Advanced Control for Applications最新文献

The paper considers the application of feedback control to orbital transfer maneuvers subject to constraints on the spacecraft thrust and on avoiding the collision with the primary body. Incremental reference governor (IRG) strategies are developed to complement the nominal Lyapunov controller, derived based on Gauss variational equations, and enforce the constraints. Simulation results are reported that demonstrate the successful constrained orbital transfer maneuvers with the proposed approach. A Lyapunov function based IRG and a prediction-based IRG are compared. While both implementation successfully enforce the constraints, a prediction-based IRG is shown to result in faster maneuvers.

Nowadays, controlling industrial processes and choosing the proper method for this purpose is very important. DC–DC converters are widely used in industrial applications, such as DC–DC step-up converters of 150 and 250 V in power drives and power supplies of portable electrical and hybrid vehicles. A predictive control algorithm is a method to deal with such complex processes. Many methods have been proposed as predictor control, leading to better and more accurate evolution with problems. This study proposes a fuzzy explicit predictive control method for adjusting the boost DC–DC converter. A fuzzy method for selecting the weights of the cost function of the predictive control algorithm of the DC–DC converter will be presented. In addition, for better evaluation and analysis, the designed controller is compared with similar methods, and the simulation results show that the controller designed on this system has had a proper performance with other methods in realizing the desired goals.

This article focuses on improving the control approach for a synchronous reluctance motor (SynRM) drive powered by a two-level pulse width modulation (PWM) inverter. While classical sliding mode control (SMC) has been extensively used in control system design, it comes with various drawbacks such as pronounced chattering effects, considerable transient state errors, and reduced robustness. These limitations hinder its practical applicability. To enhance the performance of the SynRM, this paper introduces a novel strategy that combines direct vector control (DVC) with advanced sliding mode control (ASMC), here referring to third-order sliding mode command (TOSMC), for regulating speed and dq-axis stator currents. The primary objective of this approach is to achieve precise and efficient control while minimizing total harmonic distortion (THD) in current and reducing output torque fluctuations. Notably, this strategy capitalizes on the strengths of TOSMC and DVC. The efficacy of the proposed control scheme is verified through two sets of thorough simulations realized in MATLAB/Simulink environment. The first set of simulations encompasses the load–torque test, where the motor is subjected to two different levels of load torque. The results from these tests showcase the control scheme's performance under varying load conditions. The second set of simulations involves the speed variation test, where intentional changes are applied to the motor's speed. This test assesses the control approach's ability to handle dynamic speed changes effectively. The proposed control strategy is further compared with conventional control methods, including proportional–integral and second-order sliding mode command (SOSMC) controls. The results consistently demonstrate the superior performance of the novel approach in terms of accurate control, robustness, and overall stability. The combination of DVC and TOSMC offers a promising avenue for achieving enhanced motor control in the presence of load disturbances and speed variations.

This article addresses a trajectory tracking control problem concerning an autonomous underwater vehicle's pitch and yaw channel dynamics in the presence of model uncertainties, underwater disturbances, and input saturation. Three different observers are introduced to estimate unknown state variables: a Luenberger-type cubic observer, a sliding mode observer, and a high-gain observer (HGO). Initially, a backstepping controller is employed to tackle the tracking problem, extending it to incorporate backstepping sliding mode control (SMC). The mentioned observers are utilized in both aspects of the controller design. Our proposed control law assesses trajectory tracking performance by introducing virtual control inputs, with the sliding surface designed to guide the current state variables toward approximating the virtual state variables. By combining backstepping and SMC, ensure that the state variables of the closed-loop system converge to the desired state using the HGO. A rigorous analysis is incorporated to validate the robust performance of our proposed control law under conditions of model uncertainties and underwater disturbances. Furthermore, the control law is extended for anti-windup compensation, mitigating adverse effects on stern and rudder plane saturation levels. Lyapunov stability theory is adopted to establish the stability of the closed-loop system. Our simulation results convincingly demonstrate the effectiveness of the HGO-based backstepping SMC law compared to alternative control approaches.

With the process of urbanization and the increase in car ownership, traffic problems are becoming increasingly prominent. In order to improve traffic mobility and improve traffic safety, a machine learning based autonomous obstacle avoidance system was studied and designed in the context of intelligent transportation. Design an obstacle avoidance hardware system consisting of a tracking sensor module, an intelligent patrol module, an obstacle avoidance sensor module, and a motor module. Through the coordination and cooperation of multiple modules, the adaptive ability of the obstacle avoidance system is improved. On the basis of hardware design, a road coordinate system is established, and the lane-changing path is planned with the longitudinal, lateral distance and speed of the ego vehicle and the preceding vehicle as input, and the vehicle steering and lane-changing control is completed using the front wheel angle of the ego vehicle as the control quantity. The model predictive control method is used for obstacle avoidance trajectory planning. Based on the obstacle avoidance path planning results, the reinforcement learning method is used to design the vehicle's autonomous obstacle avoidance early warning to improve the efficiency of obstacle avoidance. The experimental results show that the designed system can maintain the lateral stability of the vehicle under continuous steering conditions, and the fit between the path tracking and the reference path is better, that is, the vehicle obstacle avoidance control effect is better; the convergence speed is faster. The vehicle autonomous obstacle avoidance warning time is short, which can ensure the safety of the vehicle to the greatest extent. This research achievement will provide important support for the development and practical application of intelligent transportation systems, and promote innovation and progress in the transportation field.

In this article, we are interested in the problem of adaptive synchronization of a voltage source converter with a possibly weak grid with unknown angle and frequency. To guarantee a suitable synchronization with the angle of the three-phase grid voltage we design an adaptive observer for such a signal requiring measurements only at the point of common coupling. Then we propose an alternative certainty-equivalent, adaptive phase-locked loop that ensures the angle estimation error goes to zero for almost all initial conditions. Although well-known, for the sake of completeness, we also present a PI controller with feedforward action that ensures the converter currents converge to an arbitrary desired value. Relevance of the theoretical results and their robustness to variation of the grid parameters are thoroughly discussed and validated in the challenging scenario of a converter connected to a grid with low short-circuit-ratio.

A Flocking obstacle avoidance algorithm based on the extensive game with perfect information is proposed for the blockage problem of UAV swarm in front of multi-narrow type obstacles. The two UAVs closest to the target are selected as participants of the game, and the game tree is defined to determine the combination of the motion strategies of the two UAVs to obtain the payoff matrix. Determine the subgame perfect Nash equilibrium to get the optimal strategy, and give the UAVs different motion states respectively so as to ensure that the UAVs can successfully pass multi-narrow type obstacles. Simulation results demonstrate that the proposed algorithm has a higher over-hole rate in the case of the multi-narrow type obstacle compared to the static game-based Flocking obstacle avoidance algorithm.

Search and rescue operations are all time-sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real-life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.