## 1 Introduction  
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. [PERSON] 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. [PERSON] et al. [12] put forward an artificial muscle based on multi-walled carbon nanotubes, ionic liquids and biopolymer chitosan, performing an excellent bio-compatibility. [PERSON] et al. [13] studied the electrically-controllable characteristics of conjugated polymers artificial muscles and applied them to the drug injection therapy. Then, [PERSON] et al. [14] designed a magnetically sensitive actuator with a ball screw and rare-earth magnet coreless, which was compact enough to be used in practical artificial prosthesis, and the limitations of high-price and lower-power in conventional electrochemical polymer actuations were overcome. Recently, [PERSON] and [PERSON] [15] developed a hybrid muscle powered by skeletal muscle cells based on multi-walled carbon nanotubes. It provides potential for substantial innovation in the next generation of cell-based biohybrid microsystems. Although the above research results look appealing, with the bionics research moving forward, researchers start to realize the rhythmic and involuntary contraction characterized by oscillations of the muscle which implies the instability of the muscle's output response. Such a drawback definitely limits the applications of artificial muscle in clinical medicine and industrial production. Obviously, capturing the tendency of tremor behavior can provide adequate time for the suppression of the tremor, which is essential to the improvement of the artificial muscle's output performance. Normally, the tremor behavior of artificial muscle can be characterized by the tremor occurrence times and maximum peak-to-peak values which can be predicted by applying grey systems theory. The grey systems theory, established by [PERSON] [16] in 1982, focuses on uncertainty systems with small samples, which has been widely used in education, industry, agriculture, medicine and other fields [17; 18; 19; 20; 21; 22; 23]. Grey prediction, an important part of grey systems theory, can provide scientific and reasonable forecasts on the future states of grey systems. For the requirement of solving practical problems, many improvements have been proposed by researchers to obtain accurate prediction results. According to their features and functions, grey predictions can be classified into grey sequence prediction, grey topological prediction, grey catastrophe prediction and seasonal system prediction, etc. [24; 25; 26; 27]. Among them, grey catastrophe prediction (GCP) is essentially the prediction for the time distribution of abnormal values and it can forecast the forthcoming catastrophe moments to help relevant persons to prepare for the worst conditions in advance [28; 29; 30; 31]. In this paper, based on the generation mechanism of tremor behavior, a novel DAGCP method is proposed to forecast the tremor occurrence time of artificial muscles. Due to the importance of occurrence times and maximum peak-to-peak values in unit time for muscle tremor behavior, the time subsequence models under different starting points and dimensions are constructed. Then, the appropriate dimension is selected based on grey correlation degree and model precision, and updated with the newly generated values to forecast the forthcoming tremor time. The prediction results are compared with actual time series values for evaluating relative error and accuracy.