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Path-tracking control occupies a critical role within autonomous driving systems, directly reflecting vehicle motion and impacting both safety and user experience. However, the ever-changing vehicle states, road conditions, and delay characteristics of control systems present new challenges to the path tracking of autonomous vehicles, thereby limiting further enhancements in performance. This article introduces a path-tracking controller, time-varying gain-scheduled path-tracking controller with delay compensation (TGDC), which utilizes a linear parameter-varying system and optimal control theory to account for time-varying vehicle states, road conditions, and steering control system delays. Subsequently, a polytopic-based path-tracking model is applied to design the control law, reducing the computational complexity of TGDC. To evaluate the effectiveness and real-time capability of TGDC, it was tested under a series of complex conditions using a hardware-in-the-loop platform. The results demonstrate that through the polytopic-based path-tracking model and delay compensation strategy in TGDC, it can effectively enhance path-tracking performance with minimal computational load, even under conditions of parameter variability and control delays.

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

This paper introduces a sensorless nonlinear control scheme based on feedback linearization control (FLC) of an open-end winding five-phase induction motor (OeW-5PIM) topology fed by a dual neutral point clamped (NPC) inverter. The suggested sensorless control is combined with the sliding mode (SM) controller to improve the dynamic performance (i.e., rising time, overshoot, etc.) of the studied motor. Furthermore, a stator-current-based model reference adaptive system (SC-MRAS) estimator is designed for the estimation of the rotor flux and the motor speed. In parallel, to enhance the robustness of the designed sensorless control against motor parameter changes, an adaptive estimation method is suggested to estimate the rotor and stator resistances during low-speed ranges. The estimation method of motor resistances is associated with the suggested sensorless control to further improve the speed estimation accuracy and minimize the speed estimation error. Finally, the effectiveness and correctness of the suggested control with the examined estimators are validated in real-time implementation using a hardware-in-the-loop (HIL) based on the dSpace 1103 board.

With the widespread application of Ultra-High Voltage Direct Current (UHVDC) transmission in power delivery, the demand for stability research on complex systems featuring concentrated DC power sending and feeding has become increasingly prominent. Based on a Real-Time Digital Simulation (RTDS) platform, this paper proposes a hardware-in-the-loop simulation scheme for interconnected multi-UHVDC systems. This scheme aims to enable the coupled operation analysis of multi-DC systems. It provides a research foundation for complex simulation requirements such as renewable energy integration and flexible DC interconnection. The research integrates three heterogeneous RTDS platforms, employing global bus microsecond-level timing synchronization and low-latency IRC optical fiber communication technology to overcome traditional “data silo” limitations and construct a high-fidelity environment for cascading failure analysis. Simultaneously, an innovative multi-scale hybrid modeling method is proposed, effectively circumventing issues like the “curse of dimensionality” and loss of accuracy. Simulation test results demonstrate that the proposed multi-platform interconnection scheme can effectively support transient characteristic analysis of complex AC/DC systems, providing a high-precision tool for transient and steady-state studies of high-density multi-infeed systems. In the future, it can be extended to “double-high” power grid scenarios (characterized by a high proportion of renewables and power electronics) with large-scale renewable energy integration, supporting the safe and stable operation of new power systems.

This work describes the development of a platform to deal with simulated and real autonomous flights with rotary-wing aircrafts. Such a platform, referred to as AuRoRA Platform – Autonomous Robots for Research and Applications – contemplates hardware and software, and is designed for use with commercial miniature rotorcrafts, also embedding the instrumentation necessary to autonomously guide them. An electronic board, called AuRoRA Board , is designed and manufactured to integrate the instrumentation and the actuators already included in the commercial vehicles. AuRoRa is implemented to exchange information with such a board and an external computer (ground control station), performing as a high level Hardware-in-the-Loop platform, capable of running simulations and real experiments. In the last case it works as a ground station responsible for sending control signals to the servomotors of the aircraft. In terms of real flights, the AuRoRa Platform was already tested with the AR.Drone Parrot quadrotor and the ALIGN T-REX 450 and T-REX 600 miniature helicopters. The AuRoRa platform also has a characteristic of being a decentralized system, in terms of computer effort. It avoids overloading a single computer with the synthesis of the control signals and the online exhibition of the flight data. The user can enable such feature, and the online exhibition of the flight data start running in a second computer, using an UDP communication channel. Simulation and experimental results are run using the AuRoRa platform, some of which are presented in this paper. The most important characteristics of our proposal is the integration of modelling, control, simulation, experimentation and data display, as detailed in this manuscript.

Battery Management Systems are essential for safe and effective use of Lithium-Ion batteries. The increasing complexity of the control and estimation algorithms requires deeper functional testing and validation phases of BMSs. However, the use of real batteries in such phases leads to hazards and safety risks. Battery emulators and the Hardware-in-the-Loop approach can instead speed-up and increase the safety of the functional testing and algorithm validation phases. This work describes the design and the characterization of a low-cost modular multi-cell battery emulator which provides a complete emulation of cell voltage, temperature, and current. This platform can be used to carry out Hardware-in-the-Loop tests on custom and commercial Battery Management Systems. The paper describes the platform design constraints derived from the most diffused Battery Management System architectures, the main design and implementation choices, and the platform characterization results. The proposed emulation platform is compared with literature and commercial ones showing a very good trade-off between performance and cost. This characteristic makes it appealing for small-size laboratories that develop and test Battery Management Systems. The project has therefore been made available to the scientific community as a freely downloadable open hardware platform.

The isolated multi-port converters quad-active bridge (QAB) presents a unique opportunity to connect multiple sources and loads operating at different power and voltage levels, offering galvanic isolation and shared magnetics as advantages. However, the high number of modulation variables, dynamic response, and overall modeling complexity of QAB converters pose challenges to controller design. Traditional linear controllers often struggle with voltage overshooting and undershooting under abrupt load changes and exhibit limited dynamic performance and coupling among different ports. To address these challenges, this paper introduces a moving discretized control set-model predictive control (MDCS-MPC) strategy for QAB converters. The developed approach predicts phase shift values through the converter model, ensuring fast dynamic performance and eliminating steady-state errors in control variables. The prediction model’s embedded circuit parameters and operating modes enhance performance across various power and terminal voltage ranges. An adaptive step is implemented for quick transitions, significantly reducing computational demands. These analytical findings and the MDCS-MPC strategy are verified through Matlab simulation results and experimental results obtained from the Hardware-in-the-Loop (HIL) real-time Typhoon 602 platform. Both experimental and simulation results demonstrate the effectiveness of the developed strategy, showing superior dynamic response, robustness, and reduced computational requirements. Furthermore, the voltage achieves a very fast dynamic response and exhibits no significant voltage overshoot or undershoot.

Battery Management Systems are fundamental components of the present battery generation. The development and characterization phases of a BMS often require an emulator of the battery cells with which the Battery Management System functions can be assessed with no safety risks as it would instead happen using a real battery. This work describes the design and characterization of a modular cell emulator circuit to be used as platform for the Hardware-in-the-loop test of a Battery Management System. The design constraints and choices are first described. Then, the experimental characterization of the cell emulator is shown and discussed. The proposed circuit shows a voltage resolution of 76 μV, an accuracy of 2.17 mV, and a setting time of 340 μs. Its cost is around 40 USD. The circuit results to be a very good trade-off between performance and cost. The Project is available to the scientific community as open hardware platform freely downloadable. It could be useful to small-size laboratories to self-produce a low-cost battery emulator with good performance for the development and the functional test of custom Battery Management Systems.

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.

The battery management system (BMS) is a core component to ensure the efficient and safe operation of electric vehicles, and the practical evaluation of key BMS functions is thus of great importance. However, the testing of a BMS with actual battery packs suffers from a poor testing repeatability and a long status transition time due to the uncontrollable degradation of battery systems and testing environment variations. In this paper, to overcome this challenge, we propose an efficient BMS testing framework that uses virtual battery packs rather than actual ones, thus enabling a rapid and accurate evaluation of a BMSs key functions. A series-connected virtual battery pack model through leveraging Copula’s method is formulated to capture the dynamics and inconsistency of individual batteries in the pack. The developed lithium iron phosphate model features low computational efforts and is experimentally validated with different dynamical profiles, implying a high-precision virtual battery pack that is capable of reproducing the actual one. Furthermore, this framework includes a closed-loop testing platform, which can provide the state-of-charge/state-of-power references and thus automatically test and evaluate the states of the battery packs estimated from the BMS. Particularly, we consider the initial polarization that often exists in the batteries during the operation to accurately calibrate the available state-of-power benchmark of battery packs in the real world. The performed BMS testing results using the proposed framework illustrate that the tested BMS cannot adapt to the varied operation conditions, thus leading to high state estimation errors, which may result in the over-charge/discharge or over-temperature of the batteries. Therefore, this work highlights the value of effective BMS testing, providing the promising potential to achieve reliability and durability for battery systems.