A fuzzy PID-controlled SMA actuator for a two-DOF joint

Abstract

Shape memory alloy (SMA) actuator is a potential advanced component for servosystems of aerospace vehicles and aircraft. This paper presents a joint with two degrees of freedom (DOF) and a mobility range close to ±60°when driven by SMA triple wires. The fuzzy proportional-integral-derivative (PID)-controlled actuator drive was designed using antagonistic SMA triple wires, and the resistance feedback signal made a closed loop. Experiments showed that, with the driving responding frequency increasing, the overstress became harder to be avoided at the position under the maximum friction force. Furthermore, the hysteresis gap between the heating and cooling paths of the strain-to-resistance curve expanded under this condition. A fuzzy logic control was considered as a solution, and the curves of the wires were then modeled by fitting polynomials so that the measured resistance was used directly to determine the control signal. Accurate control was demonstrated through the step response, and the experimental results showed that under the fuzzy PID-control program, the mean absolute error (MAE) of the rotation angle was about 3.147°. In addition, the investigation of the external interference to the system proved the controllable maximum output.

Smart Tendon Actuated Flexible Actuator

“…The fuzzy proportional-integral-derivative-(PID-) controlled actuator drive was designed using antagonistic SMA triple wires. This research showed the investigation of the external interference to the system proved the controllable maximum output [16].…”

“…Shape memory alloy (SMA) actuator is a potential advanced component for servo systems of aerospace vehicles and aircraft. SMA triple wires perform two degrees of freedom (DOFs) connected with a joint where the mobility range close to ±60 ∘ in the research [16]. The fuzzy proportional-integral-derivative-(PID-) controlled actuator drive was designed using antagonistic SMA triple wires.…”

A torque estimator using online tuning grey fuzzy PID for applications to torque-sensorless control of DC motors

“…But the fuzzy is a nonlinear control and the controlled system output has a static error [11]. Then fuzzy PID control which combines the traditional PID and fuzzy control algorithms is a solution [11][12][13][14][15][16][17][18]. However, the typical fuzzy PID controllers are experimentally designed based on working conditions of the control systems and their dynamic responses [19].…”

“…There is no systematic method to design and examine the number of rules, input space partitions, and membership functions (MFs). Hence, the typical fuzzy PID controllers cannot adapt for a wide range of working environments with large variation of perturbations [18]. As a result, other control techniques such as robust control, intelligent theory, or estimation methods are needed to combine with the fuzzy PID to overcome this weakness [14][15][16][17][18][19][20][21].…”

Fuzzy Feedforward and Feedback Control Method Based On Long Time Delay Systems

“…values, can satisfy the performance index of control system [10]. But the contradiction between dynamic and static performance of these fixed parameters as well as the contradiction between the setpoint and disturbance rejection can not be taken into account the system controller.…”