Software testing is indeed an important part of the application development. In order to identify the bugs, present in the developed source code, software testing techniques are applied. The testing method considers v...
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The primary responsibility of load dispatch centers is to maximize economic efficiency while maximizing operational efficiency, which is achieved by minimizing the cost of real power generation at various generating u...
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This research introduces a PID controller designed for a Vehicle Suspension System (VSS) using the Wild Goat optimization (WGO) algorithm. The goal is to improve ride comfort by mitigating the impacts caused by uneven...
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This paper focuses on the control problem for a two-degree-of-freedom flight simulator experimental setup, proposing a reinforcement learning-based flight attitude controller. The flight simulator aims to simulate the...
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ISBN:
(纸本)9798350373707;9798350373691
This paper focuses on the control problem for a two-degree-of-freedom flight simulator experimental setup, proposing a reinforcement learning-based flight attitude controller. The flight simulator aims to simulate the aircraft attitude control system, requiring consideration of its nonlinearity, model uncertainty, and the impact of external disturbances when designing the controller. Proximal Policy optimization (PPO), as a policy gradient-based deep reinforcement learning algorithm, autonomously learns an approximately optimal controller based on a given objective function without the need for a mathematical model of the controlled object. Thanks to the application of the Actor-Critic framework and neural networks, the training of the two-degree-of-freedom flight simulator controller can rapidly converge within a short period. Simulations validate the generalization capability of the trained PPO controller and its robustness to external disturbances.
Aiming at the problems of many parameters, difficult parameter adjustment and low efficiency of active disturbance rejection controller (ADRC) in permanent magnet synchronous motor (PMSM) speed control system, this pa...
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ISBN:
(纸本)9798400718212
Aiming at the problems of many parameters, difficult parameter adjustment and low efficiency of active disturbance rejection controller (ADRC) in permanent magnet synchronous motor (PMSM) speed control system, this paper proposes an active disturbance rejection parameter tuning method based on improved dung beetle optimization algorithm (HDBO). The HDBO algorithm improves the original DBO algorithm through two main steps: (1) The cubic chaotic initialization strategy is used to generate the initial population, increase the quality of the initial population, and enhance the global search ability of the algorithm. (2) The levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimum. 23 benchmark functions are used to test the improved algorithm. The results show that the improved algorithm has better solution accuracy, convergence speed and stability than other algorithms. Finally, we also apply the HDBO algorithm to the parameter tuning of the active disturbance rejection controller. The simulation results show that the control strategy proposed in this paper has a significant improvement in computational efficiency, control accuracy, response speed and other aspects compared with the traditional controller. The system reflects good control effect.
Based on the computer automatic control technology, this paper designs the energy efficiency optimization method for the traditional water-cooled central air conditioning host system. The chilled water control based o...
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Adaptive control of aircraft is an advanced control technology aimed at ensuring that the aircraft can automatically adjust its control parameters in various complex and changing flight environments and conditions, th...
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Patrol route optimization is not only related to flight safety and efficiency, but also directly related to many aspects such as resource utilization and environmental protection. However, some unreasonable route plan...
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The stochastic optimization algorithm and the random signal nonlinear optimal filtering algorithm are typical representatives of intelligent optimization algorithms in the rapidly developing scientific research and en...
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This paper describes the design of a competent cascaded filter for image denoising using Arithmetic optimization Algorithm (AOA). Though several conventional spatial domain filters are used individually to perform den...
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