This study investigates the problem of nano-scale precision trajectory tracking control with extended state observer (ESO) for piezoelectric linear motors (PELMs). Since PELMs need different driven signals for various...
This study investigates the problem of nano-scale precision trajectory tracking control with extended state observer (ESO) for piezoelectric linear motors (PELMs). Since PELMs need different driven signals for various operating requirements, they are subject to different degrees of system uncertainties and external disturbances. By analyzing the input–output characteristics of the PELM, the system model for the PELM is constructed. Due to the high uncertainties of the PELM, an ESO is introduced for the estimation of disturbances. Specifically, an enhanced error-dependent observer switching mechanism (EOSM) is established based on the disturbance features. Then, to suppress the adverse effects of disturbances, an EOSM based super-twisting sliding mode control (EOSM-STSMC) scheme is developed. The proposed control strategy can implement disturbance estimation and uncertainty inhibition, thus the complex nonlinearities of the PELM can be delicately treated and high accuracy tracking effect can be achieved. The system stability analysis is provided with the aid of the Lyapnuov function. Finally, a plenty of experiments are implemented on the PELM for tracking point-to-point trajectory, sinusoidal signal, and third-order S-curve. The proposed controller has smaller performance index values compared to the traditional SMC method, linear ESO based SMC method, and nonlinear ESO based SMC method. Experimental results indicate that the proposed controller has satisfactory transient and steady state tracking performance.
The proposed method aims to address the randomness and volatility of photovoltaic power generation by forecasting short-term photovoltaic power combinations using feature extraction. The method incorporates the firefl...
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A molecular imprinted polymer-based optical sensor is proposed to detect methanol. The sensor is tested with different concentrations of methanal vapour for sensitivity and ethanol for selectivity. The result shows a ...
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This article investigates an adaptive bipartite consensus tracking control algorithm for a class of heterogeneous nonaffine nonlinear multiagent systems (MASs) with prescribed finite-time tracking performance under an...
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It is urgent to establish an efficient and comprehensive security system architecture facing the increasingly severe network threat. Aiming at the security problems in information systems, a deep security detection fr...
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Aiming at the problem of high failure rate of electric vehicle charging pile, an electric vehicle charging pile failure prediction method based on cooperative game strategy and dung beetle optimisation algorithm-bidir...
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The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous contr...
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The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.
This study proposes a consensus control strategy for heterogeneous multi-agent systems characterized by uncertain parameters and external disturbances, to address the intricate robust control challenges posed by time-...
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This paper presents a speech recognition method combining spectral subtraction with improved BP neural network algorithm. The speech samples containing unstable noise are collected in the real environment. Firstly, th...
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This study conducts a comprehensive analysis of optimizing the speed profile of an AE-bus, a critical step in enhancing its operational efficiency. It encompasses three distinct case studies, each targeting specific o...
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