Shape memory alloys are among the highly applicable smart materials that have recently appealed to scientists from various fields of study. In this article, a novel shape memory alloy actuator, in the form of a rod, i...
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Shape memory alloys are among the highly applicable smart materials that have recently appealed to scientists from various fields of study. In this article, a novel shape memory alloy actuator, in the form of a rod, is introduced, and an adaptive model predictive control system is designed for position control of the developed actuator. The need for such an advanced control system emanates from the fact that modeling and controlling of shape memory alloy actuators are thwarted by their hysteresis nonlinearity, dilatory response, and high dependence on environmental conditions. Real-time identification and dynamic parameter estimation of the model are addressed according to orthogonal Laguerre functions and recursiveleast square algorithm. In the end, the designed control system is implemented on the experimental setup of the fabricated shape memory alloy actuator. It is observed that the designed control system successfully tracks the variable step and sinusoidal control references with startling accuracy of +/- 1 mu m.
Hammerstein system identification is difficult because there exist the product items of the parameters between the non-linear block and the linear block. This study presents a novel parameter separation based recursiv...
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Hammerstein system identification is difficult because there exist the product items of the parameters between the non-linear block and the linear block. This study presents a novel parameter separation based recursiveleastsquares (PS-RLS) identificationalgorithm for resolving this problem. Its basic idea is to use a linear filter to filter the output data and the noise, and then to obtain two new identification submodels in each of which the output is linear in the corresponding parameter vector. Compared with the over-parametrisation based recursiveleastsquares method, the proposed algorithm can avoid estimating the redundant parameters and has a higher computational efficiency. The simulation results show that the proposed PS-RLS algorithm can generate highly accurate parameter estimates with less computational effort.
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