Robust predictive tracking control for a class of nonlinear systems

A robust predictive tracking control (RPTC) approach is developed in this paper to deal with a class of nonlinear SISO systems. To improve the control performance, the RPTC architecture mainly consists of a robust fuzzy PID (RFPID)-based control module and a robust PI grey model (RPIGM)-based prediction module. The RFPID functions as the main control unit to drive the system to desired goals. The control gains are online optimized by neural network-based fuzzy tuners. Meanwhile using grey and neural network theories, the RPIGM is designed with two tasks: to forecast the future system output which is fed to the RFPID to optimize the controller parameters ahead of time; and to estimate the impacts of noises and disturbances on the system performance in order to create properly a compensating control signal. Furthermore, a fuzzy grey cognitive map (FGCM)-based decision tool is built to regulate the RPIGM prediction step size to maximize the control efforts. Convergences of both the predictor and controller are theoretically guaranteed by Lyapunov stability conditions. The effectiveness of the proposed RPTC approach has been proved through real-time experiments on a nonlinear SISO system

A novel robust predictive control system over imperfect networks

This paper aims to study on feedback control for a networked system with both uncertain delays, packet dropouts and disturbances. Here, a so-called robust predictive control (RPC) approach is designed as follows: 1- delays and packet dropouts are accurately detected online by a network problem detector (NPD); 2- a so-called PI-based neural network grey model (PINNGM) is developed in a general form for a capable of forecasting accurately in advance the network problems and the effects of disturbances on the system performance; 3- using the PINNGM outputs, a small adaptive buffer (SAB) is optimally generated on the remote side to deal with the large delays and/or packet dropouts and, therefore, simplify the control design; 4- based on the PINNGM and SAB, an adaptive sampling-based integral state feedback controller (ASISFC) is simply constructed to compensate the small delays and disturbances. Thus, the steady-state control performance is achieved with fast response, high adaptability and robustness. Case studies are finally provided to evaluate the effectiveness of the proposed approach

A Real-Time Bilateral Teleoperation Control System over Imperfect Network

Functionality and performance of modern machines are directly affected by the implementation of real-time control systems. Especially in networked teleoperation applications, force feedback control and networked control are two of the most important factors, which determine the performance of the whole system. In force feedback control, generally it is necessary but difficult and expensive to attach sensors (force/torque/pressure sensors) to detect the environment information in order to drive properly the feedback force. In networked control, there always exist inevitable random time-varying delays and packet dropouts, which may degrade the system performance and, even worse, cause the system instability. Therefore in this chapter, a study on a real-time bilateral teleoperation control system (BTCS) over an imperfect network is discussed. First, current technologies for teleoperation as well as BTCSs are briefly reviewed. Second, an advanced concept for designing a bilateral teleoperation networked control (BTNCS) system is proposed, and the working principle is clearly explained. Third, an approach to develop a force-sensorless feedback control (FSFC) is proposed to simplify the sensor requirement in designing the BTNCS, while the correct sense of interaction between the slave and the environment can be ensured. Fourth, a robust-adaptive networked control (RANC)-based master controller is introduced to deal with control of the slave over the network containing both time delays and information loss. Case studies are carried out to evaluate the applicability of the suggested methodology

A real-time bilateral teleoperation control system over imperfect network

Functionality and performance of modern machines are directly affected by the implementation of real-time control systems. Especially in networked teleoperation applications, force feedback control and networked control are two of the most important factors and determine the performance of the whole system. In force feedback control, generally it is necessary but difficult and expensive to attach sensors (force/torque/pressure sensors) to detect the environment information in order to drive properly the feedback force. In networked control, there always exist inevitable random time-varying delays and packet losses, which may degrade the system performance and, even worse, cause the system instability. Therefore in this chapter, a study on a real-time bilateral teleoperation control system (BTCS) over an imperfect network is discussed. First, current technologies for teleoperation as well as bilateral teleoperation control systems are briefly reviewed. Second, an advanced concept for designing a bilateral teleoperation networked control (BTNCS) system is proposed and the working principle is clearly explained. Third, an approach to develop a force-sensorless feedback control (FSFC) is proposed to simplify the sensor requirement in designing the BTNCS while the correct sense of interaction between the slave and environment can be ensured. Forth, a robust adaptive networked control (RANC) -based master controller is introduced to deal with control of the slave over the network containing both time delays and information loss. Case studies are carried out to evaluate the applicability of the suggested methodology

A data-based hybrid driven control for networked-based remote control applications

This paper develops a data-based hybrid driven control (DHDC) approach for a class of networked nonlinear systems compromising delays, packet dropouts and disturbances. First, the delays and/or packet dropouts are detected and updated online using a network problem detector. Second, a single-variable first-order proportional-integral (PI) -based adaptive grey model is designed to predict in a near future the network problems. Third, a hybrid driven scheme integrated a small adaptive buffer is used to allow the system to operate without any interrupt due to the large delays or packet dropouts. Forth, a prediction-based model-free adaptive controller is developed to compensate for the network problems. Effectiveness of the proposed approach is demonstrated through a case study

Grey Signal Predictor and Fuzzy Controls for Active Vehicle Suspension Systems via Lyapunov Theory

In order to investigate and decide that the vehicle asymptotic vibration stability and improved comfort, the present paper deals with a fuzzy neural network (NN) evolved bat algorithm (EBA) backstepping adaptive controller based on grey signal predictors. The Lyapunov theory and backstepping method is utilized to appraise the math nonlinearity in the active vehicle suspension as well as acquire the final simulation control law in order to track the suitable signal. The Discrete Grey Model DGM (2,1) have been thus used to acquire prospect movement of the suspension system, so that the command controller can prove the convergence and the stability of the entire formula through the Lyapunov-like lemma. The controller overspreads the application range of mechanical elastic vehicle wheel (MEVW) as well as lays a favorable theoretic foundation in adapting to new wheels

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

A generic architecture style for self-adaptive cyber-physical systems

Die aktuellen Konzepte zur Gestaltung von Regelungssystemen basieren auf dynamischen Verhaltensmodellen, die mathematische Ansätze wie Differentialgleichungen zur Ableitung der entsprechenden Funktionen verwenden. Diese Konzepte stoßen jedoch aufgrund der zunehmenden Systemkomplexität allmählich an ihre Grenzen. Zusammen mit der Entwicklung dieser Konzepte entsteht eine Architekturevolution der Regelungssysteme. In dieser Dissertation wird eine Taxonomie definiert, um die genannte Architekturevolution anhand eines typischen Beispiels, der adaptiven Geschwindigkeitsregelung (ACC), zu veranschaulichen. Aktuelle ACC-Varianten, die auf der Regelungstheorie basieren, werden in Bezug auf ihre Architekturen analysiert. Die Analyseergebnisse zeigen, dass das zukünftige Regelungssystem im ACC eine umfangreichere Selbstadaptationsfähigkeit und Skalierbarkeit erfordert. Dafür sind kompliziertere Algorithmen mit unterschiedlichen Berechnungsmechanismen erforderlich. Somit wird die Systemkomplexität erhöht und führt dazu, dass das zukünftige Regelungssystem zu einem selbstadaptiven cyber-physischen System wird und signifikante Herausforderungen für die Architekturgestaltung des Systems darstellt. Inspiriert durch Ansätze des Software-Engineering zur Gestaltung von Architekturen von softwareintensiven Systemen wird in dieser Dissertation ein generischer Architekturstil entwickelt. Der entwickelte Architekturstil dient als Vorlage, um vernetzte Architekturen mit Verfolgung der entwickelten Designprinzipien nicht nur für die aktuellen Regelungssysteme, sondern auch für selbstadaptiven cyber-physischen Systeme in der Zukunft zu konstruieren. Unterschiedliche Auslösemechanismen und Kommunikationsparadigmen zur Gestaltung der dynamischen Verhalten von Komponenten sind in der vernetzten Architektur anwendbar. Zur Bewertung der Realisierbarkeit des Architekturstils werden aktuelle ACCs erneut aufgenommen, um entsprechende logische Architekturen abzuleiten und die Architekturkonsistenz im Vergleich zu den originalen Architekturen basierend auf der Regelungstheorie (z. B. in Form von Blockdiagrammen) zu untersuchen. Durch die Anwendung des entwickelten generischen Architekturstils wird in dieser Dissertation eine künstliche kognitive Geschwindigkeitsregelung (ACCC) als zukünftige ACC-Variante entworfen, implementiert und evaluiert. Die Evaluationsergebnisse zeigen signifikante Leistungsverbesserungen des ACCC im Vergleich zum menschlichen Fahrer und aktuellen ACC-Varianten.Current concepts of designing automatic control systems rely on dynamic behavioral modeling by using mathematical approaches like differential equations to derive corresponding functions, and slowly reach limitations due to increasing system complexity. Along with the development of these concepts, an architectural evolution of automatic control systems is raised. This dissertation defines a taxonomy to illustrate the aforementioned architectural evolution relying on a typical example of control application: adaptive cruise control (ACC). Current ACC variants, with their architectures considering control theory, are analyzed. The analysis results indicate that the future automatic control system in ACC requires more substantial self-adaptation capability and scalability. For this purpose, more complicated algorithms requiring different computation mechanisms must be integrated into the system and further increase system complexity. This makes the future automatic control system evolve into a self-adaptive cyber-physical system and consistitutes significant challenges for the system’s architecture design. Inspired by software engineering approaches for designing architectures of software-intensive systems, a generic architecture style is proposed. The proposed architecture style serves as a template by following the developed design principle to construct networked architectures not only for the current automatic control systems but also for self-adaptive cyber-physical systems in the future. Different triggering mechanisms and communication paradigms for designing dynamic behaviors are applicable in the networked architecture. To evaluate feasibility of the architecture style, current ACCs are retaken to derive corresponding logical architectures and examine architectural consistency compared to the previous architectures considering the control theory (e.g., in the form of block diagrams). By applying the proposed generic architecture style, an artificial cognitive cruise control (ACCC) is designed, implemented, and evaluated as a future ACC in this dissertation. The evaluation results show significant performance improvements in the ACCC compared to the human driver and current ACC variants

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science