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This paper presents a comprehensive tutorial on the identification process for a class of continuously dynamic systems expressed in state-space form using the model reference adaptive approach. The proposed algorithm does not require prior knowledge of the systems but needs all state variables to adapt the estimated parameters. Through simulations using m-script code, software-in-the-loop (SIL), and hardware-in-the-loop (HIL) simulations, the effectiveness of the proposed method in identifying the system model of a DC motor is evaluated. Simulation results demonstrate consistency across various platforms. Steady-state estimated models can be achieved using the proposed estimation algorithm with adaptation gains of [Formula: see text] after 5 s. Furthermore, this paper demonstrates the implementation of the proposed method on both SIL and HIL platforms, using Python and MicroPython programming languages, respectively. This approach leverages the Numpy library for efficient matrix computations. It is evident that the proposed estimation algorithm is readily applicable in real-world scenarios.

This paper presents a comprehensive tutorial on the identification process for a class of continuously dynamic systems expressed in state-space form using the model reference adaptive approach. The proposed algorithm does not require prior knowledge of the systems but needs all state variables to adapt the estimated parameters. Through simulations using m-script code, software-in-the-loop (SIL), and hardware-in-the-loop (HIL) simulations, the effectiveness of the proposed method in identifying the system model of a DC motor is evaluated. Simulation results demonstrate consistency across various platforms. Steady-state estimated models can be achieved using the proposed estimation algorithm with adaptation gains of diag ([ 100 100 ]) after 5 s. Furthermore, this paper demonstrates the implementation of the proposed method on both SIL and HIL platforms, using Python and MicroPython programming languages, respectively. This approach leverages the Numpy library for efficient matrix computations. It is evident that the proposed estimation algorithm is readily applicable in real-world scenarios. [ABSTRACT FROM AUTHOR]

Hardware-in-the-Loop (HiL) testing has emerged as a pivotal technique for validating and enhancing the performance of control systems across various industries, notably in automotive and embedded manufacturing sectors. This paper provides an extensive exploration of HiL testing methodologies and their application in the Internet of Things (IoT) domain. HiL testing involves simulating real-world conditions by integrating specialized simulation software and hardware components, facilitating realistic evaluation of controllers' functionality and behaviour. Different variants of HiL, such as Model-in-the-Loop (MiL), Software-in-the-Loop (SiL), Processor-in-the-Loop (PiL), and Hardware-in-the-Loop (HiL), are discussed within the broader context of XiL (Anything-in-the-Loop) models. The paper underscores the significance of HiL testing in optimizing the development process, reducing costs, and minimizing hazards. It elaborates on the utilization of HiL testing to validate intricate control algorithms, simulate fault scenarios, and assess system behaviour under diverse conditions. The integration of HiL testing with IoT applications is examined, highlighting the potential benefits in areas like smart cities, industrial IoT, healthcare, agriculture, and energy management. The study presents a novel fault injection framework within HiL testing, allowing real-time analysis of complex systems' responses to simulated faults. This framework aids in identifying vulnerabilities and evaluating system reliability, thereby enhancing safety and performance. The authors introduce the concept of sensor schema, Observations & Measurements (O&M), and Sensor Web Enablement (SWE) as key components for efficient sensor data integration, sharing, and interoperability. The paper concludes by emphasizing the indispensable role of Hardware-in-the-Loop testing in fostering innovation, safety, and efficiency in the IoT landscape. By offering a comprehensive overview, analysis, and practical insights into the application of HiL testing methodologies, this research contributes to the advancement of robust and reliable IoT systems. [ABSTRACT FROM AUTHOR]

Vector control plays a critical role in a permanent magnet synchronous motor (PMSM) drive to deliver the desired torque in electrified vehicle applications. Motor speed and stator current control depend on various nonlinear motor parameters that influence the performance of PMSM. Moreover, tuning of speed and current controller parameters using conventional control techniques also depends on these PMSM parameters. To enhance the robustness of vector control and tracking methodology against PMSM parameter uncertainties and load disturbances, a novel deep reinforcement learning (DRL) based advanced speed and current control technique is proposed in this article. The proposed method mitigates the effects of disturbance due to parameter variations as well as the load torque. The novel architecture delivers closed-loop reinforcement learning agents trained with the deep deterministic policy gradient learning algorithm in the plant environment where the cost of exploration is expensive. First, an overview and need for the proposed DRL vector control architecture are provided. Subsequently, the design and training methods for the proposed DRL controller are elicited. Thereafter, the proposed control scheme is validated with real-time software-in-the-loop testing under various conditions and compared against adaptive proportional–integral control of the same PMSM in OPAL-RT simulator. [ABSTRACT FROM AUTHOR]

This paper presents a controller-agent-based design framework (named OROMACS) for the construction of hierarchically structured multi-agent control system (MACS). OROMACS uses the decentralized coordination mechanism to build MACS. Each MACS can be in the form of an elementary controller-agent or a composite controller-agent. The latter consists of a group of elementary and/or composite controller-agents and a coordinator; the coordinator has a role to coordinate the activity behaviour of the whole group. The main idea in creating composite controller-agents is to consider a group of coordinated controller-agents as if it is an individual controlleragent. Then, a composite controller-agent can be used in other groups, leading to hierarchical structures of MACS. To provide real-time features for MACS, the OROMACS framework has used the real-time OROCOS software framework as running environment. The OROMACS-based design method is illustrated through a design of MACS for a single-axis electro-mechanical motion system using a linear motor with the dominant compliance in the transmission. The process starts with the design of an Operation Controller Agent and then reusing its whole design to develop a more complex system called Safe-Guarded MACS. Software-In-the-Loop (SIL) co-simulation results have demonstrated the applicability of the design method. [ABSTRACT FROM AUTHOR]

Integrated charging for electric vehicles (EVs) is considered as a good transitional scheme to expand the mileage range under the battery capacity limit. It can save the costs and reduce the volume by replacing the on-board charger, and can increase the charging power by reusing the high power propulsion system. Then, the charging and propulsion system will therefore be light and compact. As the charging topology is based on the propulsion system, the design degrees of freedom are not very high, further resulting in limited application. And the laws of topological construction are lacking. Therefore, most of the integrated charging schemes are designed for a specific occasion and are difficult to adopt for other applications. In this article, a general derivation law with a unified design criterion is proposed to improve the generality of integrated charging, by considering the charging torque and equivalent filter inductance of the multiplexed converter and motor. The criterion is illustrated and analyzed in a specific scheme that is applied in the Toyota plug-in hybrid EV. The simulation results in a software-in-the-loop environment and experimental results in a down-scaled prototype are demonstrated to confirm the validity of the unified criterion and the effectiveness of the control schemes. It is suitable for the design of integrated charging solutions with multiphase power and multiphase motors. [ABSTRACT FROM AUTHOR]

This article investigates the fuzzy model predictive control synthesis of a power buffer for dynamic stabilization of a dc microgrid (MG), which is controlled through a low-latency communication network, such as the one envisioned in fifth-generation (5G). The proposed approach employs a Takagi–Sugeno fuzzy model, a fuzzy observer, and a model predictive scheme to alleviate the effects of the 5G-network-induced delays and data loss of the sensor-to-controller and controller-to-actuator links on the dc MG plant response. By employing the so-called time-stamp technique and network delay compensator (NDC), the delays are computed, and the data loss effects are compensated, thus improving the effectiveness and robustness of the proposed controller. In addition, a time-delay-independent observer is proposed to estimate the states of the constant power loads (CPLs) and the power buffer, based on the measured information. Due to the usage of two NDCs, the presented approach is robust against the network delays and results in a small computational burden. To show the merits of the proposed approach, it is applied to a dc MG that feeds two CPLs. Results show the simplicity of designing the observer-based controller and better robustness against the network delays, compared with the state-of-the-art methods. Additionally, software-in-the-loop simulations are presented to prove the practical applicability of the proposed controller. [ABSTRACT FROM AUTHOR]

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a non-iterative state feedback design approach for polynomial systems using polynomial Lyapunov function based on the sum of squares (SOS) decomposition. The polynomial Lyapunov matrix consists of states of the system leading to the non-convex problem. A lower bound on the time derivative of the Lyapunov matrix is considered to turn the non-convex problem into a convex one; and hence, the solutions are computed through semi-definite programming methods in a non-iterative fashion. Furthermore, we show that the proposed approach can be applied to a wide range of practical and industrial systems that their controller design is challenging, such as different chaotic systems, chemical continuous stirred tank reactor, and power permanent magnet synchronous machine. Finally, software-in-the-loop (SiL) real-time simulations are presented to prove the practical application of the proposed approach. [ABSTRACT FROM AUTHOR]

The software-in-the-loop and hardware-in-the-loop tests of a battery management system require a real-time compatible electro-thermal battery pack model. In our study, a numerically complex electrochemical-thermal model has been characterized from experimental data of a nickel-rich, silicon-graphite 18650-type lithium-ion cell. While it accurately represents the electro-thermal battery behavior, it is hardly suitable for real-time application due to its intensively numerical solving effort and related calculation time if no huge numerical efforts are applied to reduce the model. The objective of this paper is to present a simple method to derive a reduced-order electro-thermal cell model from the complex electrochemical-thermal cell model and build a real-time compatible battery pack model with the reduced-order cell model.

The energy management strategy (EMS) or power management strategy (PMS) unit is the core of power sharing control in the hybridization of automotive drivetrains in hybrid electric vehicles (HEVs). Once a new topology and its corresponding EMS are virtually designed, they require undertaking different stages of experimental verifications toward guaranteeing their real-world applicability. The present paper focuses on a new and less-extensively studied topology of such vehicles, HEVs equipped with an electrical variable transmission (EVT) and assessed the controllability validation through hardware-in-the-loop (HiL) implementations versus model-in-the-loop (MiL) simulations. To this end, first, the corresponding modeling of the vehicle components in the presence of optimized control strategies were performed to obtain the MiL simulation results. Subsequently, an innovative versatile HiL test bench including real prototyped components of the topology was introduced and the corresponding experimental implementations were performed. The results obtained from the MiL and HiL examinations were analyzed and statistically compared for a full input driving cycle. The verification results indicate robust and accurate actuation of the components using the applied EMSs under real-time test conditions.

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. We also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, an interferometric radar altimeter (IRA) is used to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real-time on board. Finally, the performance of the proposed system is verified through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment. [ABSTRACT FROM AUTHOR]

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria. [ABSTRACT FROM AUTHOR]