Control of Shape Memory Alloy Actuators with a Neuro-fuzzy Feedforward Model Element

Kumagai, Akihiko; Liu, Tien-I.; Hozian, Paul

doi:10.1007/s10845-005-5512-2

Cited by 49 publications

(19 citation statements)

References 19 publications

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“…The most typical configuration is a combination of feedforward and feedback controllers [13][14][15][16][17]. The main idea of the feedforward controllers consists of using an inverse model of the system to generate the appropriate control input to obtain the desired response.…”

Section: Related Workmentioning

confidence: 99%

“…The neural network is trained to learn the inverse hysteresis behavior and, after that, a PI with anti-windup control loop is used. Kumagi et al [14] have proposed a controller with a feedforward part that uses a neuro-fuzzy inference system and a PD controller. Song et al [15] have designed a neural network feedforward controller for open loop tracking control of a SMA wire actuator without a position sensor.…”

Section: Related Workmentioning

confidence: 99%

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Position control of a shape memory alloy actuator using a four-term bilinear PID controller

Villoslada

Escudero

Alonso-Martín

et al. 2015

Sensors and Actuators A: Physical

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This is a postprint version of the following published document: Villoslada, A.; Escudero, N.; Martín, F.; Flores, A.; Rivera, C.; Collado, M.; Moreno, L. (2015). Position control of a shape memory alloy actuator using a four-term bilinear PID controller. Sensors and Actuators AbstractShape memory alloy (SMA) actuators have a number of appealing features, such as their low weight or their high force-to-weight ratio, that make them a potential alternative to traditional actuation tech-nologies in fields such as space applications, surgical devices or wearable robotics. In this paper, a type of bilinear controller consisting of a conventional PID controller cascaded with a bilinear compensator, known as BPID, is proposed. Bilinear controllers are a subset of nonlinear controllers, which is why the BPID may be a promising alternative to control the position of a SMA actuator. Nonlinear control tech-niques are commonly applied to control SMA actuators, because of their nonlinear behavior caused by thermal hysteresis. The BPID controller is simpler and easier to implement than other nonlinear control strategies, which makes it a very appealing candidate to control SMA actuators. The performance of the BPID controller has been compared with other two controllers, a conventional PID and a commuted feed-forward PIPD, controlling a real SMA actuator. To this end, a set of five tests has been defined, in which the controlled actuator must follow a series of position references. From these tests, the position and error of the actuator have been plotted, and a series of metrics has been computed to have quantitative mea-surements of the performance of the three controllers. It is shown that, in most of the experiments, the BPID has a better performance than the other two tested controllers, especially tracking step references. However, the power consumption is slightly higher when the actuator is controlled with this strategy, although the difference is minimal. Also, the BPID imposes greater energy variations to the SMA actuator, which might affect its service life. Overall, the BPID controller has proved to be a viable alternative to control SMA actuators.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Position control of a shape memory alloy actuator using a four-term bilinear PID controller

Villoslada

Escudero

Alonso-Martín

et al. 2015

Sensors and Actuators A: Physical

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show abstract

“…Generally, residuals are functions of the difference between real and estimated state outputs. As a matter of fact, for two tank sensors fault detection and isolation, the following residuals are built given by, e e e 1 (kT ) = x x x 1 (kT ) −x x x 1 (kT ) (17) e e e 2 (kT ) = x x x 2 (kT ) −x x x 2 (kT )…”

Section: Extended Kalman Filter (Ekf)mentioning

confidence: 99%

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

Mani¹

2014

Archives of Control Sciences

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Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system PANDIYAN MANIKANDAN, MANI GEETHAThe inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

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“…However, this control system cannot control the position of actuator in minor loops, especially those cycled close to the extremes. In [37] a motion controller consisting of a feedforward part to calculate the open-loop input voltage to the SMA amplifier using the neuro-fuzzy inference system and a feedback part which uses an ordinary PD controller to compensate for the open-loop inaccuracy, is proposed by Kumagi et al It is shown that using the PD controller only, a significant delay of the raise motion of the actual trajectory compared to that of the desired trajectory is reported. Song et al [38] designed a neural network feedforward controller for open loop tracking control of an SMA wire actuator without a position sensor.…”

Section: Literature Review On Sma Control Systemsmentioning

confidence: 99%

Position control of shape memory alloy actuator based on the generalized Prandtl–Ishlinskii inverse model

Salarieh

Zakerzadeh

2012

Mechatronics

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Control of Shape Memory Alloy Actuators with a Neuro-fuzzy Feedforward Model Element

Cited by 49 publications

References 19 publications

Position control of a shape memory alloy actuator using a four-term bilinear PID controller

Position control of a shape memory alloy actuator using a four-term bilinear PID controller

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

Position control of shape memory alloy actuator based on the generalized Prandtl–Ishlinskii inverse model

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