Current Issue : April-June Volume : 2024 Issue Number : 2 Articles : 5 Articles
Suspension systems are critical parts of modern cars. In this study, a radial basis function neural networks-based adaptive PID optimal method is presented for vehicle suspension systems. To avoid the shortcoming that the parameters of PID control are determined by experience in the traditional method, to avoid the local optimality problem and the slow rate of convergence in the modern intelligence method, radial basis function neural networks are applied in this paper. First, a quarter-car suspension is presented. Then, the radial basis function neural networks are employed to obtain the parameters of proportional, integral, and derivate components that are used in PID control. The simulation is conducted later. Next, a comparison of the progress between uncontrolled suspension, the radial basis function-based PID control, the H∞ control method, and the FPM control method is presented. According to the simulation results, the proposed control method performs better than the others. This contrast reveals the superior characteristics of the suggested control strategy....
A stepping valve system has been proposed before and proved to be feasible. The stepping valve move perpendicular to the piston, thereby avoiding piston-valve collisions associated with interference engines. In this study, the proposed Stepping Valve Control Unit, SVCU, was integrated with the conventional Engine Control Unit, ECU, of a simple SI engine. The resulting Integrated Engine Control Unit, IECU, as it is called, simplified the design of the ECU. The integration involved combining the basic fuel injector and sparking controls with the SVCU. The camshaft and crankshaft sensors were replaced by a Piston Direction Sensor, PDS. The PDS is an optical sensor, which works in conjunction with a gray coded trigger wheel to relay information to the IECU. The IECU was simulated using Quartus II design software. The hardware implementation of the IECU was done using EPM7128SL FPGA contained within the UP1 Altera Development Board. The behavior of the IECU was expected....
Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches....
Position control of servo motor systems is a challenging task because of inevitable factors such as uncertainties, nonlinearities, parametric variations, and external perturbations. In this article, to alleviate the above issues, a practical adaptive fast terminal sliding mode control (PAFTSMC) is proposed for better tracking performance of the servo motor system by using a state observer and bidirectional adaptive law. First, a smooth-tangent-hyperbolic-function-based practical fast terminal sliding mode control (PFTSM) surface is designed to ensure not only fast finite time tracking error convergence but also chattering reduction. Second, the PAFTSMC is proposed for the servo motor, in which a two-way adaptive law is designed to further suppress the chattering and overestimation problems. More importantly, the proposed adaptive technique can update the switching gain according to the system uncertainties, which can provide high gain in the reaching phase and then decrease to the smallest value in the sliding phase to avoid the monotonically increasing gain that exists in most adaptation methods. Third, the finite-time stability of the closed-loop system is proved based on the Lyapunov theorem. Finally, the simulation studies and experimental tests verify the effectiveness of the proposed control in terms of better tracking, strong robustness, and reduced chattering, compared to existing algorithms....
In order to address the issue of drill string stick–slip vibration, which leads to drill bit wear and reduces the drilling velocity, we conducted a study on the characteristics of stick–slip vibration using a proportional-integral-derivative (PID) controller. By applying the principles of rigid body mechanics, we established a two-degree-of-freedom torsional dynamics equation and derived the first-order differential dynamics equation for the drill string. Subsequently, we designed a PID controller and obtained an equation for the control of stick–slip vibration. The research findings indicate that variations in the difference between the static and dynamic friction coefficients directly impact the nature of the limit cycles in the phase plane. As this difference decreases, the limit cycle narrows and the stick–slip vibrations weaken progressively. When the static and dynamic friction coefficients are equalized, no stick–slip vibrations occur within the drill string. The implementation of PID control effectively manages stick–slip vibrations in the drill string, with greater efficiency observed in controlling the turntable velocity compared to the drill bit velocity. This research provides valuable insights for the development of control strategies aimed at mitigating stick–slip vibrations in drilling engineering applications, thereby facilitating the efficient and safe extraction of oil and gas resources....
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