Dynamics Modelling and Control of a Novel Fuel Metering Valve Actuated by Two Binary-Coded Digital Valve Arrays

A fuel metering valve actuated by two binary-coded digital valve arrays (BDVAs) is proposed to improve the reliability of conventional fuel metering valves piloted by a servo valve. The design concept of this configuration is obtained from the structural characteristics of the dual nozzle-flapper and the flow regulation method of the digital hydraulic technology. The structure and working principle of the fuel metering valve are presented. Then, a mathematical model of the entire valve is developed for dynamic analysis. Subsequently, the mechanism of the transient flow uncertainty of the BDVA is revealed through simulation to determine the fluctuation in the velocity of the fuel metering valve. Furthermore, step response indicates that the delay time of the fuel metering valve is within 4.1 ms. Finally, to improve the position tracking accuracy of the fuel metering valve, a velocity feedforward proportional-integral controller with pulse code modulation is proposed. A series of comparative analyses indicate that compared with those of the velocity feedforward controller, the average and standard deviation of the position error for the proposed controller are reduced by 78 and 72.7%, respectively. The results prove the feasibility of the proposed valve and the effectiveness of the proposed control strategy.

Comparative Analysis of Advanced Controllers for Standalone WECs for DC Microgrid Applications

The small-scale wind energy generation system is one of the solutions to empower the isolated loads and provides a promising solution to decrease the greenhouse effect. This chapter describes the simulation analysis for wind energy conversion system incorporated with maximum power point tracking feature. The MPPT algorithms like variable current perturb and observe algorithm and variable step perturb and observe algorithm are incorporated with WECS. The comparative analysis is done in the closed-loop model in continuous time-varying wind speed. The closed-loop simulation is performed using a conventional fixed gain controller. To address the limitations of the fixed gain controller, the analysis is done using the gain scheduling proportional integral controller and the good gain method to tune the proportional integral controller. The comparative analysis between the fixed gain controller, the gain scheduling proportional integral controller, and the good gain method to tune proportional integral controller for above-stated MPPT methods is shown.

Kiểm nghiệm khả năng kết hợp giữa điều khiển PI và trượt thích nghi trên thiết bị GUNT-RT020

Mặc dù được sử dụng rộng rãi trong công nghiệp, nhưng với tham số cố định, bộ điều khiển tích phân tỷ lệ PI (proportional integral controller) khó thích ứng với sự thay đổi của điều kiện thực tế. Trong khi đó, điều khiển trượt (sliding mode control – SMC) cho đáp ứng ổn định trên các đối tượng phi tuyến, nhưng lại tồn tại một số hạn chế. Bài báo này đề xuất giải pháp kết hợp giữa điều khiển PI và SMC thích nghi dựa trên mạng neuron hàm cơ sở xuyên tâm RBF (radial basis function neural network), gọi tắt là điều khiển PI-SMC. Nguyên tắc kết hợp này là tận dụng ưu điểm thích nghi, bền vững của bộ SMC để khắc phục hạn chế của bộ điều khiển PI, đồng thời sử dụng bộ PI mang năng lượng chủ đạo để đẩy bộ SMC nhanh chóng hội tụ về mặt trượt. Bộ điều khiển PI-SMC được kiểm nghiệm trên thiết bị ổn định lưu lượng RT020 của hãng Gunt-Hamburg. Kết quả cũng cho giá trị khởi tạo của bộ RBF và hệ số mặt trượt ảnh hưởng lớn đến chất lượng điều khiển. Thực nghiệm cũng cho thấy cơ chế trượt thích nghi có thể khắc phục được hạn chế cố định tham số của bộ PI. Với giá trị khởi tạo của bộ tham số được chọn, bộ điều khiển PI-SMC đã cải thiện tốt đáp ứng lưu lượng trên hệ RT020 với độ vọt lố nhỏ hơn 5 (%), thời gian xác lập nhỏ hơn 2 (giây) và sai số xác lập nhỏ hơn 0,3 (lít/giờ).

Back Electro Motive Force Estimation Method for Cascade Proportional Integral Control in Permanent Magnet Synchronous Motors

A cascade proportional integral control method with back-electro motive force compensation has been widely used for permanent magnet synchronous motors. In the permanent magnet synchronous motor control, it is important to accurately know the back-electro motive force constant for torque generation as well as back-electro motive force compensation. In this study, a real-time back-electro motive force constant estimation algorithm is developed to improve the velocity tracking control performance. The proposed method consists of a proportional integral controller and a back-electro motive force constant estimator. The proportional integral controller is designed to reduce the velocity tracking error. The back-electro motive force constant estimator is designed to estimate the back-electro motive force constant. It was verified that the estimated back-electro motive force constant converges to the actual back-electro motive force constant. The estimated back-electro motive force constant is applied to the cascade proportional integral controller. To verify the effectiveness of the proposed method, the performance of the proposed method is validated experimentally.

Intelligent Search of Values for a Controller Using the Artificial Bee Colony Algorithm to Control the Velocity of Displacement of a Robot

The optimization is essential in the engineering area and, in conjunction with use of meta-heuristics, has had a great impact in recent years; this is because of its great precision in search of optimal parameters for the solution of problems. In this work, the use of the Artificial Bee Colony Algorithm (ABC) is presented to optimize the values for the variables of a proportional integral controller (PI) to observe the behavior of the controller with the optimized Ti and Kp values. It is proposed using a robot built using the MINDSTORMS version EV3 kit. The objective of this work is to demonstrate the improvement and efficiency of the controllers in conjunction with optimization meta-heuristics. In the results section, we observe that the results improve considerably compared to traditional methods. In this work, the main contribution is the implementation of an optimization algorithm (ABC) applied to a controller (PI), and the results are tested to control the movement of a robot. There are many papers where the kit is used in various domains such as education as well as research for science and technology tasks and some real-world problems by engineering scholars, showing the acceptable result.