With the progress of miniaturization, shape memory alloy (SMA) actuators exhibit high energy density, self-sensing ability and ease of fabrication, which make them well suited for practical applications. This paper presents a self-sensing controlled actuator drive that was designed using antagonistic pairs of SMA wires. Under a certain pre-strain and duty cycle, the stress between two wires becomes constant. Meanwhile, the strain to resistance curve can minimize the hysteresis gap between the heating and the cooling paths. The curves of both wires are then modeled by fitting polynomials such that the measured resistance can be used directly to determine the difference between the testing values and the target strain. The hysteresis model of strains to duty cycle difference has been used as compensation. Accurate control is demonstrated through step response and sinusoidal tracking. The experimental results show that, under a combination control program, the root-mean-square error can be reduced to 1.093%. The limited bandwidth of the frequency is estimated to be 0.15 Hz. Two sets of instruments with three degrees of freedom are illustrated to show how this type actuator could be potentially implemented.

Considering nonlinearity, time-variation and inertia during temperature control of large supercritical extraction units, especially under the disturbance of system flow and pressure, a multi-artificial neural network (ANN) predictive control policy was proposed. It contains a radial basis function (RBF) ANN, aiming to approach nonlinear extraction temperature object and predicting output variable based on this model. There is also a back propagation (BP) ANN controller, seeking the optimal controlling signal by feedback correction and rolling optimization on purpose to overcome the time-variation and inertia. The experimental results indicate that this control strategy has excellent dynamic response performance, small steady state error and strong robustness.