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"control system analysis"
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Developing HSS iteration schemes for solving the quadratic matrix equation AX2+BX+C=0 $AX^{2}+BX+C=0
The quadratic matrix equation (QME) Q(X)=AX2+BX+C=0, $$\\begin{equation*} Q(X)=AX^{2}+BX+C=0, \\end{equation*}$$ occurs in the branches such as the quadratic eigenvalue problems and quasi‐birth‐death processes. Also, the numerical solution of QMEs is an essential step in many computational methods for linear‐quadratic and robust control, filtering, controller order reduction, inner‐outer factorization, spectral factorization, and other applications. In this study, schemes are presented to solve the QME based on the Hermitian and skew‐Hermitian splitting (HSS). It is shown that the proposed schemes converge to the solutions of the QME. Finally, some examples are solved to discover the application of the schemes in comparison with Newton's method. The quadratic matrix equation (QME) Q(X)=AX2+BX+C, $$\\begin{equation*} Q(X)=AX^{2}+BX+C, \\end{equation*}$$ occurs in the branches such as the quadratic eigenvalue problems and quasi‐birth‐death processes. Also, the numerical solution of QMEs is an essential step in many computational methods for linear‐ quadratic and robust control, filtering, controller order reduction, inner‐outer factorization, spectral factorization, and other applications. In this study, we present schemes to solve the QME on Hermitian and skew‐Hermitian splitting (HSS). We show that the proposed schemes converge to the solutions of the QME. Finally, some examples are solved to discover the application of the schemes in comparison with Newton's method.
Journal Article
Preview repetitive control with equivalent input disturbance for continuous‐time linear systems
This paper investigates the problem of preview repetitive control with equivalent‐input‐disturbance for a class of continuous‐time linear systems in the presence of unknown external disturbances. First, an augmented delay system is constructed by using the nominal state equation with error system and the state equation of a modified repetitive controller, which is then transformed into a non‐delayed one by state transformation. Next, by using optimal control theory, a preview repetitive control law is obtained. It is composed of state feedback, tracking error compensation, output of modified repetitive controller and preview compensation. Furthermore, in order to achieve a good disturbance estimation and attenuation performance, by applying the results of the standard equivalent‐input‐disturbance theory, a preview repetitive control law with equivalent‐input‐disturbance is offered for the uncertain system with unknown external disturbance. Finally, the numerical simulation example is provided to illustrate the effectiveness of the proposed method.
Journal Article
Complexity science in air traffic management
\"Air traffic management (ATM) comprises a highly complex socio-technical system that keeps air traffic flowing safely and efficiently, worldwide, every minute of the year. Over the last few decades, several ambitious ATM performance improvement programmes have been undertaken. Such programmes have mostly delivered local technological solutions, whilst corresponding ATM performance improvements have fallen short of stakeholder expectations. In hindsight, this can be substantially explained from a complexity science perspective: ATM is simply too complex to address through classical approaches such as system engineering and human factors. In order to change this, complexity science has to be embraced as ATM's 'best friend'. The applicability of complexity science paradigms to the analysis and modelling of future operations is driven by the need to accommodate long-term air traffic growth within an already-saturated ATM infrastructure\"--Provided by publisher.
Book
Closed‐loop stability analysis of deep reinforcement learning controlled systems with experimental validation
Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU). This research investigates the closed‐loop stability of dynamic systems controlled by deep reinforcement learning agents through Lyapunov analysis and a linear‐quadratic polynomial approximation of the trained agent. The study validates its approach with simulations and experiments on real‐world hardware, confirming the deep reinforcement learning's effectiveness and identifying critical operational thresholds and stability margins for practical applications.
Journal Article
Systems, automation & control
\"The book elaborates selected, extended and peer reviewed papers from the International Conference on Power Electrical Systems held in Mahdia, Tunisia in 2015. Main Topics are: multivariable -, nonlinear-, stochastic-, and robust control, robotics and mechatronics, synthesis of automation systems.\"-- Provided by Publisher.
Book
Gravity compensation and optimal control of actuated multibody system dynamics
This work investigates the gravity compensation topic, from a control perspective. The gravity could be levelled by a compensating mechanical system or in the control law, such as proportional derivative (PD) plus gravity, sliding mode control, or computed torque method. The gravity compensation term is missing in linear and nonlinear optimal control, in both continuous‐ and discrete‐time domains. The equilibrium point of the control system is usually zero and this makes it impossible to perform regulation when the desired condition is not set at origin or in other cases, where the gravity vector is not zero at the equilibrium point. The system needs a steady‐state input signal to compensate for the gravity in those conditions. The stability proof of the gravity compensated control law based on nonlinear optimal control and the corresponding deviation from optimality, with proof, are introduced in this work. The same concept exists in discrete‐time control since it uses analog to digital conversion of the system and that includes the gravity vector of the system. The simulation results highlight two important cases, a robotic manipulator and a tilted‐rotor hexacopter, as an application to the claimed theoretical statements.
Journal Article
HACCP : a food industry briefing
\"Readers of this accessible book - now in a revised and updated new edition - are taken on a conceptual journey which passes every milestone and important feature of the HACCP landscape at a pace which is comfortable and productive. The information and ideas contained in the book will enable food industry managers and executives to take their new-found knowledge into the workplace for use in the development and implementation of HACCP systems appropriate for their products and manufacturing processes.The material is structured so that the reader can quickly assimilate the essentials of the topic. Clearly presented, this HACCP briefing includes checklists, bullet points, flow charts, schematic diagrams for quick reference, and at the start of each section the authors have provided useful key points summary boxes. HACCP: a Food Industry Briefing is an introductory-level text for readers who are unfamiliar with the subject either because they have never come across it or because they need to be reminded. The book will also make a valuable addition to material used in staff training and is an excellent core text for HACCP courses\"-- Provided by publisher.
BOOK
Finite-Time Input-to-State Stability and Applications to Finite-Time Control Design
This paper extends the well-known concept, Sontag's input-to-state stability (ISS), to finite-time control problems. In other words, a new concept, finite-time input-to-state stability (FTISS), is proposed and then is applied to both the analysis of finite-time stability and the design of finite-time stabilizing feedback laws of control systems. With finite-time stability, nonsmoothness has to be considered, and serious technical challenges arise in the design of finite-time controllers and the stability analysis of the closed-loop system. It is found that FTISS plays an important role as the conventional ISS in the context of asymptotic stability analysis and smooth feedback stabilization. Moreover, a robust adaptive controller is proposed to handle nonlinear systems with parametric and dynamic uncertainties by virtue of FTISS and related arguments. [PUBLICATION ABSTRACT]
Journal Article