Janan Zaytoon

Bio: Janan Zaytoon is an academic researcher from University of Reims Champagne-Ardenne. The author has contributed to research in topics: Control theory & Hybrid system. The author has an hindex of 18, co-authored 97 publications receiving 1103 citations. Previous affiliations of Janan Zaytoon include Reims University & Northeastern University.

State observer based robust adaptive fuzzy controller for nonlinear uncertain and perturbed systems

Abstract: A robust adaptive fuzzy controller, based on a state observer, for a nonlinear uncertain and perturbed system is presented. The state observer is introduced to resolve the problem of the unavailability of the state variables. Two control signals are added to a basic state feedback control law, deduced from a nominal model, to guarantee the tracking performance in the presence of structural uncertainties and external disturbances. The first control signal is computed from an adaptive fuzzy system and eliminates the effect of structural uncertainties and estimation errors. Updating the adjustable parameters is ensured by a PID law to obtain a fast convergence. Robustness of the closed-loop system is guaranteed by an H/spl infin/ supervisor computed from a Riccati type equation. Simulation example is presented to show the efficiency of the proposed method.

Adaptive Control

Abstract: This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Review of control strategies for robotic movement training after neurologic injury

Abstract: There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.

A survey on robotic devices for upper limb rehabilitation

Abstract: The existing shortage of therapists and caregivers assisting physically disabled individuals at home is expected to increase and become serious problem in the near future The patient population needing physical rehabilitation of the upper extremity is also constantly increasing Robotic devices have the potential to address this problem as noted by the results of recent research studies However, the availability of these devices in clinical settings is limited, leaving plenty of room for improvement The purpose of this paper is to document a review of robotic devices for upper limb rehabilitation including those in developing phase in order to provide a comprehensive reference about existing solutions and facilitate the development of new and improved devices In particular the following issues are discussed: application field, target group, type of assistance, mechanical design, control strategy and clinical evaluation This paper also includes a comprehensive, tabulated comparison of technical solutions implemented in various systems

A survey on robotic devices for upper limb

Abstract: The existing shortage of therapists and caregivers assisting physically disabled individuals at home is expected to increase and become serious problem in the near future. The patient population needing physical rehabilitation of the upper extremity is also constantly increasing. Robotic devices have the potential to address this problem as noted by the results of recent research studies. However, the availability of these devices in clinical settings is limited, leaving plenty of room for improvement. The purpose of this paper is to document a review of robotic devices for upper limb rehabilitation including those in developing phase in order to provide a comprehensive reference about existing solutions and facilitate the development of new and improved devices. In particular the following issues are discussed: application field, target group, type of assistance, mechanical design, control strategy and clinical evaluation. This paper also includes a comprehensive, tabulated comparison of technical solutions implemented in various systems.

Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer

Abstract: In this paper, an adaptive neural output feedback control scheme is proposed for uncertain nonlinear systems that are subject to unknown hysteresis, external disturbances, and unmeasured states. To deal with the unknown nonlinear function term in the uncertain nonlinear system, the approximation capability of the radial basis function neural network (RBFNN) is employed. Using the approximation output of the RBFNN, the state observer and the nonlinear disturbance observer (NDO) are developed to estimate unmeasured states and unknown compounded disturbances, respectively. Based on the RBFNN, the developed NDO, and the state observer, the adaptive neural output feedback control is proposed for uncertain nonlinear systems using the backstepping technique. The first-order sliding-mode differentiator is employed to avoid the tedious analytic computation and the problem of “explosion of complexity” in the conventional backstepping method. The stability of the whole closed-loop system is rigorously proved via the Lyapunov analysis method, and the satisfactory tracking performance is guaranteed under the integrated effect of unknown hysteresis, unmeasured states, and unknown external disturbances. Simulation results of an example are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain nonlinear systems.