On the design of a nonlinear model predictive controller based on enhanced disturbance observer for dynamic walking of biped robots

In this paper a nonlinear model predictive control strategy based on enhanced nonlinear disturbance observer is proposed to control the dynamic walking of biped robots on the smooth surface considering double support phase, single support phase, and impact. Optimal tracking of reference trajectories via optimal joint torque is established via a nonlinear predictive controller with well-defined cost functions and associated constraints. The implementation of a conventional disturbance observer encounters numerous challenges due to the joint acceleration requirements. The proposed nonlinear disturbance observer here, which only requires the position and angular velocity, helps to estimate the disturbances introduced on the robot and reduce the complications. The simulation results performed on the dynamic walking of a 5-DOF biped robot on flat surface shows the merits of the proposed method in tracking arbitrary trajectories despite the disturbances

Robust fault detection and isolation in semi-actively controlled building structures using a set of unknown input observers

Building structures are subject to earthquakes and unwanted vibrations which can be effectively managed via controllers. Semi-actively controlled building structures are prone to sensor and actuator faults very similar to other dynamical systems. When a fault occurs in sensors or actuators of a controlled systems, the system faces a performances degradation or even failure. Consequently, it is vitally important to detect and isolate a fault at the right time in these systems. To do so, here, unknown input observers (UIO) are proposed for robust fault detection and isolation of actuators and sensors in buildings. For the proof of concept, a three-story structure with magnetorheological (MR) dampers is taken into account. Via designing these observers, each faulty actuator and/or sensor is detected and isolated. Here, the LQR controller is also used to facilitate an optimal control strategy over the system. The obtained simulation results utter the acceptable accuracy of the proposed method in the detection of fault (time and location) and the robustness of the fault detection method against external disturbances

Performance evaluation of convolutional auto encoders for the reconstruction of Li-ion battery electrode microstructure

Li-ion batteries play a critical role in the transition to a net-zero future. The discovery of new materials and the design of novel microstructures for battery electrodes is necessary for the acceleration of this transition. The battery electrode microstructure can potentially reveal the cells’ electrochemical characteristics in great detail. However, revealing this relation is very challenging due to the high dimensionality of the problem and the large number of microstructure features. In fact, it cannot be achieved via the traditional trial-and-error approaches, which are associated with significant cost, time, and resource waste. In search for a systematic microstructure analysis and design method, this paper aims at quantifying the Li-ion battery electrode structural characteristics via deep learning models. Deliberately, here, a methodology and framework are developed to reveal the hidden microstructure characteristics via 2D and 3D images through dimensionality reduction. The framework is based on an auto-encoder decoder for microstructure reconstruction and feature extraction. Unlike most of the existing studies that focus on a limited number of features extracted from images, this study concentrates directly on the images and has the potential to define the number of features to be extracted. The proposed methodology and model are computationally effective and have been tested on a real open-source dataset where the results show the efficiency of reconstruction and feature extraction based on the training and validation mean squared errors between 0.068 and 0.111 and from 0.071 to 0.110, respectively. This study is believed to guide Li-ion battery scientists and manufacturers in the design and production of next generation Li-ion cells in a systematic way by correlating the extracted features at the microstructure level and the cell’s electrochemical characteristics

Two layer Markov model for prediction of future load and end of discharge time of batteries

To predict the remaining discharge energy of a battery, it is significant to have an accurate prediction of its end of discharge time (EoDT). In recent studies, the EoDT is predicted by assuming that the battery load profile (current or power) is a priori known. However, in real-world applications future load on a battery is typically unknown with high dynamics and transients. Therefore, predicting battery EoDT in an online manner can be very challenging. The purpose of this paper is to derive a load prediction method for capturing historical charge/discharge behaviour of a battery to generate the most probable future usage of it, enabling an accurate EoDT prediction. This method is based on a two layer Markov model for the load extrapolation and iterative model-based estimation. To develop the proposed concept, lithium-ion batteries are selected and the numerical simulation results show an improvement in terms of the accuracy of the EoDT prediction compared to methods presented in the literature

Stabilization of arbitrary switched nonlinear fractional order dynamical systems : application to Francis Hydro-Turbine Governing System

This paper is a theoretical and practical study on the stabilization of fractional order Lipschitz nonlinear systems under arbitrary switching. The investigated system is a generalization of both switched and fractional order dynamical systems. Firstly, a switched frequency distributed model is introduced as an equivalent for the system. Subsequently, a sufficient condition is obtained for the stabilizability of the system based on the Lyapunov approach. Finally, the results are extended to synthesis mode-dependent state feedback controller for the system. All the results are expressed in terms of coupled linear matrix inequalities, which are solvable by optimization tools and directly reducible to the conditions of the integer order nonlinear switching systems as well as the conventional non-switched nonlinear fractional order systems. The proposed method has various practical implications. As an example, it is utilized to control Francis hydro-turbine governing system. This system is represented as a switching structure and supposed to supply a load suffering abrupt changes driven by an arbitrary switching mechanism. The simulation results support the usefulness of the method

Riding pattern identification by machine learning for electric motorcycles

Identification of riding patterns is one of the key enablers to update energy consumption strategy, optimise the energy management system and increase the range of electric motorcycles despite their weight and space limits. Considering the varying driving conditions in real applications, improving accuracy of the riding pattern recognition without significant complexity is the main challenge. In this paper a simple and efficient online classification method is introduced based on features extracted only from the motorcycle speed. The recognition mechanism is firstly developed using support vector machine technique. The effect of validation method for removing the optimism in classification and the contribution of features to the accuracy of model is then investigated. Evaluation of the method on the real riding conditions in simulation environment shows the effectiveness of the approach

Load prediction based remaining discharge energy estimation using a combined online and offline prediction framework

Remaining discharge energy (RDE) indicates how much useful energy can be extracted from a battery before reaching the discharge limit. Future current loading on vehicle battery systems can be predicted to increase the accuracy of RDE estimations. This is done by using clustering techniques to group load measurements into states, and then using a probability-based framework, along with real-world data, to calculate the transitional probabilities between states. Here, an adapted K-means clustering method is used to cluster load profile data. Markov modelling is used to produce state transition probabilities. Two methods for load prediction are used, which are referred to as the offline-training method and the moving window method, where the offline-training method has not been implemented for this application before. Additional control logic is implemented to combine the proposed load prediction methods to produce a new hybrid load prediction method. This hybrid method shows improved RDE accuracy for a generalised load case. The robustness of the proposed technique is assessed in the presence of model errors, still showing good accuracy when compared to state-of-charge based calculations

Model-based end of discharge temperature prediction for lithium-ion batteries

Battery fast charging is one of the key techniques that affects the public acceptability and commercialization of electric vehicles. Temperature is the critical barrier for fast charging as at low temperatures an increased risk of lithium plating and at high temperatures safety concerns limits the charging rate. To facilitate a fast charging mechanism, preconditioning the battery and maintaining its temperature is vital. Battery temperature prediction before a fast charging event can help reducing the energy consumption for battery preconditioning. In this paper, we propose a method for battery end of discharge temperature prediction for fast charging purposes. Firstly, a Gaussian mixture data clustering is performed on battery load data characterisation, subsequently a Markov model is trained for load prediction, and finally a battery lumped parameter equivalent circuit and thermal model is developed and employed for end of discharge time and ultimately end of discharge temperature prediction. Cylindrical lithium-ion battery is selected to prove the concept and both simulations and experiments show the capabilities of the proposed method for temperature prediction of batteries under load profiles obtained from real-world drive cycles of electric vehicles

Exponential synchronization of a complex dynamical network with piecewise-homogeneous Markovian jump structure and coupling delay

In this paper, exponential synchronization problem is studied for a complex dynamical network (CDN) with a class of Markovian jump structure with coupling-time delay. The CDN under consideration has a piecewise-Markovian jump structure with piecewise-constant transition rates (TRs). First piecewise-homogeneous Markovian process is modeled by two Markov chains then, the synchronization problem of the CDN is inspected by constructing Lyapunov-Krasovskii function with Markov dependent matrices. Ultimately, the controller gain matrices for guaranteeing the synchronization problem in terms of linear matrix inequalities (LMIs) are derived. A CDN with a Chua circuit dynamic for each node is given as a numerical example to show the effectiveness of the theoretical results

A hybrid forecasting model with logistic regression and neural networks for improving key performance indicators in supply chains

This study investigates the potential of predictive analytics in improving Key Performance Indicators (KPIs) forecasting by leveraging Lean implementation data in supply chain enterprises. A novel methodology is proposed, incorporating two key enhancements: using Lean maturity assessments as a new data source and developing a hybrid forecasting model combining Logistic regression and Neural Network techniques. The proposed methodology is evaluated through a comprehensive empirical study involving 30 teams in a large supply chain company, revealing notable improvements in forecasting accuracy. Compared to a baseline scenario without process improvement data, the new methodology achieves an enhanced accuracy score by 17% and an improved F1 score by 13 %. These findings highlight the benefits of integrating Lean maturity assessments and adopting a hybrid forecasting model, contributing to the advancement of supply chain analytics. By incorporating lean maturity assessments, the forecasting process is enhanced, providing a deeper comprehension of the underlying Lean framework and the impact of its elements on supply chain performance. Additionally, adopting a hybrid model aligns with current best practices in forecasting, allowing for the utilisation of various techniques to optimise KPI prediction accuracy while leveraging their respective strengths