Feedforward-Feedback Hybrid Control for Magnetic Shape Memory Alloy Actuators Based on the Krasnosel'skii-Pokrovskii Model

Zhou, Miaolei; Zhang, Qi; Wang, Jingyuan

doi:10.1371/journal.pone.0097086

Cited by 15 publications

(23 citation statements)

References 27 publications

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“…( B ) Hysteresis curves of inverse radial basis function neural network (RBFNN) model. ( C ) Modeling error of inverse Krasnosel'skii-Pokrovskii (KP) model (11). ( D ) Modeling error of inverse RBFNN model.…”

Section: Simulationsmentioning

confidence: 99%

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Zhou

Wang

et al. 2017

Journal of Applied Biomaterials & Functional Materials

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Gain control and hysteresis compensation phase shifter were used to improve the proportion integration differentiation (PID) feed-back control in the experiment. Results showed that control precision, settling time and overshoot were improved and the control accuracy was 25 nm. Ruderman and Bertram (17) proposed the system-oriented dynamic model for MSMA actuators, and combined the dynamic model of second-order linear actuators with Preisach hysteresis nonlinearity model. The discrete model parameters were identified by using experimental data and effectiveness of this dynamic model was validated. Adaptive inverse hysteresis control method based on observer was implemented to improve the robustness of system (17). The effectiveness of the control method was proved by using experiment. Mao Chiang et al predigested the control rules by using sliding mode controller and fuzzy sliding surfaces. Experimental results showed that this method was more effective and the control precision was 0.25 nm (18,19).With the advantages of adaptive learning, associative memory, strong robustness and fault tolerance, radial basis function neural network (RBFNN) has the capacity to identify any nonlinear functions. The hidden layers' output is used to obtain a set of basic functions. The linear approach is achieved by linear combination of output layers of RBFNN. In this paper, the RBFNN hysteresis model of the MSMA actuators is proposed. First, RBFNN is used as activation function to establish an inverse model. Then, a feed-forward controller is proposed

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Section: Simulationsmentioning

confidence: 99%

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Zhou

Wang

et al. 2017

Journal of Applied Biomaterials & Functional Materials

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“…While it is possible that a similar method could be used to actuate bistable mechanisms, such as the use of shape-memory materials [42, 43] or the heating of the bistable flexures to trigger them into their second position, we also investigated more rapid actuation methods. Because a small input displacement can actuate a bistable mechanism from its second stable position to its fabricated position, this is one of the simplest methods of activation.…”

Section: Proposed Applicationsmentioning

confidence: 99%

Bistable Mechanisms for Space Applications

et al. 2016

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Compliant bistable mechanisms are monolithic devices with two stable equilibrium positions separated by an unstable equilibrium position. They show promise in space applications as nonexplosive release mechanisms in deployment systems, thereby eliminating friction and improving the reliability and precision of those mechanical devices. This paper presents both analytical and numerical models that are used to predict bistable behavior and can be used to create bistable mechanisms in materials not previously feasible for compliant mechanisms. Materials compatible with space applications are evaluated for use as bistable mechanisms and prototypes are fabricated in three different materials. Pin-puller and cutter release mechanisms are proposed as potential space applications.

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“…However, position control of SMA actuators represents a major challenge for practical applications due to their nonlinear hysteretic behavior, producing steady-state errors when conventional controllers are used [18]. To overcome this problem, proportional-integral-derivate (PID) controllers with hysteresis models have been implemented [18,19,20]; for position control of SMA actuators using the generalized Prandtl–Ishlinskii inverse model [18], for micro-positioning control of SMA actuators by modeling the hysteresis using NNs [19], and for magnetic SMA actuators by using a radial basis function NN to obtain the Jacobian information of the system in order to adjust the controller parameters [20]. In addition, neural network controllers, previously trained to identify the system were proposed to control SMA actuators [21,22,23,24]; using the inverse of the ANN that replicate the dynamics of the SMA force actuator to implement the controller [21], implementing a model predictive controller based on a functional link ANN to control the linear memory metal actuator displacement [22], and realizing a recurrent neural model predictive, variable structure, controller designed to control a one degree of freedom (1-DOF) rotary manipulator actuated by an SMA wire [23,24].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators

Gómez-Espinosa

Sundin

Eguren

et al. 2019

Sensors

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New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83° was achieved when validated against several set-points within the possible range.

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Feedforward-Feedback Hybrid Control for Magnetic Shape Memory Alloy Actuators Based on the Krasnosel'skii-Pokrovskii Model

Cited by 15 publications

References 27 publications

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Feed-Forward Control for Magnetic Shape Memory Alloy Actuators Based on the Radial Basis Function Neural Network Model

Bistable Mechanisms for Space Applications

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators

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