a b s t r a c tIn this paper, a new approach is proposed for stability issues of neutral-type neural networks (DNNs) with constant delay. First, the semi-free weighting matrices are proposed and used instead of the known free weighting matrices to express the relationship between the terms in the Leibniz-Newton formula to simplify the system synthesis and to obtain less computation demand. Second, global exponential stability conditions which are less conservative and restrictive than the known results are derived. At the same time, based on the above approach, fewer variable matrices are introduced in the construction of the Lyapunov functional and augmented Lyapunov functional. Two examples are given to show their effectiveness and advantages over others.
Shape memory alloy (SMA) actuators exhibit severe hysteresis, a nonlinear behavior, which complicates control strategies and limits their applications. This paper presents a new approach to controlling an SMA actuator through an adaptive inverse model based controller that consists of a dynamic neural network (DNN) identifier, a copy dynamic neural network (CDNN) feedforward term and a proportional (P) feedback action. Unlike fixed hysteresis models used in most inverse controllers, the proposed one uses a DNN to identify online the relationship between the applied voltage to the actuator and the displacement (the inverse model). Even without a priori knowledge of the SMA hysteresis and without pre-training, the proposed controller can precisely control the SMA wire actuator in various tracking tasks by identifying online the inverse model of the SMA actuator. Experiments were conducted, and experimental results demonstrated real-time modeling capabilities of DNN and the performance of the adaptive inverse controller.
Shape-memory alloys (SMAs) have received considerable amount of attentions for their engineering applications in recent years. The hysteresis in SMAs is sensitive to the state-varying tendency and frequency. Utilizing past information to estimate the hysteretic behavior gets increasing attention. In this paper, a timedelayed dynamic neural network (TDDNN) is proposed for modeling hysteresis of SMAs in online applications. By introducing a time delay between the input and output response, the TDDNN considers the time delay's effect on the hysteresis. This proposed network was applied to a SMA wire actuator. Experimental results demonstrate the effectiveness of TDDNN. The identified results obtained by TDDNN are better than those obtained by dynamic neural network without considering the delay information. It demonstrates the importance of introducing the time delay. The different values of time delay item can also affect TDDNN's identified results.
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