Search Results (10)

Electric vehicle (EV) drivetrains often suffer from degraded control precision due to resolver zero-position deviation. This issue becomes particularly critical under diverse automotive-grade operating conditions, posing challenges for achieving reliable and efficient drivetrain performance. To tackle this problem, we propose a dual-mode PID dynamic compensation control methodology. This approach establishes a divide-and-conquer framework that differentiates between weak-magnetic and non-weak-magnetic regions. It integrates current loop feedback with a fuzzy self-tuning mechanism, enabling real-time dynamic compensation of the resolver’s initial angle. To ensure system stability under extreme automotive conditions (−40 °C to 125 °C, ±0.5 g vibration, and electromagnetic interference), a triple-redundancy architecture is implemented. This architecture combines hardware filtering, software verification, and fault diagnosis. Our contribution lies in presenting a reliable solution for intelligent EV drivetrain calibration. The proposed method effectively mitigates resolver zero-position deviation, not only enhancing drivetrain performance under challenging automotive environments but also ensuring compliance with ISO 26262 ASIL-C safety standards. This research has been validated through its implementation in a 3.5-ton commercial logistics vehicle by a leading automotive manufacturer, demonstrating its practical viability and potential for widespread adoption in the EV industry. Full article

This paper presents a comprehensive study of an ESP32 microcontroller-based self-balancing mobile robot system designed in conjunction with an Android app for Bluetooth control. The robot employs an MPU6050 accelerometer/gyroscope to execute dynamic equilibrium control for robotic balance. This study explores the design of a system composed of an ESP32-based dual-platform architecture. The firmware for the ESP32 executes real-time motor control and sensor processing, while the Android application provides the user interface, data visualization, and command transmission. The system achieves stable operation with tilt angle variations of ±2.5° ($\sigma =0.8°$, n = 50 trials) during normal operation with a PID controller tuned to KP = 6.0, KI = 0.1, and KD = 1.5. In experimental tests, control latency was measured at 38–72 ms (mean = 55 ms, $\sigma =12$ ms) over distances of 1–10 m with a robust Bluetooth connection. Extended operational tests indicated the reliability of both autonomous obstacle avoidance mode and manual control exceeding 95%. Key contributions include gyro drift compensation using a progressive calibration scheme, intelligent battery management for operational efficiency, and a dual-mode control interface to facilitate seamless transition between manual and autonomous operation. Processing of real-time telemetry on the Android application allows visualization of important parameters like tilt angle, motor speeds, and sensor readings. This work contributes to a cost-effective mobile robotics platform (total cost: USD 127) through the provision of detailed design specifications, implementation strategies, and performance characteristics. Full article

This paper introduces a self-consistent field-null optimization algorithm of a poloidal magnetic field that precisely accounts for the influence of vacuum vessel eddy currents. Building on existing poloidal field (PF) coil currents, the algorithm can refine these waveforms to achieve various target field-null configurations. Firstly, based on the TokSys toolbox, a response model, including the PF coils and vacuum vessel circuits for the HL-3 tokamak, is developed under the MATLAB^® and Simulink^™ framework. The resistivity parameters of the model are calibrated using experimental data obtained from single-coil discharge tests. Subsequently, an iterative method was employed to simultaneously solve the dynamic field-null optimization problem within a specified spatial region and precisely account for the effect of passive eddy currents. Typically, $\left|B_{\perp }\right|\le 1 G$ within a large area can be obtained with this iterative scheme, which can be stably sustained for over 15 milliseconds to ensure the robustness of breakdown. Finally, a low-pass filtered PID controller is applied to the model to achieve precise control of the PF coils currents, confirming the feasibility of implementing the proposed algorithm in real experiments. Full article

This paper presents a novel fuzzy-augmented model reference adaptive voltage regulation strategy for the DC–DC buck converters to enhance their resilience against random input variations and load-step transients. The ubiquitous proportional-integral-derivative (PID) controller is employed as the baseline scheme, whose gains are tuned offline via a pre-calibrated linear-quadratic optimization scheme. However, owing to the inefficacy of the fixed-gain PID controller against parametric disturbances, it is retrofitted with a model reference adaptive controller that uses Lyapunov gain adaptation law for the online modification of PID gains. The adaptive controller is also augmented with an auxiliary fuzzy self-regulation system that acts as a superior regulator to dynamically update the adaptation rates of the Lyapunov gain adaptation law as a nonlinear function of the system’s classical error and its normalized acceleration. The proposed fuzzy system utilizes the knowledge of the system’s relative rate to execute better self-regulation of the adaptation rates, which in turn, flexibly steers the adaptability and response speed of the controller as the error conditions change. The propositions above are validated by performing tailored hardware experiments on a low-power DC–DC buck converter prototype. The experimental results validate the improved reference tracking and disturbance rejection ability of the proposed control law compared to the fixed PID controller. Full article

Underground cable trench inspection robots work in narrow, variable friction coefficient, and complex road environments. The running trajectory easily deviates from the desired path and leads to a collision, or even the destruction of the robot or cable. Addressing this problem, a path-following control method for the dual-tracked chassis robot based on a self-coupling PID (SCPID) control algorithm was developed. The caterpillar robot dynamics were modelled and both the unknown dynamics and external bounded disturbances were defined as sum disturbances, thus mapping the nonlinear system into a linearly disturbed system, then the self-coupling PD (SCPD) controller was designed. The system proved to be a robust stability control system and only one parameter, the velocity factor, needed to be tuned to achieve parameter calibration. Meanwhile, to solve the problem that the error-based speed factor is not universal and to improve the adaptive ability of the SCPD controller, an iterative method was used for adaptive tuning. The simulation results showed that the SCPID can achieve better control. The field test results showed that the SCPD’s maximum offset angle was 56.7% and 10.3% smaller than incremental PID and sliding mode control (SMC), respectively. The inspection time of the SCPD was 20% faster than other methods in the same environment. Full article

The vehicle longitudinal control algorithm is the core function of the adaptive cruise control system, whose main task is to convert vehicle acceleration and deceleration requirements into vehicle driving and braking commands so that the vehicle can quickly and accurately track the desired acceleration. Traditional longitudinal control algorithms rely on accurate vehicle dynamic modeling or complex controller parameter calibrations. To overcome those difficulties, a longitudinal control algorithm based on RBF-PID is proposed in this paper. The algorithm uses the RBFNN (radial basis function neural network), which can simply and quickly approximate any complex nonlinear system, to identify the Jacobian information of the vehicle and perform parameter tuning for PID control and achieve vehicle longitudinal control with self-tuning capability. Finally, the algorithm of this paper is verified by the joint simulation of Matlab/Simulink and Carsim. The results show that this algorithm has a better response rate and anti-jamming capability than the traditional PID control and can achieve accurate and rapid tracking of the desired acceleration. Full article

Microwave radiometers can be used in human tissue temperature measurement scenarios due to the advantages of non-destructive and non-contact temperature measurement. However, their accuracy often cannot meet the needs of practical applications. In this paper, a Ku-Band high-precision blackbody calibration target is designed to provide calibration for microwave radiometers and meet the requirements of a high temperature-measurement accuracy and high temperature-measurement resolution. From a practical application point of view, the blackbody calibration target needs to have the characteristics of high emissivity and high temperature uniformity. However, previous studies on blackbody calibration targets often focused on the scattering characteristics or temperature uniformity of the calibration target separately, and thus lack a comprehensive consideration of the two characteristics. In this paper, the electromagnetic scattering model and the temperature-distribution model of the calibration target are established through the multi-physical simulation combined with the Finite Element Method. Then, according to the simulation results of the two characteristic models, the structural parameters and composition of the coated cone array are continuously optimized. In addition, to achieve high-precision temperature control of the blackbody calibration target, this paper studies three PID controller parameter self-tuning algorithms, namely, BP-PID, PSO-PID and Fuzzy-PID for the optimal parameter tuning problem of traditional PID algorithms and determines the optimal temperature-control algorithm by comparing the performance of heating and cooling processes. Then, the blackbody calibration target is processed and manufactured. The arch test system is used to validate the reflectance of the calibration target, the emissivity is calculated indirectly, and the temperature-distribution uniformity of the temperature-control panel of the calibration target is tested by a multi-point distribution method. Finally, the uncertainty of the brightness temperature of the blackbody calibration target is analyzed. Full article

To overcome the problem of poor uniformity of solid-fertilizer-dissolving devices due to lag of fertilizer dissolution, a closed-loop control system based on fuzzy proportional-integral-derivative (PID) was designed and tested. A fertilizer concentration regulation model was then established according to the results. In this system, the control core was an STM32 used to feed back the fertilization concentration by detecting the electrical conductivity. For real-time adjustment of the fertilizer flow rate and water flow rate, a fuzzy PID control algorithm was utilized to compare the detected concentrations with the set concentrations. The linear relationships between quantities such as the fertilizer rate and PWM frequency, water flow rate and PWM duty ratio of the direct-current pump, and fertilizer concentration and electrical conductivity were all established to calibrate the system. The influence of the fertilizer flow rate and water flow rate on fertilizer concentration was determined by the control variable test method. The results showed a positive linear relationship between fertilizer concentration and fertilizer flow rate, while a reverse linear relationship was established between fertilizer concentration and water flow rate. After the introduction of the control system into the self-developed solid-fertilizer-dissolving device, the fertilizer concentration fluctuated near the set concentration in a range of no more than 1 g/L. After the disturbance of the fertilization device, the control system fine-tuned the device with a steady-state error of about 0.55 g/L after the system reached stability. The control system designed in this study was shown to run normally with good stability, speed, and accuracy, and with improved fertilization uniformity of the solid-fertilizer-dissolving device. This study lays the foundation for further study of fertilization control systems. It also provides a reference for the development of precise and intelligent fertigation. Full article

The path tracking control system is a crucial component for autonomous vehicles; it is challenging to realize accurate tracking control when approaching a wide range of uncertain situations and dynamic environments, particularly when such control must perform as well as, or better than, human drivers. While many methods provide state-of-the-art tracking performance, they tend to emphasize constant PID control parameters, calibrated by human experience, to improve tracking accuracy. A detailed analysis shows that PID controllers inefficiently reduce the lateral error under various conditions, such as complex trajectories and variable speed. In addition, intelligent driving vehicles are highly non-linear objects, and high-fidelity models are unavailable in most autonomous systems. As for the model-based controller (MPC or LQR), the complex modeling process may increase the computational burden. With that in mind, a self-optimizing, path tracking controller structure, based on reinforcement learning, is proposed. For the lateral control of the vehicle, a steering method based on the fusion of the reinforcement learning and traditional PID controllers is designed to adapt to various tracking scenarios. According to the pre-defined path geometry and the real-time status of the vehicle, the interactive learning mechanism, based on an RL framework (actor–critic—a symmetric network structure), can realize the online optimization of PID control parameters in order to better deal with the tracking error under complex trajectories and dynamic changes of vehicle model parameters. The adaptive performance of velocity changes was also considered in the tracking process. The proposed controlling approach was tested in different path tracking scenarios, both the driving simulator platforms and on-site vehicle experiments have verified the effects of our proposed self-optimizing controller. The results show that the approach can adaptively change the weights of PID to maintain a tracking error (simulation: within ±0.071 m; realistic vehicle: within ±0.272 m) and steering wheel vibration standard deviations (simulation: within ±0.04°; realistic vehicle: within ±80.69°); additionally, it can adapt to high-speed simulation scenarios (the maximum speed is above 100 km/h and the average speed through curves is 63–76 km/h). Full article

Backlash is a commonly non-linear phenomenon, which can directly degrade the control accuracy of a pneumatic control valve. To explain the cause and law of backlash error, and to propose an effective method, many research works on the modeling of a pneumatic control valve system have been carried out. The currently model of a control valve system can be classified as a physical model, data-driven model, and semi-physical model. However, most models only consider the force-displacement conversion process of a pneumatic diagram actuator in a pneumatic control valve system. A physical model based on the whole workflow of the pneumatic control valve system is established and a control method to eliminate the backlash error is proposed in this paper. Firstly, the physical model of the pneumatic control valve system is established, which is composed of three parts: pneumatic diaphragm actuator model, nozzle-flapper structure model and electromagnetic model. After that, the input–output relationship of the pneumatic control valve system can be calculated according to the established physical model, and the calculation results are consistent with the experimental result. Lastly, a self-calibration PID (SC-PID) control method is proposed for backlash error elimination. The proposed method can solve valve stem oscillation caused by backlash during valve control. Full article