Dynamic response of an inverted pendulum system in water under parametric excitations for energy harvesting : a conceptual approach

In this paper, we have investigated the dynamic response, vibration control technique, and upright stability of an inverted pendulum system in an underwater environment in view point of a conceptual future wave energy harvesting system. The pendulum system is subjected to a parametrically excited input (used as a water wave) at its pivot point in the vertical direction for stabilization purposes. For the first time, a mathematical model for investigating the underwater dynamic response of an inverted pendulum system has been developed, considering the effect of hydrodynamic forces (like the drag force and the buoyancy force) acting on the system. The mathematical model of the system has been derived by applying the standard Lagrange equation. To obtain the approximate solution of the system, the averaging technique has been utilized. An open loop parametric excitation technique has been applied to stabilize the pendulum system at its upright unstable equilibrium position. Both (like the lower and the upper) stability borders have been shown for the responses of both pendulum systems in vacuum and water (viscously damped). Furthermore, stability regions for both cases are clearly drawn and analyzed. The results are illustrated through numerical simulations. Numerical simulation results concluded that: (i) The application of the parametric excitation control method in this article successfully stabilizes the newly developed system model in an underwater environment, (ii) there is a significant increase in the excitation amplitude in the stability region for the system in water versus in vacuum, and (iii) the stability region for the system in vacuum is wider than that in water

Kernel recursive least square tracker and long-short term memory ensemble based battery health prognostic model

Summary: A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics

Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control

Trajectory tracking is an essential requirement in robot manipulator movement and localization applications. It is a current research topic of interest, and several researchers have proposed different schemes to achieve the task accurately. This research proposes efficient control of a hydraulic non-linear robot manipulator using a modified sliding mode control, named proportional derivative sliding mode control with sliding perturbation observer (PDSMCSPO), to overcome parameter uncertainties and non-linearity. The proposed new control strategy achieves higher accuracy and better time convergence than the previous one. A positive derivative gain, which has a value less than one, is multiplied with the velocity error term of the sliding surface. The proposed control (PDSMCSPO) also achieves robustness. Results show that by introducing the derivative gain, the chattering from the system has been reduced more than classical sliding mode control (SMC). The reason is that during reaching phase this small gain multiplies with the perturbation and minimizes the effect of perturbation on the system. A smaller value of switching gain K is required as compared to SMC, and the transfer function between sliding surface and perturbation in proportional derivative sliding mode control (PDSMC)has low pass filter characteristics. The proposed PDSMCSPO has a faster response than previous sliding mode control with sliding perturbation observer (SMCSPO), and the output and sliding surface convergence to the desired value is much quicker than conventional logic. Some other characteristics such as error in the output are small because of more attenuation of the perturbation signal. Simulation and experimental results are presented for a link between the hydraulic robot manipulator and the mass damper system

Estimated Reaction Force-Based Bilateral Control between 3DOF Master and Hydraulic Slave Manipulators for Dismantlement

This paper proposes a novel bilateral control design based on an estimated reaction force without a force sensor for a three-degree of freedom hydraulic servo system with master–slave manipulators. The proposed method is based upon sliding mode control with sliding perturbation observer (SMCSPO) using a bilateral control environment. The sliding perturbation observer (SPO) estimates the reaction force at the end effector and second link without using any sensors. The sliding mode control (SMC) is used as a bilateral controller for the robust position tracking and control of the slave device. A bilateral control strategy in a hydraulic servo system provides robust position and force tracking between master and slave. The difference between the reaction force of the slave produced by the effect of the remote environment and the operating force applied to the master by the operator is expressed in the target impedance model. The impedance model is applied to the master and allows the operator to feel the reaction force from the environment. This research experimentally verifies that the slave device can follow the trajectory of the master device using the proposed bilateral control strategy based on the estimated reaction force. This technique will be convenient for three or more degree of freedom (DOF) hydraulic servo systems used in dismantling nuclear power plants. It is worthy to mention that a camera is used for visual feedback on the safety of the environment and workspace

Robust Controller for Pursuing Trajectory and Force Estimations of a Bilateral Tele-Operated Hydraulic Manipulator

In hazardous/emergency situations, public safety is of the utmost concern. In areas where human access is not possible or is restricted due to hazardous situations, a system or robot that can be distantly controlled is mandatory. There are many applications in which force cannot be applied directly while using physical sensors. Therefore, in this research, a robust controller for pursuing trajectory and force estimations while deprived of any signals or sensors for bilateral tele-operation of a hydraulic manipulator is suggested to handle these hazardous, emergency circumstances. A terminal sliding control with a sliding perturbation observer (TSMCSPO) is considered as the robust controller for a coupled leader and hydraulic follower system. The ultimate use of this controller is as a sliding perturbation observer (SPO) that can estimate the reaction force without any physical force sensors. Robust and perfect position tracking is attained with terminal sliding mode control (TSMC) in addition to control of the hydraulic follower manipulator. The force estimation and pursuing trajectory for the leader–follower system is built upon a bilateral tele-operation control approach. The difference between the reaction forces (caused by the remote environment) and the operating forces (applied by the human operator) required the involvement of an impedance model. The impedance model is implemented in the leader manipulator to provide human operators with an actual sense of the reaction force while the manipulator connects with the remote environment. A camera is used to ensure the safety of the workplace through visual feedback. The experimental results showed that the controller was robust at pursuing trajectory and force estimations for the bilateral tele-operation control of a hydraulic manipulator

Tele-Operated Bilateral Control of Hydraulic Manipulator Using a Robust Controller Based on the Sensorless Estimated Reaction Force

In nuclear power plants (NPP), dismantling is the most technically involved process during their life time. During the dismantling process, public safety must be ensured. In crisis situations, a remotely controlled robot system is needed for the dismantling of NPP. Therefore, in this research, a bilateral tele-operation system is proposed to tackle these emergency conditions. Transparency can be improved by using force and position signal in the control strategy. In some applications, force cannot be determine directly using physical sensors. In this work, a novel tele-operated bilateral control strategy is proposed to estimate the reaction force of 3-degree-of-freedom (DOF) master and hydraulic slave manipulators without the use of a sensor. The control strategy is developed by using sliding mode control with sliding perturbation observer (SMCSPO). The sliding perturbation observer (SPO) estimates the reaction force at the end effector and second link without using sensors. The sliding mode control (SMC) is used as a tele-operated bilateral controller for the robust position tracking and control of the slave device. The impedance model is used to differentiate between the applied force (force exerted by operator) and the reaction force due to the remote environment. Different experiments were performed to verify the proposed strategy. The results indicate that the slave manipulator exactly follows the trajectory of the master device. A camera is used to take visual feedback of the workspace for safety purpose. This technique can also be applied for higher-order DOF manipulators in NPP

An Asymmetric Bargaining Model for Natural-Gas Distribution

For the sustainable socio-economic growth, the energy supply is one of the foundations for any country. The gas shortage is one of the most significant impediments to any emerging country’s economic progress, making it a contested and disputed resource. In the middle of a supply–demand mismatch, distributing limited available gas across administrative units/provinces with competing requirements is a key challenge. In this work, an asymmetric gas allocation bargaining model is proposed under gas shortage to resolve natural gas-related disputes among Pakistan’s administrative units/provinces. Each administrative unit/province is characterized by its gas demand. Results show that the Nash bargaining theory, when applied under equal and bargaining weights, can address the supply–demand mismatches of the gas sector in Pakistan. Such an approach could help policymakers to make a fair gas-supply management system during gas shortage periods and would help in resolving the disputes between the provinces

Evaluation of a Sustainable Urban Transportation System in Terms of Traffic Congestion—A Case Study in Taxila, Pakistan

Traffic delays are not wholly new and are a well-known problem that impacts many of the world’s populations through disruptions and pollution. The rising urbanization and quantity of powered road vehicles necessitate a greater traffic control demand to maintain flow and avoid jams. In order to understand the notion of sustainable transportation, this study first examined sustainable transportation systems. This research then assessed Pakistan’s present transportation infrastructure and urban transportation to find the most reasonable and sustainable alternative to reduce congestion. The Taxila intersection was utilized as a pilot study area because of its vicinity to Pakistan’s leading economic hubs (i.e., industrial estates and the twin cities of Islamabad and Rawalpindi). The study used multi-criteria decision making (MCDM) techniques, including the fuzzy AHP, TOPSIS, VIKOR, and traffic simulation software, to determine the optimal solution for a more sustainable transportation system, and reducing traffic congestion. A pairwise comparison of the criteria and alternatives was made using a survey. This survey was used to look into the perspectives of various stakeholders and experts. The outcomes of the fuzzy AHP-TOPSIS and fuzzy AHP-VIKOR revealed that a flyover is the best alternative. In contrast, the best alternative, according to the software was a parking area. Ultimately, we assessed our results using the literature, and site observation, and concluded that a parking area would be the most sustainable alternative in the Taxila intersection

A Robust Computer-Aided Automated Brain Tumor Diagnosis Approach Using PSO-ReliefF Optimized Gaussian and Non-Linear Feature Space

Brain tumors are among the deadliest diseases in the modern world. This study proposes an optimized machine-learning approach for the detection and identification of the type of brain tumor (glioma, meningioma, or pituitary tumor) in brain images recorded using magnetic resonance imaging (MRI). The Gaussian features of the image are extracted using speed-up robust features (SURF), whereas its non-linear features are obtained using KAZE, owing to their high performance against rotation, scaling, and noise problems. To retrieve local-level information, all brain MRI images are segmented into an 8 × 8 pixel grid. To enhance the accuracy and reduce the computational time, the variance-based k-means clustering and PSO-ReliefF algorithms are employed to eliminate the redundant features of the brain MRI images. Finally, the performance of the proposed hybrid optimized feature vector is evaluated using various machine learning classifiers. An accuracy of 96.30% is obtained with 169 features using a support vector machine (SVM). Furthermore, the computational time is also reduced to 1 min compared to the non-optimized features used for training of the SVM. The findings are also compared with previous research, demonstrating that the suggested approach might assist physicians and doctors in the timely detection of brain tumors

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors