Fuzzy Logic-Based Intelligent Control System for Active Ankle Foot Orthosis

Fuzzy Logic-Based Intelligent Control System for Active Ankle Foot Orthosis

M. Kanthi (Manipal University, India)
Copyright: © 2017 |Pages: 34
DOI: 10.4018/978-1-5225-1908-9.ch050
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The Ankle Foot Orthosis (AFO) is an orthotic device intended to assist or to restore the movements of the ankle foot complex in the case of pathological gait. Active AFO consists of sensor, controller, and actuator. The controller used in the conventional AFO to control the actuator does not use the property of synchronization of the feet. This chapter deals with development of a fuzzy-based intelligent control unit for an AFO using property of symmetry in the foot movements. The control system developed in LabVIEW provides real-time control of the defective foot by continuously monitoring the gait patterns. The input signals for the control system are generated by the sensor system having gyroscope. DC motor is used as an actuator. The data acquisition for Gait Analysis is done using National Instrument's data acquisition system DAQ6221 interfaced with a gyro-sensor.
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Fuzzy logic controller (FLC) is a set of linguistic control rules related by the dual concepts of fuzzy implication and the compositional rule of inference. In essence, then the the FLC provides an algorithm which can convert the linguistic control strategy based on expert knowledge in to an automatic control strategy. Experience shows that FLC yields results superior to those obtained by conventional control algorithms (Chuen Lee, 1990). Conventional control techniques such as Proportional Integral and Differential (PID) control, nonlinear feedback control, adaptive control, sliding mode control, Linear quadratic Gaussian control etc. are advantageous when the values of the controller parameters are known and the control signals are generated exactly. Also, when the underlying assumptions are satisfied, many of these methods provide good stability, robustness to model uncertainties and disturbances, and speed of response. However these control algorithms are “hard” or “inflexible” and cannot handle “soft” intelligent control which may involve reasoning and inference making using incomplete, vague, noncrisp, and qualitative information, and learning and self- organization through past experience and knowledge. The fuzzy control gives the optimal performance to control the DC motor and also overcome the disadvantages of the conventional control sensitiveness to inertia variation and sensitiveness to variation of speed with drive system of DC motor (Thepsatoml P, 2006). Jamal Abdaltayef & ZHU Qunxiong (2010) have implemented a DC motor position control to show that FLC responds with less overshoot and minimum settling time over conventional PID control. Fuzzy logic systems emulate human decision making more closely than artificial neural network. The main advantages are that no mathematical modeling is required as in PID since the controller rules are based on the knowledge of system behavior and experience of control engineer (Chuen Lee, 1990; Clarence W.de Silva, 1995; Pedro Ponce-Cruz & Fernando D. Ramirez-Figueroa, 2010).

Fuzzy control is a type of intelligent control whose main feature is that a control knowledge base is available within the controller and control actions are generated by applying existing conditions or data to the knowledge base, making use of an inference mechanism. The knowledge base and inference mechanism can handle non crisp and incomplete information, and the knowledge itself will improve and evolve through “learning” and past experience (Clarence W.de Silva, 1995; Pedro Ponce-Cruz & Fernando D. Ramirez-Figueroa, 2010). In particular, the methodology of the FLC appears very useful when the processes are too complex for analysis by conventional quantitative techniques or available sources of information are interpreted qualitatively, inexactly, or uncertainly. Thus fuzzy logic control may be viewed as a step toward a rapprochement between conventional precise mathematical control and human like decision making.

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