Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDecoupled Reference Governors: A Constraint Management Technique for MIMO Systems
154
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
This paper presents a computationally efficient solution for constraint management of multi-input and multi-output (MIMO) systems. The solution, referred to as the Decoupled Reference Governor (DRG), maintains the highly-attractive computational features of Scalar Reference Governors (SRG) while having performance comparable to Vector Reference Governors (VRG). DRG is based on decoupling the input-output dynamics of the system, followed by the deployment of a bank of SRGs for each decoupled channel. We present two formulations of DRG: DRG-tf, which is based on system decoupling using transfer functions, and DRG-ss, which is built on state feedback decoupling. A detailed set-theoretic analysis of DRG, which highlights its main characteristics, is presented. We also show a quantitative comparison between DRG and the VRG to illustrate the computational advantages of DRG. The robustness of this approach to disturbances and uncertainties is also investigated.
rate research
Read More
This paper presents a constraint management strategy based on Scalar Reference Governors (SRG) to enforce output, state, and control constraints while taking into account the preview information of the reference and/or disturbances signals. The strategy, referred to as the Preview Reference Governor (PRG), can outperform SRG while maintaining the highly-attractive computational benefits of SRG. However, as it is shown, the performance of PRG may suffer if large preview horizons are used. An extension of PRG, referred to as Multi-horizon PRG, is proposed to remedy this issue. Quantitative comparisons between SRG, PRG, and Multi-horizon PRG on a one-link robot arm example are presented to illustrate their performance and computation time. Furthermore, extensions of PRG are presented to handle systems with disturbance preview and multi-input systems. The robustness of PRG to parametric uncertainties and inaccurate preview information is also explored.
In this paper, we introduce a definition of phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose frequency responses are (semi-)sectorial at all frequencies. The newly defined phase concept subsumes the well-known notions of positive real systems and negative imaginary systems. We formulate a small phase theorem for feedback stability, which complements the celebrated small gain theorem. The small phase theorem lays the foundation of a phase theory of MIMO systems. We also discuss time-domain interpretations of phase-bounded systems via both energy signal analysis and power signal analysis. In addition, a sectored real lemma is derived for the computation of MIMO phases, which serves as a natural counterpart of the bounded real lemma.
The work aims to improve the existing fast load shedding algorithm for industrial power system to increase performance, reliability, and scalability for future expansions. The paper illustrates the development of a scalable algorithm to compute the shedding matrix, and the test performed on a model of the electric grid of an offshore platform. From this model it is possible to study the impact on the transients of various parameters, such as spinning reserve and delay time. Subsequently, the code is converted into Structured Text and implemented on an ABB PLC. The scalability of the load shedding algorithm is thus verified, confirming its performance with respect to the computation of the shedding matrix and the usefulness of the dynamic simulations during the design phase of the plant.
Hybrid-electric propulsion systems powered by clean energy derived from renewable sources offer a promising approach to decarbonise the worlds transportation systems. Effective energy management systems are critical for such systems to achieve optimised operational performance. However, developing an intelligent energy management system for applications such as ships operating in a highly stochastic environment and requiring concurrent control over multiple power sources presents challenges. This article proposes an intelligent energy management framework for hybrid-electric propulsion systems using deep reinforcement learning. In the proposed framework, a Twin-Delayed Deep Deterministic Policy Gradient agent is trained using an extensive volume of historical load profiles to generate a generic energy management strategy. The strategy, i.e. the core of the energy management system, can concurrently control multiple power sources in continuous state and action spaces. The proposed framework is applied to a coastal ferry model with multiple fuel cell clusters and a battery, achieving near-optimal cost performance when applied to novel future voyages.
In this paper a novel approach to co-design controller and attack detector for nonlinear cyber-physical systems affected by false data injection (FDI) attack is proposed. We augment the model predictive controller with an additional constraint requiring the future---in some steps ahead---trajectory of the system to remain in some time-invariant neighborhood of a properly designed reference trajectory. At any sampling time, we compare the real-time trajectory of the system with the designed reference trajectory, and construct a residual. The residual is then used in a nonparametric cumulative sum (CUSUM) anomaly detector to uncover FDI attacks on input and measurement channels. The effectiveness of the proposed approach is tested with a nonlinear model regarding level control of coupled tanks.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
