Nonlinear model predictive control-Cross-coupling control with deep neural network feedforward for multi-hydraulic system synchronization control.

This paper studies a multi-hydraulic system (MHS) synchronization control algorithm. Firstly, a general nonlinear asymmetric MHS state space entirety model is established and subsequently the model form is simplified by nonlinear feedback linearization. Secondly, an entirety model-type solution is proposed, integrating a nonlinear model predictive control (NMPC) algorithm with a cross-coupling control (CCC) algorithm. Furthermore, a novel disturbance compensator based on the system's inverse model is introduced to effectively handle disturbances, encompassing unmodeled errors and noise. The proposed innovative controller, known as nonlinear model predictive control-cross-coupling control with deep neural network feedforward (NMPC-CCC-DNNF), is designed to minimize synchronization errors and counteract the impact of disturbances. The stability of the control system is rigorously demonstrated. Finally, simulation results underscore the efficacy of the NMPC-CCC-DNNF controller, showcasing a remarkable 60.8% reduction in synchronization root mean square error (RMSE) compared to other controllers, reaching up to 91.1% in various simulations. These results affirm the superior control performance achieved by the NMPC-CCC-DNNF controller.

Development of a novel method for estimating and planning automatic skimmer operation in response to offshore oil spills.

Offshore oil production and transportation of oil by pipelines and tankers are associated with the risks of an oil spill, and accidents of various scales, from emissions of several liters to several thousand tons, occur regularly in different parts of the world. Currently, there are no automatic or automated systems for responding to such incidents, although special equipment exists that is able to collect oil from the surface of water. The oil collected by skimmers can be used for its intended purpose. The purpose of this research is to develop a novel method for estimating the number of skimmers required for automated oil recovery in the event of oil contamination in the open sea, taking into account errors in measured weather conditions and initial spill information. In this work, a program is developed to simulate the position and state of an oil slick on the water surface, based on realistic weather conditions, and the movement of a group of skimmers while performing the oil removal task. The results of the study demonstrate the robustness of the system with respect to errors in the initial data, weather condition, position and parameters of the oil spill. Two main emergencies are considered: an instantaneous release of oil from a tanker and continuous leakage from a damaged pipeline. The developed system detects and collects oil on the map in a limited time, even with a significant shift in the initial coordinates, and limits the spread of the oil slick where there is continuous leakage. In addition, the designed method has a short-term overestimation of the skimmer group size in case of time delay in the response to the spill. The developed method can be applied in real cases of oil spills to create and update the plan of movement and collection of oil for a group of skimmers.

Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study.

This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.

Development of a novel real-time simulation of human skeleton/muscles.

The objective of this research work was to develop a model of human skeleton with the capability of real-time simulation of the physical movements of a person in front of the motion capture hardware (Kinect) in order to analyze the motion data and measure the changes of joint torques. Mevea simulation software has been utilized for this purpose, which is a novel application of this software in the field of biomechanics. The model of the human skeleton was created in Mevea using the graphics built in 3ds Max. Simulink external interface for Mevea was established. Simulink acts as a connection between the Mevea software and Kinect for controlling the model. The developed model has been tested through three case studies involving the elbow joint, thoracic joint, and full body. Changes in torque and angular position of joints based on the input of joints are presented as graphs. The developed real-time model of the human skeleton in Mevea can execute the real-time simulation of a person's movements in front of a motion capture camera and provide the changes of torques, which are dependent on the angular positions of the body joints. This work provides the possibility to use the developed real-time model for physiotherapeutic rehabilitation to identify problematic muscles based on produced torque of the joints in order to specify the therapeutic options. The future research direction would be creating a reference databank by measuring healthy individuals' muscle forces for comparison purposes.

Monitoring of the human body and brain behavior using optical motion capture system and EEG utilizing horseback riding simulator: an extended case study.

[Purpose] Hippotherapy is an unusual type of treatment and has been found to be effective for diseases of the musculoskeletal system and rehabilitation. Horseback riding simulator is used as a beneficial alternative to the real horse with utilizing an optical motion capture system and EEG. [Participants and Methods] The idea is to monitor body and brain behaviour of the professional rider and non-professional rider utilizing a horse simulator, using optical motion capture system to identify differences in pelvic region activity between professional and non-professional riders and EEG to investigate the brain effect of professional rider utilizing horseback riding simulator. [Results] For the monitoring body and brain behaviour of the professional rider and non-professional rider, two types of experiment were handled, the first experiment represents body behaviour and the second experiment represents brain behaviour. [Conclusion] The study shows, that inexperienced rider may make mistakes of pelvis movements that leads to the asymmetry in hip external rotation and back region. Also, the study of EEG provides that while horseback riding mostly frontal lobe is active, that refers to concentration, body movements and intelligence. Moreover, temporal and parietal lobes are highlighted that relates to sensor-motor cortex and moving which are needed during riding.