Over the last two decades, smart materials have received significant attention over a broad range of engineering applications because of their unique and inherent characteristics for actuating and sensing aspects. In this review article, recent research works on various robots, medical devices and rehabilitation mechanisms whose main functions are activated by smart materials are introduced and discussed. Among many smart materials, electro-rheological fluids, magneto-rheological fluids, and shape memory alloys are considered since there are mostly appropriate application candidates for the robot and medical devices. Many different types of robots proposed to date, such as parallel planar robots, are investigated focusing on design configuration and operating principles. In addition, specific mechanism and operating principles of medical devices and rehabilitation systems are introduced and commented in terms of practical realization.

This paper provides a preliminary study on the hysteresis compensation of a piezoresistive silicon-based polymer composite, poly(dimethylsiloxane) dispersed with carbon nanotubes (CNTs), to demonstrate its feasibility as a conductive composite (i.e., a force-sensitive resistor) for force sensors. In this study, the potential use of the nanotube/polydimethylsiloxane (CNT/PDMS) as a force sensor is evaluated for the first time. The experimental results show that the electrical resistance of the CNT/PDMS composite changes in response to sinusoidal loading and static compressive load. The compensated output based on the Duhem hysteresis model shows a linear relationship. This simple hysteresis model can compensate for the nonlinear frequency-dependent hysteresis phenomenon when a dynamic sinusoidal force input is applied.

This article presents vibration control of a semi-active quarter-car suspension system equipped with a magneto-rheological damper that provides the physical constraint of a damping force. In this study, model predictive control was designed to handle the constraints of control input (i.e. the limited damping force). The explicit solution of model predictive control was computed using multi-parametric programming to reduce the computational time for real-time implementation and then adopted in the semi-active suspension system. The control performance of model predictive control was compared with that of a clipped linear-quadratic optimal controller, where the damping force was bound using a standard saturation function. Two types of road conditions (bump and random excitation) were applied to the suspension system, and the vibration control performance was evaluated through both simulations and experiments.

The road roughness acts as a disturbance input to the vehicle dynamics, and causes undesirable vibrations associated with the ride and handing characteristics. Furthermore, the accurate measurement of road roughness plays a key role in better understanding a vehicle dynamic behaviour and active suspension control systems. However, the direct measurement by laser profilometer or other distance sensors are not trivial due to technical and economic issues. This study proposes a new road roughness estimation method by using the discrete Kalman filter with unknown input (DKF-UI). This algorithm is built on a quarter-car model and uses the measurements of the wheel stroke (suspension deflection), and the acceleration of the sprung mass and unsprung mass. The estimation results are compared to the measurements by laser profilometer in-vehicle test.