In this paper, the problem of finite-time stabilization and control of an n-degree of freedom (DOF) robotic manipulator is concerned. Factors such as model nonlinearity, uncertainties, and external disturbances can interfere in a closed-loop system performance. In order to attenuate these effects and improve the response characteristics of the system, a proper controller is developed. This paper is the pioneering one on integrating adaptive backstepping control approach into fractional-order controller design for an integer-order dynamic system. An analytic proof is provided based on the fractional Lyapunov stability theorem to guarantee global asymptotic stability of the controlled system. Ensuring finite-time convergence of the system regardless of initial states values is another important aspect of this study. Furthermore, model uncertainties and external disturbances are taken into account and the controller is developed to attenuate these effects. The developed fractional-order adaptive backstepping controller (FOABC) is experimentally implemented on a manipulator and extensive experiments are carried out. Moreover, an exact comparison between FOABC, integer-order adaptive backstepping controller (ABC), and two of the recently released adaptive control approaches is drawn which gives an evidence of superiority of the controller.

Attention deficit hyperactivity disorder is a neurodevelopmental condition associated with varying levels of hyperactivity, inattention, and impulsivity. This study investigates brain function in children with attention deficit hyperactivity disorder

One of the major problems associated with the motor imagery (MI) electroencephalogram (EEG) based brain-computer interface (BCI) classifications is the informative ambiguities mainly caused by interferences of artifacts and nonstationarities in EEG signals. Other factors containing mislabeling or misleading MI EEG trials might also cause more uncertainties in training datasets that lead to decline in classification performance. This paper proposes a new framework to achieve more efficient classification in multiclass MI EEG-based BCIs. An artifact rejected common spatial pattern (AR-CSP) method is proposed for feature extraction in order to cope with the interferences of artifacts. A self-regulated adaptive resonance theory based neuro-fuzzy classifier that is referred to as “self-regulated supervised Gaussian fuzzy adaptive system Art (SRSG-FasArt)” is introduced to deal with EEG nonstationarities. A metacognitive self-regulatory-based learning algorithm is also proposed to more efficiently deal with the uncertainties. The algorithm captures the training data samples by priority and automatically creates, upgrades, or prunes the fuzzy rules by scanning the knowledge content existing in the data patterns and the created rules. The mechanism improves the generalization capability of the SRSG-FasArt and prevents over-training. The performance of the proposed cooperative framework of AR-CSP and SRSG-FasArt is evaluated using the BCI competition IV dataset 2a. The results indicate more accurate and efficient BCI classification compared to the existing frameworks.