The Feedback Error Learning (FEL) for Functional Electrical Stimulation (FES) controller was examined through computer simulation using a musculoskeletal model. Feedforward controller of the FEL controller consisted of the inverse dynamics model (IDM) and the inverse statics model (ISM) in order to achieve successful learning. The new controller showed better performance in learning of the feed...

In this paper, the type-2 fuzzy logic system (T2FLS) controller using the feedback error learning (FEL) strategy has been proposed for load frequency control (LFC) in the restructure power system. The original FEL strategy consists of an intelligent feedforward controller (INFC) (i.e. artificial neural network (ANN)) and the conventional feedback controller (CFC). The CFC acting as a general fe...

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main ...

The Feedback Error Learning (FEL) was found to be applicable to joint angle control by FES in our previous study. However, the inverse dynamics model (IDM) in the FEL-FES controller was not learned appropriately in some cases. In this paper, 4 methods of applying the FEL to FES control were compared in controlling 1-DOF movement of the wrist joint stimulating 2 muscles through computer simulati...

Functional electrical stimulation (FES) has been studied and clinically applied to restoring or assisting motor functions lost due to spinal cord injury or cerebrovascular disease. Electrical stimulation without control of functional movements is also used for therapy or in rehabilitation training. In recent years, one of the main focuses of FES studies has been its application for rehabilitati...

In this study, a new theoretical foundation for the Discrete-Time Feedback Error Learning (DTFEL) method is proposed. This method is analogous to the original Continuous-Time Feedback Error Learning (FEL) originally proposed as a control model of the cerebellum in the field of computational neuroscience. DTFEL is superior to FEL since it is applicable for digital controllers which are commonly ...

Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES) is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hyb...

In this study, a new adaptive controller is proposed for position control of pneumatic systems. Difficulties associated with the mathematical model of the system in addition to the instability caused by Pulse Width Modulation (PWM) in the learning-based controllers using gradient descent, motivate the development of a new approach for PWM pneumatics. In this study, two modified Feedback Error L...