can be an extremely challenging problem even in the utopian case of perfect experimental information. Unfortunately, the practical situation is often more complex than that, since the measurements are typically incomplete, noisy and sometimes dynamically not rich enough, introducing further obstacles to the structure/parameter estimation process. In this paper, we show how the structural uniqueness and identifiability of the models can be guaranteed by carefully adding extra constraints, and that these important properties can be checked through appropriate computation methods.</p
Synchronization of reactionâdiffusion Hopfield neural networks with s-delays through sliding mode control
Synchronization of reactionâdiffusion Hopfield neural networks with s-delays via sliding mode control (SMC) is investigated in this paper. To begin with, the system is studied in an abstract Hilbert space C([âr; 0];U) rather than usual Euclid space Rn. Then we prove that the state vector of the drive system synchronizes to that of the response system on the switching surface, which relies on equivalent control. Furthermore, we prove that switching surface is the sliding mode area under SMC. Moreover, SMC controller can also force with any initial state to reach the switching surface within finite time, and the approximating time estimate is given explicitly. These criteria are easy to check and have less restrictions, so they can provide solid theoretical guidance for practical design in the future. Three different novel LyapunovâKrasovskii functionals are used in corresponding proofs. Meanwhile, some inequalities such as Young inequality, Cauchy inequality, PoincarĂ© inequality, Hanalay inequality are applied in these proofs. Finally, an example is given to illustrate the availability of our theoretical result, and the simulation is also carried out based on RungeâKuttaâChebyshev method through Matlab
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