Найдено 10
Ничего не найдено
Optimal Control of Endemic Epidemic Diseases With Behavioral Response
Parino F., Zino L., Rizzo A.
,
Pareto-Optimal Event-Based Scheme for Station and Inter-Station Control of Electric and Automated Buses
PASQUALE C., SACONE S., SIRI S., FERRARA A.
A Control-Theoretical Zero-Knowledge Proof Scheme for Networked Control Systems
Fioravanti C., Hadjicostis C.N., Oliva G.
Novel Bounds for Incremental Hessian Estimation With Application to Zeroth-Order Federated Learning
Maritan A., Schenato L., Dey S.
On the Ratio of Reactive to Active Power in Wave Energy Converter Control
Said H.A., García-Violini D., Faedo N., Ringwood J.V.
Vibrational Stabilization of Cluster Synchronization in Oscillator Networks
Qin Y., Nobili A.M., Bassett D.S., Pasqualetti F.
Exploiting the Synchronization of Nonlinear Dynamics to Secure Distributed Consensus
Fioravanti C., Bonagura V., Oliva G., Hadjicostis C.N., Panzieri S.
Distributed Data-Driven Control of Network Systems
Celi F., Baggio G., Pasqualetti F.
Velocity Estimation of Robot Manipulators: An Experimental Comparison
Liu S.B., Giusti A., Althoff M.
Accurate velocity information is often essential to the control of robot manipulators, especially for precise tracking of fast trajectories. However, joint velocities are rarely directly measured and instead estimated to save costs. While many approaches have been proposed for the velocity estimation of robot joints, no comprehensive experimental evaluation exists, making it difficult to choose the appropriate method. This paper compares multiple estimation methods running on a six degrees-of-freedom manipulator. We evaluate: 1) the estimation error using a ground-truth signal, 2) the closed-loop tracking error, 3) convergence behavior, 4) sensor fault tolerance, 5) implementation and tuning effort. To ensure a fair comparison, we optimally tune the estimators using a genetic algorithm. All estimation methods have a similar estimation error and similar closed-loop tracking performance, except for the nonlinear high-gain observer, which is not accurate enough. Sliding-mode observers can provide a precise velocity estimation despite sensor faults.
