A Qualitative Analysis of Hybrid Control Systems

This paper studies the adaptive state feedback for state tracking control problem for piecewise linear systems, which are approximations of nonlinear controlled systems at multiple operating points. Piecewise linear reference model systems are studied and used for generating state trajectories. Adaptive schemes are developed using Lyapunov design methods, and their stability and tracking performance are analyzed and evaluated by simulation examples. Asymptotic tracking performance of such an adaptive control system with a sufficiently rich reference input is shown by simulation results, indicating that certain persistent excitation conditions can be sufficient for ensuring the desired asymptotic tracking in the presence of repetitive system switchings.

The increasing complexity associated with many modern engineering applications, including autonomous robot guidance and navigation, process control in sensor-rich environments, and control of biological systems, has far-reaching implications for control system design. As an example, reactive, embedded software systems, interacting among themselves and remote users over communication networks, introduce a whole new set of system-level challenges, and classic control design objectives such as stability, performance, and robustness are being complemented with a number of new questions. These include the cost of hardware implementation, measured for example not only by computational requirements such as speed and memory, but also by communication requirements such as available communication bandwidth. Moreover, the complexity associated with specifying the control procedures and with verifying the behavior of the closed-loop system increasingly plays a fundamental role, especially in safety-critical control systems arising in energy and transportation networks, and in medical applications.

In this paper, by applying interval methods, a new framework for analysis and control of discrete-time hybrid systems with parameter uncertainty is proposed. In analysis and control of hybrid systems, there are problem formulations such that convex polyhedra are computed, but for high-dimensional systems, it is difficult to solve these problems within the practical computation time. In this paper, instead of computing convex polyhedra, an interval method, which is one of classical methods in verified numerical computation, is applied to analysis and control of hybrid systems. By applying an interval method, discrete-time piecewise systems with parameter uncertainty can be approximately transformed into a mixed logical dynamical model.

The application of fuzzy models in thermal power control systems is considered. The structure of a fuzzy system for a thermal object (boiler) is described. Results of the design and a schematic realization of boiler aggregate control systems are reported. Use of the suggested fuzzy models for control of the fuel-air relationship and boiler water level are described as examples.

Correct design, and system-level dependability prediction of highly-integrated systems demand the collocation of requirements and architectural artifacts within an integrated development environment. Hybrid systems, having dependencies and extensive interactions between their control portion and their environment, further intensify this need.

To strengthen the integration of the primary and secondary systems, a concept of Cyber Physical Systems (CPS) is introduced to construct a CPS in Power Systems (Power CPS). The most basic work of the Power CPS is to build an integration model which combines both a continuous process and a discrete process. The advanced form of smart grid, the Active Distribution Network (ADN) is a typical example of Power CPS. After designing the Power CPS model architecture and its application in ADN, a Hybrid System based model and control method of Power CPS is proposed in this paper. As an application example, ADN flexible load is modeled and controlled with ADN feeder power control by a control strategy which includes the normal condition and the underpowered condition. In this model and strategy, some factors like load power consumption and load functional demand are considered and optimized. In order to make up some of the deficiencies of centralized control, a distributed control method is presented to reduce model complexity and improve calculation speed. The effectiveness of all the models and methods are demonstrated in the case study.

Uncertainty is an inherent characteristic of actual control systems since their parameters are not fixed and may change based on external factors. The behaviour and performance of control systems can be strongly impacted by these unknown characteristics. Variability in the environment, ageing of core components, and manufacturing tolerances are some of the factors that contribute to this uncertainty and pose an unknown risk to control systems. As a result, real control systems' intrinsic parameter indeterminacy may have an impact on how well they operate. Real-life scenarios are often ambiguous and complex, posing challenges for decision-makers to articulate their opinions. Neutrosophic sets are employed in such contexts to handle indeterminacy effectively. Neutrosophic theory suggests a variable interval based on truth, indeterminacy, and falsity, allowing for the expression of both determinate and indeterminate information. Nevertheless, neutrosophic sets have not yet been used in the modelling, analysis, and design of uncertain control systems with determinate parameters. To close this gap, a new neutrosophic design technique is established in this study by introducing single-valued neutrosophic systems. The suggested control design method is illustrated with a numerical example employing neutrosophic parameters.

This book treats analysis and design of piecewise linear control systems. In this chapter, we lay the foundation for the analysis by presenting the mathematical model on which the subsequent developments will be based. We derive an explicit matrix representation of the model and discuss solution concepts. We extend modeling techniques for uncertain linear systems to piecewise linear systems and show how norm-bound uncertainties and smooth nonlinearities can be treated rigorously in this framework. Finally, we note that piecewise linear systems enjoy important interconnection properties that allow complex systems to be constructed from the interconnection of simpler subsystems, and discuss memory-efficient model representations.

This paper studies the adaptive output feedback control problem for single-input, single-output piecewise linear systems with switched parameters. By employing the knowledge of the time instant indicator functions of system parameter switching, a new controller structure parametrization is proposed for the development of a stable adaptive control scheme which is capable of reducing the modeling error in the estimation error signal used for parameter adaptive laws. This key feature is achieved by the new control scheme's ability to avoid a major parameter swapping term in the error model, with the help of indicator functions whose knowledge is available in many applications. It is shown that under the usual slow system parameter switching condition, closed-loop stability (signal boundedness) and small output tracking error in the mean square sense are achieved. Simulation results on linearized NASA GTM models are presented, demonstrating the effectiveness and performance improvement of the proposed adaptive control scheme.

Hybrid control systems contain two distinct types of systems, continuous state and discrete-state, that interact with each other. Their study is essential in designing sequential supervisory controllers for continuous-state systems, and it is central in designing control systems with high degree of autonomy. After an introduction to intelligent autonomous control and its relation to hybrid control, models for the plant, controller, and interface are introduced. The interface contains memoryless mappings between the supervisor's symbolic domain and the plant's nonsymbolic state space. The simplicity and generality afforded by the assumed interface allows us to directly confront important system theoretic issues in the design of supervisory control systems. such as determinism, quasideterminism, and the relationship of hybrid system theory to the more mature theory of logical discrete event systems.

The dynamic performance of base isolation systems provides an effective means to reduce the seismic vulnerability of buildings. However, large displacements in the base isolation during seismic events pose a major challenge for this control system. Recently, novel control systems have been implemented to control and reduce the excessive displacements. This study presents an evaluation of the seismic performance of unconnected‐fiber‐reinforced elastomeric isolators (U‐FREI) and variable orifice damper (VOD) devices used together as a hybrid control system for a low‐degree‐of‐freedom reference structure by employing real‐time hybrid simulation (RTHS) testing technique. This physical cybernetic testing methodology allows a dynamic system of interest to be substructured into two components: numerical and experimental. In this study, the numerical substructure corresponds to a 2‐degree of freedom model, representing a reference structure, and an experimental substructure was formed using the hybrid control system described. To develop an interface between the two substructures, a horizontal transfer system (HTS) was implemented and coupled with a vertical transfer system (VTS). Using RTHS, the performance of the reference structure and the hybrid control system was evaluated under six seismic events: CAM, CAT, El Centro, Loma Prieta, Pizarro, and Chihuahua, at a maximum acceleration of 0.10 g (0.981 m·s−2). The experimental behavior of the hybrid control system allows the reference structure to reduce its maximum drift by more than 24% compared to the fixed structure. Finally, the performance and accuracy of the RTHS tests were calculated.

The increasing relevance of areas such as real-time and embedded systems, pervasive computing, hybrid systems control, and biological and social systems modeling is bringing a growing attention to the temporal aspects of computing, not only in the computer science domain, but also in more traditional fields of engineering.This article surveys various approaches to the formal modeling and analysis of the temporal features of computer-based systems, with a level of detail that is also suitable for nonspecialists. In doing so, it provides a unifying framework, rather than just a comprehensive list of formalisms.The article first lays out some key dimensions along which the various formalisms can be evaluated and compared. Then, a significant sample of formalisms for time modeling in computing are presented and discussed according to these dimensions. The adopted perspective is, to some extent, historical, going from “traditional” models and formalisms to more modern ones.

Hybrid actuator–damper–bracing control (HDABC) system with intelligent strategy and soil–structure interaction

In the new global economy, automation technologies play an important role in the industry, agriculture, transportation and daily life in the 4.0 era. Generally, studying the theory of automatic control involves the design of the dynamic control system. Students have to understand the system and be able to create mathematical models or models of control systems, both open-loop control system and closed-loop control systems, based on the knowledge of physics, mathematics, and differential equations. They have to use all knowledge to design a control system that provides the desired results by simulating the system with computer software, which is difficult to understand and practice their skills for real implementation. By using the teaching materials that emphasize hands-on practice along with theoretical studies, is very important in creating practical experience and skills. This research has developed learning materials in designing a basic automatic control system by systematically designing and building a controller with the appropriate actuators and sensors. The designing steps are clear and easy to understand include of application of control laws, programming the controller via microcontrollers, and using a Graphic User Interface (GUI) to display the response of the system. The learning materials can compare the results from the different control systems by adjusting the parameters of the dynamics system. Students can experiment with adjusting the parameters of the controller, which makes them understand the automatic control system through actual hands-on operations with the low cost and easy to produce teaching materials.

Context. The question of increasing the productivity of technological processes of extraction, processing and preparation of raw materials, improving product quality, reducing energy consumption, as well as creating safe working conditions during technological processes and preventing accidents is always quite relevant and requires the implementation of modern control and management systems. For the effective operation of such systems, it is important to pre-process and filter the data received from the sensors for monitoring the grinding processes and the explosive status of objects. One of the possible ways to increase the informativeness of data is the use of singular spectral analysis.Objective. Increasing the efficiency of technological process control systems and the reliability of explosive control systems of coal mines and oil and fuel complex facilities by processing and pre-filtering data received from sensors for monitoring grinding processes and the state of facilities.Method. To analyze the output signals of sensors used in control and management systems, the method of singular spectral analysis is used, which allows revealing hidden structures and regularities in time series by pre-filtering and data processing of acoustic, thermocatalytic, and semiconductor sensors.Results. A new approach to the management of technological processes of grinding raw materials in jet mills and control of the explosiveness of coal mines and objects of the oil and fuel complex is proposed, based on methods that allow to speed up the processing speed of sensor output data and improve the quality of information. It is shown that one of the promising methods that can be used for the pre-processing of time series of output data of sensors in control and control systems is the method of singular spectral analysis, the use of which allows filtering data, revealing hidden structures and regularities, and forecasting changes based on the analysis of previous information , identify anomalies and unusual situations, make more informed decisions and improve the processes of managing technological processes.Conclusions. The conducted experiments have confirmed the proposed software operability and allow recommending it for use in advancing both theoretical and practical aspects of process control systems through an enhanced singular spectral analysis (SSA) method for time series processing. This improved approach has been successfully demonstrated in real-world applications, including grinding processes in jet mills and explosion monitoring in coal mines and oil and fuel facilities. The implementation demonstrates a significant increase in data processing speed and information quality, which makes it particularly valuable for use in safety-critical industrial facilities.