Abstract
Recently, bio-inspired artificial muscles based on ionic polymers have shown a bright perspective in engineering and medical research, but the inherent tremor behavior can cause instability of output response. In this paper, dynamic additional grey catastrophe prediction (DAGCP) is proposed to forecast the occurrence time of tremor behavior, providing adequate preparation time for the suppression of the chitosan-based artificial muscles. DAGCP constructs various dimensions of time subsequence models under different starting points based on the threshold of tremor occurrence times and peak-to-peak values in unit time. Next, the appropriate subsequence is selected according to grey correlation degree and prediction accuracy, then it is updated with the newly generated values to achieve a real-time forecast of forthcoming tremor time. Compared with conventional grey catastrophe prediction (GCP), the proposed method has the following advantages: (1) the degradation of prediction accuracy caused by the immobilization of original parameters is prevented; (2) the dynamic input, real-time update and gradual forecast of time sequence are incorporated into the model. The experiment results show that the novel DAGCP can predict forthcoming tremor time earlier and more accurately than the conventional GCP. The generation mechanism of tremor behavior is illustrated as well.
Highlights
Artificial muscle, as one of the most promising smart materials, has attracted great attention for its potential applications in intelligent robots, artificial organs and biomedical devices in recent decades [1,2,3,4,5]
Chitosan powder was added to 2% dilute acid solution, and the mixture was stirred for 30 min at 60 ◦ C water bath, multi-walled carbon nanotubes (MCNTs) aqueous dispersion was gradually added into the above mixture for another
The data tested comes from the China’s hydropower production from 2000 to 2015, and prediction results are given by Wang et al [25] by seasonal autoregressive integrated moving average (SARIMA) method and GM (1, 1) are provided for a direct comparison
Summary
Artificial muscle, as one of the most promising smart materials, has attracted great attention for its potential applications in intelligent robots, artificial organs and biomedical devices in recent decades [1,2,3,4,5]. Due to the impressive characteristics of light weight, high flexibility, super agility and long durability [6,7,8,9,10], plentiful research results have been reported regarding the properties of artificial muscle. Li et al [11] proposed a novel cantilever beam artificial muscle using single-walled carbon nanotubes, which showed superfast response and ultrahigh mechanical output power density. Lu et al [12] put forward an artificial muscle based on multi-walled carbon nanotubes, ionic liquids and biopolymer chitosan, performing an excellent bio-compatibility. Jager et al [13] studied the electrically-controllable characteristics of conjugated polymers artificial muscles and applied them to the drug injection therapy. Kim and Kwon [15] developed a hybrid muscle powered
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