Hybrid Control System Research Articles

A Feedback Control Strategy to Regulate the Temperature in a Nonlinear Solar Receiver

Abstract The substantial energy provided by the sun is a promising substitute for traditional heat sources in various industrial applications. However, the transient nature of solar energy still poses a significant challenge to its widespread utilization. This work presents a methodology for regulating the temperature within a solar receiver by dynamically adjusting incoming sunlight through the aperture using a controlled iris mechanism. The performance of this technique is experimentally compared with the gas flowrate control method, which is typically used in industry. The proposed control system, grounded in the physical model of the solar receiver, underwent experimental testing under varying conditions, including different gas flowrates, simulator power levels, and aperture sizes. The collected data were then analyzed to estimate a simplified model of the solar receiver. A model predictive controller (MPC) is implemented using the model estimations, and its performance was assessed by tracking two set points (335 and 325 °C) over a period of 2 h. The experimental testing of both control systems indicates the superiority of iris mechanism over gas flowrate controller in terms of robustness, settling time, and smoothness. A hybrid control system utilizing both aperture size and gas flowrate is also developed and tested during the operation of the solar receiver via computer simulations.

Uniform exponential stability approximations of semi‐discretization schemes for two hybrid systems

The uniform exponential stabilities (UESs) of two hybrid control systems comprised of a wave equation and a second‐order ordinary differential equation are investigated in this study. Linear feedback law and local viscosity are considered, as are nonlinear feedback law and internal anti‐damping. The hybrid system is first reduced to a first‐order port‐Hamiltonian system with dynamical boundary conditions, and the resulting system is discretized using the average central‐difference scheme. Second, the UES of the discrete system is obtained without prior knowledge of the exponential stability of the continuous system. The frequency domain characterization of UES for a family of contractive semigroups and the discrete multiplier approach are used to validate the main conclusions. Finally, the Trotter–Kato theorem is used to perform a convergence study on the numerical approximation approach. Most notably, the exponential stability of the continuous system is derived by the convergence of energy and UES, which is a novel approach to studying the exponential stability of some complex systems. Numerical simulation is used to validate the effectiveness of the numerical approximating strategy.

Collaborative human and computer controls of smart machines – A proposed hybrid control

Human-Machine Interaction (HMI) and Brain-Computer Interface (BCI) are evolving technologies that show the great potentials to extract and utilize humans’ intents in controlling smart machines. However, existing HMI and BCI technologies are limited in terms of (1) the number of Degrees- of-Freedom (DoF) to be controlled and (2) the ways the performance of BCI-enabled control systems are verified and validated. This study aimed to explore the solutions to addree both of above concerns; we proposed a hybrid control system that is capable of training, detecting, and interpreting humans’ intents, and utilizing humans’ intents in real-time controls of smart machines. More specifically, the system acquired brain signals in the form of Electroencephalography (EEG) by an Emotiv Epoc X and processed these signals to detect and extract humans’ intents in real-time machine controls. To cope with the frequency difference of humans’ thinking and machine motion controls, we developed a hybrid control module to fuse humans’ and machine's intelligence so that low-frequency humans’ intents could be used in real-time machine controls. The system was prototyped and verified experimentally. The system was verified to achieve the accuracy of over 90 % in recognizing humans’ intents and controlling a robot by the operator's intents with a satisfactory responding time and accuracy.

Ultrasonic Obstacle Avoidance and Full-Speed-Range Hybrid Control for Intelligent Garages.

In the current study, which focuses on the operational safety problem in intelligent three-dimensional garages, an obstacle avoidance measurement and control scheme for the AGV parking robot is proposed. Under the premise of high-precision distance detection using Kalman filtering, a mathematical model of a brushless DC (BLDC) motor with full-speed range hybrid control is established. MATLAB/Simulink (R2022a) is used to build the control model, which has dual closed-loop vector-controlled motors in the low- to medium-speed range, with photoelectric encoders for speed feedback. The simulation results show that, at lower to medium speeds, the maximum overshoot of the output response curve is 1.5%, and the response time is 0.01 s. However, at higher speeds, there is significant jitter in the speed output waveform. Therefore, the speed feedback is switched to a sliding mode observer (SMO) instead of the original speed sensor at high speeds. Experiments show that, based on the SMO, the problem of speed waveform jitter at high motor speeds can be significantly improved, and the BLDC motor system has strong robustness. The above shows that the motor speed under the full-speed range hybrid control system can meet the AGV control and safety requirements.

Emulation Structures and Control of Wind-Tidal Turbine Hybrid Systems for Saudi Arabia Off-shore Development

Emulation Structures and Control of Wind-Tidal Turbine Hybrid Systems for Saudi Arabia Off-shore Development

Additional damping control for multi-terminal AC-DC hybrid system based on reachability analysis

Abstract The application proportion of multi-terminal AC-DC hybrid power distribution systems is constantly increasing. In order to describe the operation status and trajectory of the system and judge whether the system can maintain stable and safe operation, in this paper, the differential algebraic equation is firstly established by mathematical and physical methods for a typical multi-terminal AC-DC hybrid power distribution system. Then, the reachable set in the operation process is analyzed according to the reachability analysis calculation method to judge the stable operation status. An additional damping control method is proposed, which can effectively restrain the negative impedance characteristics of the DC micro-grid and improve the operation stability of the system. Finally, the improvement performance of the control method is verified by combining the reachability analysis results.

The improved force/position hybrid control system for multi joint humanoid multi fingered dexterous hand

The improved force/position hybrid control system for multi joint humanoid multi fingered dexterous hand

Hybrid control systems: from ontological reflection to conceptualization of the phenomenon

As the practices of using digital means in various spheres of life and at the levels of ensuring its regulation, it becomes more and more urgent to consider the nature and consequences of hybridization of social and digital relations, the results of which are now manifested at all levels of social management: social (interaction of the state and society), organizational (managerial, sectoral), local (territorial and interpersonal interaction). In this context, a dual perspective of hybridization emerges: technocratic (unification, modernization, and control from above). In this context, a dual perspective of hybridization arises: technocratic (unification, modernization and control from above) and socio-contextual (taking into account needs and interests from below, development taking into account life strategies). From a technocratic perspective, hybridization can be viewed as an idealized product (object) of the evolution of artificial regulatory systems, as the embodiment of the ideal form, structure of algorithmization of human activity, and follow the unfolding path of technocratization of public life. However, already today we are witnessing failures in the processes of digital transformation of territories and in the operation of hybrid management systems, the purpose and functionality of which are more likely to be configured to build and reproduce technocratic principles of management organization, which gives rise to the imperatives and determination of hybrid objects over hybridizing management entities, reduces sensitivities to spontaneous processes of socio-network grouping. The latter phenomenon is proposed to be considered and studied as a mechanism for launching the formation (localization) of hybrid control systems within the framework of the theoretical construct of the sociology of control called the "sociocultural body of problem solving".

Digital-analog hybrid control of an inverted pendulum robot based on memristor

Abstract Single inertial measurement unit sensor signals are inaccurate, and traditional digital processing systems with separate storage and computation architectures cannot directly handle analog sensor signals, leading to significant delays and high power consumption. In that case, the control capability for self-balancing robots has been limited. In this work, a three-terminal MoS2-based memristor device was fabricated and successfully integrated into a memristor-based hybrid control inverted pendulum robot system. This system achieved continuous multi-sensor analog data fusion computations and analog PD control computations by combining the least squares method and a gradient descent algorithm based on a reward mechanism in digital circuits to control the memristors dynamically. Additionally, simulation testing and real, inverted pendulum robot control confirmed the efficacy of its data fusion and the accuracy of its PD control. The comparative experiments demonstrated that the hybrid control system based on memristors was approximately 86.41 mJ lower than the digital system. This system holds significant importance for the processing of large-scale sensor data and robot control.

Hybrid Control System for a Vertically Guided Bomb During Self-guidance in Turbulent Conditions

The article presents a mathematical model and an algorithm for hybrid control of a bomb aimed at a moving ground target. The guided bomb flight control system subject to the study combines a conventional PD controller and a quasi-sliding mode (QSM) controller. The target trajectory was determined based on the kinematic relations of the mutual motion of the bomb and the ground target using the proportional approach method. The main aim of the article is to analyse the impact of atmospheric turbulence on the flight of a guided bomb, and then to determine its impact on homing parameters, such as homing time and accuracy of hitting a ground target. The numerical studies covered three types of controllers: conventional PID, quasi-sliding and hybrid. The effectiveness of the proposed control system was analysed without and during random atmospheric turbulence. The article examines the hybrid system’s properties for controlling guided bombs. Numerical studies were performed using the Matlab/Simulink software suite. The article presents selected results of this computer simulation.

Hamilton–Jacobi theory for nonholonomic and forced hybrid mechanical systems

A hybrid system is a system whose dynamics is given by a mixture of both continuous and discrete transitions. In particular, these systems can be utilized to describe the dynamics of a mechanical system with impacts. Based on the approach by Clark [Invariant measures, geometry and control of hybrid and nonholonomic dynamical systems, Ph.D thesis, University of Mirchigan (2020)], we develop a geometric Hamilton–Jacobi theory for forced and nonholonomic hybrid dynamical systems. We state the corresponding Hamilton–Jacobi equations for these classes of systems and apply our results to analyze some examples.

Youla Parameterized Adaptive Hybrid Control for Active Vibration Control Platform

Abstract To increase the vibration attenuation of the damped platforms, an adaptive hybrid vibration control algorithm based on Youla (Q) parameterization is proposed, where the vehicle suspension platform is considered as an application example. First, an inner controller is designed to keep the hybrid control system stable. Then, the controller is extended to the Youla parameterized controller set. Finally, the Q-parameters are adjusted online by using a recursive least squares (RLS) adaptive algorithm to eliminate vibration disturbances. Simulation results demonstrate that the hybrid algorithm is capable of effectively suppressing the vibration signals.

The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.

Power quality improvement in distribution generation with renewable energy sources using hybrid GBDT-BCMO approach

ABSTRACT This paper presents a hybrid control system designed to improve power quality in grid-integrated hybrid renewable energy sources (HRES). The proposed hybrid control scheme is a hybrid approach gradient boosting decision tree and balancing composite motion optimisation, named GBDT-BCMO. The goals outlined above are the focus of GBDT-BCMO. The GBDT machine learning technique is enhanced using the BCMO method. GBDT method is trained on inputs like previous instantaneous energy of obtainable sources and current time required load demand based on target reference power. Based on load variation, GBDT-BCMO control system creates PI controller gain parameters to generate optimal control signal and manage HRES energy. The prediction process of the present method undertakes variations on system parameters, like reactive and real power and DC voltage. The proposed GBDT-BCMO system improves the power system damping and creates line voltage, giving reactive power compensation. The proposed methodology is then carried out in MATLAB/Simulink platform, and the performance is analysed. The behaviour of the proposed system is related with other systems. The proposed method ranges from 0 to 22.1 V. The BCMO system ranges from 0 to 22. The ALO system ranges from 0 to 22 V. The BAT Algorithm ranges from 0 to 22.0 V.

Development of a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild Hybrid Electrified Vehicle’s Electrified Propulsion System

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development.

Optimal Control and Optimization of Grid-Connected PV and Wind Turbine Hybrid Systems Using Electric Eel Foraging Optimization Algorithms.

This paper presents a comprehensive exploration of a hybrid energy system that integrates wind turbines with photovoltaics (PVs) to address the intermittent nature of electricity production from these sources. The necessity for such technology arises from the sporadic nature of electricity generated by PV cells and wind turbines. The envisioned outcome is an emissions-free, more efficient alternative to traditional energy sources. A variety of optimization techniques are utilized, specifically the Particle Swarm Optimization (PSO) algorithm and Electric Eel Foraging Optimization (EEFO), to achieve optimal power regulation and seamless integration with the public grid, as well as to mitigate anticipated loading issues. The employed mathematical modeling and simulation techniques are used to assess the effectiveness of EEFO in optimizing the operation of grid-connected PV and wind turbine hybrid systems. In this paper, the optimization methods applied to the system's architecture are described in detail, providing a clear understanding of the intricate nature of the approach. The efficacy of these optimization strategies is rigorously evaluated through simulations of diverse operating scenarios using MATLAB/SIMULINK. The results demonstrate that the proposed optimization strategies are not only capable of precisely and swiftly compensating for linked loads, but also effectively controlling the energy supply to maintain the load's power at the desired level. The findings underscore the potential of this hybrid energy system to offer a sustainable and reliable solution for meeting power demands, contributing to the advancement of clean and efficient energy technologies. The results demonstrate the capability of the proposed approach to improve system performance, maximize energy yield, and enhance grid integration, thereby contributing to the advancement of renewable energy technologies and sustainable energy systems.

К выбору периода дискретизации нелинейных гибридных систем управления

Hybrid systems are often used in various technical applications, such as robotics, aviation, space, energy, etc. The emergence of hybrid control systems is due to the use of computers to implement control laws, including nonlinear ones. Digital means cannot implement continuous control laws. However, the known methods of design, especially of nonlinear systems, lead precisely to continuous controls, which caused the need to discretize the continuous control with the largest possible period. Solving the problem of determining the maximum permissible sampling period of a nonlinear control system is a rather complex stage of its creation. In this article the problem of definition of the maximum allowed sample period of the nonlinear hybrid system control and its dependence on the module of the maximum eigenvalue of the functional matrix of quasilinear model of the continuous system is also considered. The nonlinear hybrid system is created by discretizing the control of the nonlinear continuous system. This continuous system is synthesized using the algebraic polynomial and matrix method of the nonlinear control systems design in which quasilinear models are used. It has been is established that the value of the maximum permissible sample period depends not only on the module of the maximum eigenvalue, but also on initial conditions and external influences. These dependences are complex and it is difficult to find them theoretically. Experimentally, based on the example of the specific nonlinear hybrid control system it is shown that eigenvalues smaller on the module lead to larger values of the maximum of the permissible sample period. The problem of choosing the sample period of control laws of the real hybrid control systems can be solved in the similar way based on a compromise between the system high-speed performance and the sample period.

Temporal Logic Explanations for Dynamic Decision Systems Using Anchors and Monte Carlo Tree Search (Abstract Reprint)

For many automated perception and decision tasks, state-of-the-art performance may be obtained by algorithms that are too complex for their behavior to be completely understandable or predictable by human users, e.g., because they employ large machine learning models. To integrate these algorithms into safety-critical decision and control systems, it is particularly important to develop methods that can promote trust into their decisions and help explore their failure modes. In this article, we combine the anchors methodology with Monte Carlo Tree Search to provide local model-agnostic explanations for the behaviors of a given black-box model making decisions by processing time-varying input signals. Our approach searches for descriptive explanations for these decisions in the form of properties of the input signals, expressed in Signal Temporal Logic, which are highly likely to reproduce the observed behavior. To illustrate the methodology, we apply it in simulations to the analysis of a hybrid (continuous-discrete) control system and a collision avoidance system for unmanned aircraft (ACAS Xu) implemented by a neural network.

BIZON–UGV for Airport Pavement Testing: Mechanics and Control

The paper presents a study of the performance and development of unmanned ground vehicles (UGVs), establishing mathematical and numerical models of the chassis system. The model analysis is performed by 3D software package SolidWorks 2018 with finite element discretization. The mesh modelling and analysis are focused on studying the strength and stiffness of the robotic platform chassis and the distribution of stress and deformation in the extremal condition. The paper also presents an autopilot design with a new cascade control system for the autonomous motion of an unmanned ground vehicle based on proportional–integral–derivative (PID) and feedforward (FF) control. The PID-FF controller is part of a UGV used in a hybrid control system for precise control and stabilization, which is necessary to increase the vehicle motion stability and maneuver precision. The hybrid PID-FF control system proposed for the ground vehicle model gives satisfactory control quality while maintaining the simplicity of the control system. The presented tests performed in mechanical design and control analysis give good results and prove the usefulness of the designed unmanned device.

Institutional Theory and Hybrid Accounting and Control Systems

ABSTRACT We identify several manifestations of hybridity in accounting and control systems. Hybridity in the form of multiple accounting systems and actual or postural conformity to institutional expectations can enable organizations to overtly, but sometimes ostensibly, combine multiple logics to appease stakeholders. Hybridity increases costs and the risk of internal inconsistency. Consequently, firms decouple some practices to provide an impression of conformance. We offer a typology of three forms of hybridity—compliance, complete decoupling, and partial decoupling—and illustrate using examples from accounting hybridization choices regarding corporate social responsibility (CSR), diversity, equity, and inclusion (DEI), and international reporting standards. We empirically examine hybridity in the context of the voluntary adoption of international financial reporting standards (IFRS). We find that instrumental pressures are associated with adoption through compliance; however, social pressures are likely to be placated through complete decoupling, whereby firms voluntarily adopt multiple systems in policy, but not in practice. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: B50; L21; M41.