Control of Brushless Direct-Current Motors Using Bioelectric EMG Signals.

(1) Background: The purpose of this study was to evaluate the analysis of measurements of bioelectric signals obtained from electromyographic sensors. A system that controls the speed and direction of rotation of a brushless DC motor (BLDC) was developed; (2) Methods: The system was designed and constructed for the acquisition and processing of differential muscle signals. Basic information for the development of the EMG signal processing system was also provided. A controller system implementing the algorithm necessary to control the speed and direction of rotation of the drive rotor was proposed; (3) Results: Using two muscle groups (biceps brachii and triceps), it was possible to control the direction and speed of rotation of the drive unit. The control system changed the rotational speed of the brushless motor with a delay of about 0.5 s in relation to the registered EMG signal amplitude change; (4) Conclusions: The prepared system meets all the design assumptions. In addition, it is scalable and allows users to adjust the signal level. Our designed system can be implemented for rehabilitation, and in exoskeletons or prostheses.

Driving behavior analysis and classification by vehicle OBD data using machine learning.

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.

Sliding mode control with system constraints for aircraft engines.

This paper proposes a constraint-tolerant design with sliding mode strategy to improve the stability of aircraft engine control. To handle the difficulties associated with the high-frequency switching laws, merely attenuating the chattering is far from satisfactory. System constraints on input, output, and input rate should be addressed in the design process. For a sort of uncertain nonlinear systems subjected to the constraints, sliding mode regulators are designed using Lyapunov analysis. A turbofan engine is adopted for simulation, which shows that the methodology developed in this paper can handle the speed tracking and limit protection problem in a stable fashion, despite the negative influence posed by the system constraints.

Experimental comparisons of hypothesis test and moving average based combustion phase controllers.

For engine control, combustion phase is the most effective and direct parameter to improve fuel efficiency. In this paper, the statistical control strategy based on hypothesis test criterion is discussed. Taking location of peak pressure (LPP) as combustion phase indicator, the statistical model of LPP is first proposed, and then the controller design method is discussed on the basis of both Z and T tests. For comparison, moving average based control strategy is also presented and implemented in this study. The experiments on a spark ignition gasoline engine at various operating conditions show that the hypothesis test based controller is able to regulate LPP close to set point while maintaining the rapid transient response, and the variance of LPP is also well constrained.

Experimental study for the reproduction of sudden unintended acceleration incidents.

A few cases of the sudden unintended acceleration have been reported over the last few years [1-11] and some of them seemed to be somewhat related to an electronic throttle control (ETC) system [11,12]. In this experimental study, efforts were made to reproduce the cases of sudden unintended acceleration possibly related to the ETC. Typically, an ETC of the engine is managed based on signals from airflow sensor, throttle position sensor and acceleration pedal sensor. With this typical sensor configuration in mind, these sensor signals were checked for noise levels. However, none of them showed any clear relationship with the sudden unintended acceleration mainly due to the robustness of the ETC logic software. As an alternative approach, supply voltage to an engine control unit (ECU) was tempered intentionally to observe any clues for the incidents. The observed results with the supply voltage drop and fluctuation tests were rather astonishing. The throttle valve position went all the way up to 100% for around one second when the battery voltage plunged down to 7V periodically despite that the acceleration pedal position was kept steady. As an effort to confirm the case, multiple tries were made systematically on a chassis dynamometer as well as on the test road. In this paper, detailed procedures and findings are reported accordingly.