Precise Motion Control of a Power Line Inspection Robot Using Hybrid Time Delay and State Feedback Control.

Intelligent robotic inspection of power transmission lines has proved to be an excellent alternative to the traditional manual inspection methods, which are often tedious, expensive, and dangerous. However, to achieve effective automation of the robots under different working environments, the dynamic analysis and control of the robots need to be investigated for an efficient inspection process. Nonetheless, the application of control techniques for the position, speed and vibration control of these robots has not been explored in detail by the existing literature. Thus, an approach for precise motion control of the sliding inspection robot is presented in this paper. The main contribution of the study is that the chattering problem associated with the traditional command shaping time delay control (TDC) was minimized by smoothing the chattered input signal. Then, the improved control (iTDC) which is effective for oscillation control is hybridized with a pole placement based feedback control (PPC) to achieve both position and the sway angle control of the robot. The nonlinear and the linearized models of the sliding robot were established for the control design and analysis. Three parameters of the robot, namely, the length of the suspended arm, the mass of the payload, and the friction coefficient of different surfaces, were used to assess the robustness of the controller to model uncertainties. The iTDC + PPC has improved the velocity of TDC by 201% and minimizes the angular oscillation of PPC by 209%. Thus, the results demonstrate that the hybridized iTDC + PPC approach could be effectively applied for precise motion control of the sliding inspection robot.

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm.

Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain-computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.

Novel approach for electromyography-controlled prostheses based on facial action.

Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications. Graphical abstract Individuals with severe tetraplegia frequently require to control their complex assistive devices using body movement with the remaining activity above the neck. Electromyography (EMG) signals from the contractions of facial muscles enable people to produce multiple command signals by conveying information about attempted movements. In this study, a novel EMG-controlled system based on facial actions was developed. The mechanism of different facial actions was processed using an EMG control model. Four asymmetric and symmetry actions were defined to control a two-degree-of-freedom (2-DOF) prosthesis. Both indoor and outdoor experiments were conducted to validate the feasibility of EMG-controlled prostheses based on facial action. The experimental results indicated that the new paradigm presented in this paper yields high performance and efficient control for prosthesis applications.