A knowledge-based particle swarm optimization (PSO) algorithm is used to achieve more optimized control of Ball and Beam System (BBS) adaptively. It adopts an improved nonlinear inertia weight, an adaptive strategy an...
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ISBN:
(纸本)9781665404693
A knowledge-based particle swarm optimization (PSO) algorithm is used to achieve more optimized control of Ball and Beam System (BBS) adaptively. It adopts an improved nonlinear inertia weight, an adaptive strategy and a fitness function combining prior knowledge and one traditional performance criterion. Comparing four classic performance criteria, the simulation results indicate that Integral of Time multiply Absolute Error (ITAE) is better, and it is combined with prior knowledge. based on the response curve of advanced correction, Ziegler-Nichols, basic PSO algorithm and knowledge-based PSO algorithm through experimental simulation, knowledge-based PSO algorithm is more effective to BBS.
Stochastic resonance is of great importance in the field of signal detection. Suitable system parameters determine the performance of a parameter-induced stochastic resonance detection system. Considering the difficul...
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ISBN:
(纸本)9781467376792
Stochastic resonance is of great importance in the field of signal detection. Suitable system parameters determine the performance of a parameter-induced stochastic resonance detection system. Considering the difficulty of adjusting system parameters and the requirement of real-time detection in the parameter-induced stochastic resonance, knowledge-based particle swarm optimization (KPSO) is proposed to optimize system parameters, which takes the kurtosis index as the fitness function and the property that the impact signal can produce stochastic resonance in a single potential well as the knowledge. Compared with particleswarmoptimization (PSO), this algorithm can obtain optimal system parameters more quickly, making energy transfer from the noise to the signal greatly, and produce the best output resonance effect. As a typical large-parameter signal, the impact signal is not satisfied with the stochastic resonance condition apparently. In this paper, we combine the twice sampling with KPSO, realizing weak impact signal detection, and verifying the efficiency and effectiveness of the algorithm.
In this paper,the weak signal detection under a stable noise is investigated based on bistable vibrational resonance(VR) which is driven by a high frequency *** the one hand,the energy of the high frequency drive sign...
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ISBN:
(纸本)9781509009107
In this paper,the weak signal detection under a stable noise is investigated based on bistable vibrational resonance(VR) which is driven by a high frequency *** the one hand,the energy of the high frequency drive signal is transferred to the low frequency weak signal when VR occurs;on the other hand,the control of stochastic resonance(SR) is achieved based on VR,which transfers more noise energy into useful signal *** addition,considering the requirements of real-time detection,the amplitude and frequency of the high frequency drive signal are optimized by the knowledge-based particle swarm optimization(KPSO),which takes the mean signal-noise-ratio(MSNR) of output as the fitness function,and the property that VR system produces the best resonance effect just when the valid system parameter a(B,Ω) is greater than zero as ***,the parameter compensation is combined to achieve multi-high frequency weak signals detection with a stable ***,the method is applied to the vibration fault diagnosis of a mono-crystalline silicon furnace,and the experiment results show the effectiveness and practicability of the method.
Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is ...
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Trajectory planning and obstacle avoidance play essential roles in the cooperative flight of multiple unmanned aerial vehicles (UAVs). In this paper, a unified framework for onboard distributed trajectory planning is proposed, which takes full advantage of intelligent discrete and continuous search algorithms. Firstly, the Monte Carlo tree search (MCTS) is used as the task allocation algorithm to solve the cooperative obstacle avoidance problem. Taking the task allocation decisions as the constraint, knowledge-based particle swarm optimization (Know-PSO) is used as the optimization algorithm to solve the onboard distributed cooperative trajectory planning problem. Simulation results demonstrate that the proposed intelligent MCTS-PSO search framework is effective and flexible for multiple UAVs to conduct the cooperative trajectory planning and obstacle avoidance. Further, it has been applied in practical experiments and achieved promising results.
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