Doan Ngoc Chi Nam

Adaptive Backstepping Control of an Electrohydraulic Actuator

This paper presents an adaptive position control for a pump- controlled electrohydraulic actuator (EHA) based on an adaptive backstepping control scheme. The core feature of this paper is the combination of a modified backstepping algorithm with a special adaptation law to compensate all nonlinearities and uncertainties in EHA system. First of all, the mathematical model of the EHA is developed. The position control is then formulated using a modified backstepping technique and the uncertainties in hydraulic system are adapted by employing a special Lyapunov function. The control signal consists of an adaptive control signal to compensate the uncertainties and a simple robust structure to ensure the robustness corresponding to a bounded disturbance. Experimental results proved strongly the ability of the proposed control method.

Hysteresis modeling and identification of a dielectric electro-active polymer actuator using an APSO-based nonlinear Preisach NARX fuzzy model

Dielectric electro-active polymer (DEAP) materials are attractive since they are low cost, lightweight and have a large deformation capability. They have no operating noise, very low electric power consumption and higher performance and efficiency than competing technologies. However, DEAP materials generally have strong hysteresis as well as uncertain and nonlinear characteristics. These disadvantages can limit the efficiency in the use of DEAP materials. To address these limitations, this research will present the combination of the Preisach model and the dynamic nonlinear autoregressive exogenous (NARX) fuzzy model-based adaptive particle swarm optimization (APSO) identification algorithm for modeling and identification of the nonlinear behavior of one typical type of DEAP actuator. Firstly, open loop input signals are applied to obtain nonlinear features and to investigate the responses of the DEAP actuator system. Then, a Preisach model can be combined with a dynamic NARX fuzzy structure to estimate the tip displacement of a DEAP actuator. To optimize all unknown parameters of the designed combination, an identification scheme based on a least squares method and an APSO algorithm is carried out. Finally, experimental validation research is carefully completed, and the effectiveness of the proposed model is evaluated by employing various input signals.

Identification of a nonlinear black-box model for a self-sensing polymer metal composite actuator

An ion polymer metal composite (IPMC) is an electro-active polymer that bends in response to a small applied electrical field as a result of the mobility of cations in the polymer network and vice versa. The aim of this paper is the identification of a novel accurate nonlinear black-box model (NBBM) for IPMC actuators with self-sensing behavior based on a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM). Firstly, an IPMC actuator is investigated. Driving voltage signals are applied to the IPMC in order to identify the IPMC characteristics. Secondly, the advanced NBBM for the IPMC is built with suitable inputs and output to estimate the IPMC tip displacement. Finally, the model parameters are optimized by the collected input/output training data. Modeling results show that the proposed self-sensing methodology based on the optimized NBBM model can well describe the bending behavior of the IPMC actuator corresponding to its applied power without using any measuring sensor.

Modeling and control of a self-sensing polymer metal composite actuator

An ion polymer metal composite (IPMC) is an electro-active polymer (EAP) that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network and vice versa. One drawback in the use of an IPMC is the sensing problem for such a small size actuator. The aim of this paper is to develop a physical model for a self-sensing IPMC actuator and to verify its applicability for practical position control.Firstly, ion dynamics inside a polymer membrane is investigated with an asymmetric solution in the presence of distributed surface resistance. Based on this analysis, a modified equivalent circuit and a simple configuration to realize the self-sensing IPMC actuator are proposed. Mathematical modelling and experimental evaluation indicate that the bending curvature can be obtained accurately using several feedback voltage signals along with the IPMC length. Finally, the controllability of the developed self-sensing IPMC actuator is investigated using a robust position control. Experimental results prove that the self-sensing characteristics can be applied in engineering control problems to provide a more convenient sensing method for IPMC actuating systems.

Analysis and experiment on a self-sensing ionic polymer?metal composite actuator

An ionic polymer?metal composite (IPMC) actuator is an electro-active polymer (EAP) that bends in response to a small applied electrical field as a result of the mobility of cations in the polymer network. This paper aims to develop a self-sensing actuator for practical use, since current sensing methods generally face limitations due to the compact size and mobility of the IPMC actuator. Firstly, the variation of surface resistance during bending operations is investigated. Then, the behavior of IPMC corresponding to the variation of surface resistance is mathematically analyzed. Based on the analysis results, a simple configuration to realize the self-sensing behavior is introduced. In this technique, the bending curvature of an IPMC can be obtained accurately by employing several feedback voltage signals along with the IPMC length. Finally, experimental evaluations proved the ability of the proposed scheme to estimate the bending behavior of IPMC actuators.

Ionic Polymer Metal Composite Transducer and Self-Sensing Ability

During the recent decade, ionic polymer metal composite (IPMC), as a smart material, is gaining great importance in the use for both sensors and actuators. An ion polymer metal composite (IPMC) is an Electro-Active Polymer (EAP) that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network [1] and vice versa. A typical IPMC sheet is constructed with a thin (200 μm) ionic polymer membrane like Nafion or Flemion and two metalic electrode layers (10 μm thick) outside such as platinum or gold. When a low voltage electrical field (1 – 5V) is applied, the transport of hydrated cations within the solvated IPMC and the associated electrostatic interactions lead to bending motions of the IPMC sheet. Thus, an IPMC can work as a small size actuator. Figure 1 illustrates the mechanism of the IPMC as actuator. On the other hand, when an IPMC is mechanically bent, it will generate a low voltage (order of milivolts) between the two electrodes. The generated voltage is due to the non-uniform concentration of ions in the IPMC membrane. Figure 2 illustrates the behavior of the IPMC as sensor.

A study on an ion polymer metal composite actuator as a self-sensing system

An ion polymer metal composite (IPMC) is an Electro-Active Polymer (EAP) that bends in response to a small electrical field as a result of mobility of cations in the polymer network and vice versa. A typical IPMC sheet is constructed with a thin ionic polymer membrane and two metal electrode layers outside. This paper proposes an idea to estimate the displacement of bending curvature of an IPMC actuator base on its self-sensing characteristic. A test rig is setup for an IPMC actuator type. Here, voltage signals are measured at two determined points on one side of the IPMC sheet in order to gather the deforming information. Consequently, that data set is combined with a representative vector to estimate the IPMC bending displacement. In addition, a CCD laser displacement sensor is installed to the test rig to measure accurately the displacement which is used to obtain the representative vector and also to verify the estimated bending shape by applied the suggested self-sensing technique.

Estimation of bending behavior of an ionic polymer metal composite actuator using a nonlinear black-box model

An ion polymer metal composite (IPMC) is an electro-active polymer that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network and vice versa. This paper presents a novel accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC. The NBBM is based on a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM). The model parameters are optimized by using training data. A comparison of the estimated and real IPMC bending characteristic has been done to investigate the modeling ability of the designed NBBM.

Precision control for ionic polymer metal composite actuator based on quantitative feedback theory

An ion polymer metal composite (IPMC) is an Electro-Active Polymer (EAP) that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network and vice versa. Recently, IPMC is widely applied in many fields such as biometric, biomedical and micro manipulator fields. This paper proposes a robust position controller for IPMCs which is based on the quantitative feedback theory (QFT). Firstly, the IPMC actuation was investigated. The PRBS input voltage signals were applied to the IPMC in order to identify the system characteristic. Consequently, the QFT controller for the IPMC was designed from the identified IPMC model. Experiments were carried out to validate the effectiveness of proposed controller applied to the IPMC.