40,306 research outputs found
Fault estimation and active fault tolerant control for linear parameter varying descriptor systems
Starting with the baseline controller design, this paper proposes an integrated approach of active fault tolerant control based on proportional derivative extended state observer (PDESO) for linear parameter varying descriptor systems. The PDESO can simultaneously provide the estimates of the system states, sensor faults, and actuator faults. The Lâ‚‚ robust performance of the closed-loop system to bounded exogenous disturbance and bounded uncertainty is achieved by a two-step design procedure adapted from the traditional observer-based controller design. Furthermore, an LMI pole-placement region and the Lâ‚‚ robustness performance are combined into a multiobjective formulation by suitably combing the appropriate LMI descriptions. A parameter-varying system example is given to illustrate the design procedure and the validity of the proposed integrated design approach
A Comparison of LPV Gain Scheduling and Control Contraction Metrics for Nonlinear Control
Gain-scheduled control based on linear parameter-varying (LPV) models derived
from local linearizations is a widespread nonlinear technique for tracking
time-varying setpoints. Recently, a nonlinear control scheme based on Control
Contraction Metrics (CCMs) has been developed to track arbitrary admissible
trajectories. This paper presents a comparison study of these two approaches.
We show that the CCM based approach is an extended gain-scheduled control
scheme which achieves global reference-independent stability and performance
through an exact control realization which integrates a series of local LPV
controllers on a particular path between the current and reference states.Comment: IFAC LPVS 201
Optimal Multiuser Scheduling Schemes for Simultaneous Wireless Information and Power Transfer
In this paper, we study the downlink multiuser scheduling problem for systems
with simultaneous wireless information and power transfer (SWIPT). We design
optimal scheduling algorithms that maximize the long-term average system
throughput under different fairness requirements, such as proportional fairness
and equal throughput fairness. In particular, the algorithm designs are
formulated as non-convex optimization problems which take into account the
minimum required average sum harvested energy in the system. The problems are
solved by using convex optimization techniques and the proposed optimization
framework reveals the tradeoff between the long-term average system throughput
and the sum harvested energy in multiuser systems with fairness constraints.
Simulation results demonstrate that substantial performance gains can be
achieved by the proposed optimization framework compared to existing suboptimal
scheduling algorithms from the literature.Comment: Accepted for presentation at the European Signal Processing
Conference 201
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