Fractional multi-loop active disturbance rejection control for a lower knee exoskeleton system

Rehabilitation Exoskeleton is becoming more and more important in physiotherapists’ routine work. To improve the treatment performance, such as reducing the recovery period and/or monitoring and reacting to unpredictable situations, the rehabilitation manipulators need to help the patients in various physical trainings. A special case of the active disturbance rejection control (ADRC) is applied to govern a proper realisation of basic limb rehabilitation trainings. The experimental study is performed on a model of a flexible joint manipulator, whose behaviour resembles a real exoskeleton rehabilitation device (a one-degree-of-freedom, rigid-link, flexible-joint manipulator). The fractional (FADRC) is an unconventional model-independent approach, acknowledged as an effective controller in the existence of total plant uncertainties, and these uncertainties are inclusive of the total disturbances and unknown dynamics of the plant. In this work, three FADRC schemes are used, the first one using a fractional state observer (FSO), or FADRC1, second one using a fractional proportional-derivative controller (FPD), or FADRC2, and the third one a Multi-loop fractional in PD-loop controller and the observer-loop (Feedforward and Feedback), or FADRC3. The simulated Exoskeleton system is subjected to a noise disturbance and the FADRC3 shows the effectiveness to compensate all these effects and satisfies the desired position when compared with FADRC1 and FADRC2. The design and simulation were carried out in MATLAB/Simulink

Skin cancer classifier based on convolution residual neural network

Accurate automatic classification of skin lesion images is a great challenge as the image features are very close in these images. Convolution neural networks (CNN) promise to provide a potential classifier for skin lesions. This work will present dermatologist-level classification of skin cancer by using residual network (ResNet-50) as a deep learning convolutional neural network (DLCNN) that maps images to class labels. It presents a classifier with a single CNN to automatically recognize benign and malignant skin images. The network inputs are only disease labels and image pixels. About 320 clinical images of the different diseases have been used to train CNN. The model performance has been tested with untrained images from the two labels. This model identifies the most common skin cancers and can be updated with a new unlimited number of images. The DLCNN trained by the ResNet-50 model showed good classification of the benign and malignant skin categories. The ResNet-50 as a DLCNN has verified a significant recognition rate of more than 97% on the testing images, which proves that the benign and malignant lesion skin images are properly classified

Backstepping-Based Quasi-Sliding Mode Control and Observation for Electric Vehicle Systems: A Solution to Unmatched Load and Road Perturbations

The direct current (DC) motor is the core part of an electrical vehicle (EV). The unmatched perturbation of load torque is a challenging problem in the control of an EV system driven by a DC motor and hence a deep control concern is required. In this study, the proposed solution is to present two control approaches based on a backstepping control algorithm for speed trajectory tracking of EVs. The first control design is to develop the backstepping controller based on a quasi-sliding mode disturbance observer (BS-QSMDO), and the other controller is to combine the backstepping control with quasi-integral sliding mode control (BS-QISMC). In the sense of Lyapunov-based stability analysis, the ultimate boundedness of the proposed controllers has been detailedly analyzed, assessed, and evaluated in the presence of unmatched perturbation. A modified stability analysis has been presented to determine the ultimate bounds of disturbance estimation error for both controllers. The determination of ultimate bound and region-of-attraction for tracking and estimation errors is the contribution achieved by the proposed control design. The performances of the proposed controllers have been verified via computer simulations and the level of ultimate bounds for the estimation and tracking errors are the key measures for their evaluation. Compared to BS-QISMC, the results showed that a lower level of ultimate boundedness with a higher convergent rate can be reached based on BS-QSMO. However, a higher control effort can be exerted by the BS-QSMO controller as compared to BS-QISMC; and this is the price to be paid by the BS-QSMO controller to achieve lower ultimate boundedness with a faster convergence rate

PMLSM position control based on continuous projection adaptive sliding mode controller

In this paper, the design of projection-based Adaptive Sliding Mode Controller (ASMC) is presented for position control of Permanent Magnet Linear Synchronous Motor (PMLSM) with unknown mover mass. The PMLSM model was first established and a vector control based on field orientation is used to decouple the cross-coupling in motor model. ASMC has been adopted to deal with the unknown mover mass and to give robust operation against external load thrust. Based on the Lyapunov method, the stability of adaptive sliding mode-controlled PMLSM has been proven and the adaptive law has been developed. Additionally, a continuous projection operator is applied to adaptive law such as to enforce the estimated mover mass within a pre-specified bound. The performance of ASMC based on continuous projection operator is investigated via simulation results within MATLAB environment. Also, a comparison study in ASMC performance is made due to the inclusion continuous and discontinuous projection operators. The simulated results showed that ASMC based on continuous projection gives better performance than that based on discontinuous one

Design of Integral Sliding Mode Controller for Servo DC Motor

DC servo motor is simple in construction and control and has many applications. However, the uncertainties due to its parameters changes such as load torque and friction are an evitable. Therefore, a robust controller has to be employed for keeping specified requirements irrespective to parameter variations. In the present work, two sliding mode controllers have been suggested to control the speed of DC motor under motor load changes; classical and integral sliding mode controllers. The integral slide mode control could show better tracking characteristics than its counterpart and also could compensate the change in system parameters