A force-sensing resistor is a conductive polymer that exhibits a decrease in resistance as the force applied at its surface increases. The aim of this study is to identify the characteristics of the force-sensing resistor for use in a refreshable and portable E-Braille device that can assist the blind and visually impaired persons. The force-sensing resistor is placed within a component dynamic testing device that is composed of a linear actuator that can generate different displacement loading profiles and a load cell that measures the applied forces. The system records the voltage, force, and the displacement profiles. Several strategies are used in the identification process. First, the mechanical properties of the force-sensing resistor are experimentally characterized. A second-order mechanical system whose parameters are function of the exciting frequency is created based on the results of this experiment. The performance of this model is evaluated using several test inputs. In an attempt to better identify the force-sensing resistor, alternative higher order linear and nonlinear models, including Hammerstein, Wiener, and Hammerstein–Wiener, are proposed using system identification techniques. The accuracy and robustness of these models are assessed using various loading profiles. The outputs of these models are compared with the experimental results.

Robot motion planning between two given configurations in the time domain has a great impact on robotic applications. In the presence of link flexibility, the problem becomes more difficult and critical to be solved. A two-link flexible manipulator is proposed in this study, where the manipulator's joints are required to undergo a rest-to-rest maneuvering. Two trajectories are assumed, a fourth order polynomial and soft motion trajectories1 the Genetic Algorithm is employed to optimize the unknown parameters of the fourth order trajectory in such away that minimizes the energy consumption during motion. The mathematical model of the manipulator is obtained using the extended Hamilton's principle, where the flexible links are treated using Euler-Bernoulli's beam theory. Simulation study for the optimized joints' torques for the two trajectories is introduced and a comparison between them is carried out based on the minimum energy consumption.

This work presents a dynamic model of a smart fin that is activated by a piezoelectric bimorph actuator, which is made by bonding two MFCs. The actuator is completely enclosed within the fin. Earlier research has indicated that the use of a linear model for the fin dynamics does not fully describe the fin. This work presents a more realistic approach to this problem by incorporating additional components into the model. Therefore, a proportional damping matrix is introduced. It is also observed that experimental results exhibit hysteresis and backlash. A Bouc-Wen hysteresis model, combined with four backlash operators, is proposed. These backlash operators are used to model the observed saturation and the non-symmetry of the response. HFSGA is used to identify the optimal set of parameters for the damping matrix constants, the Bouc-Wen model, and the backlash operators. One input case is considered for training the genetic algorithm. The results show that proposed model can predict the hysteresis of the smart finactuator system under various operational conditions.

There has been an increasing demand for force measurement in applications that are portable, wearable, have limited space, low power, or low cost requirements. Such applications cannot support the power and computing requirements of the load cells. Force sensing resistor (FSR) is a passive component that is composed of polymer thick films that change resistance between its terminals due to force applied at its surface. An FSR can provide a suitable alternative to the bulky load cell. However, it inherently exhibits many nonlinear behaviors. This paper evaluates five different FSR sensors (IEE FSR 151 NS, Interlink FSR 402, Sensitronics ShuntMode FSR. FlexiForce A201 FSR, and Sensitorincs ThruMode FSR). The FSRs were evaluated for sensitivity, minimum measurable force, repeatability, creep, and hysteresis. In addition, system identification techniques were used to model each FSR behavior so they can be used as calibrated standalone force sensors. Results show that the Sensitronics ThruMode FSR had the best repeatability and the least hysteresis, while the FlexiForce A201 had the lowest creep, and the Interlinks 402 FSR had the lowest threshold force.

This work presents an approach for identifying the model of a composite piezoelectric (PZT) bimorph actuator dynamics, with the objective of creating a robust model that can be used under various operating conditions. This actuator exhibits nonlinear behavior that can be described using backlash and hysteresis. A linear dynamic model with a damping matrix that incorporates the Bouc-Wen hysteresis model and the backlash operators is developed. This work proposes identifying the actuator's model parameters using the hybrid master-slave genetic algorithm neural network (HGANN). In this algorithm, the neural network exploits the ability of the genetic algorithm to search globally to optimize its structure, weights, biases and transfer functions to perform time series analysis efficiently. A total of nine datasets (cases) representing three different voltage amplitudes excited at three different frequencies are used to train and validate the model. Four cases are considered for training the NN architecture, connection weights, bias weights and learning rules. The remaining five cases are used to validate the model, which produced results that closely match the experimental ones. The analysis shows that damping parameters are inversely proportional to the excitation frequency. This indicates that the suggested hysteresis model is too general for the PZT model in this work. It also suggests that backlash appears only when dynamic forces become dominant.