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In this paper, a new filter referred to as the sliding innovation filter (SIF) is presented. The SIF is an estimation strategy formulated as a predictor-corrector that makes use of a switching gain and innovation term. In estimation theory, a trade-off exists between robustness to disturbances and optimality in terms of estimation error. Unlike the Kalman filter (KF), the SIF is a sub-optimal filter in the sense that it does not provide the optimal solution to the linear estimation problem. However, the switching gain provides an inherent amount of robustness to estimation problems that may be ill-conditioned or contain modeling uncertainties and disturbances. The paper includes the proof of stability and explanation of the SIF gain. Furthermore, the SIF is extended to nonlinear estimation problems using a Jacobian matrix, resulting in the extended sliding innovation filter (ESIF). The methods are applied to a linear and nonlinear aerospace actuator system under the presence of a leakage fault. The results of the simulation demonstrate the improved performance of the SIF and ESIF strategies over popular KF-based methods.

This review showcases a comprehensive analysis of studies that highlight the different conversion procedures attempted across the globe. The resources of biogas production along with treatment methods are presented. The effect of different governing parameters like feedstock types, pretreatment approaches, process development, and yield to enhance the biogas productivity is highlighted. Biogas applications, for example, in heating, electricity production, and transportation with their global share based on national and international statistics are emphasized. Reviewing the world research progress in the past 10 years shows an increase of ~ 90% in biogas industry (120 GW in 2019 compared to 65 GW in 2010). Europe (e.g., in 2017) contributed to over 70% of the world biogas generation representing 64 TWh. Finally, different regulations that manage the biogas market are presented. Management of biogas market includes the processes of exploration, production, treatment, and environmental impact assessment, till the marketing and safe disposal of wastes associated with biogas handling. A brief overview of some safety rules and proposed policy based on the world regulations is provided. The effect of these regulations and policies on marketing and promoting biogas is highlighted for different countries. The results from such studies show that Europe has the highest promotion rate, while nowadays in China and India the consumption rate is maximum as a result of applying up-to-date policies and procedures.

SummaryThis paper presents an advanced robust active disturbance rejection control (ADRC) for flexible link manipulator (FLM) to track desired trajectories in the joint space and minimize the link’s vibrations. It has been shown that the ADRC technique has a very good disturbance rejection capability. Both the internal dynamics and the external disturbances can be estimated and compensated in real time. The proposed robust ADRC control law is developed to solve the problems existing in the original version of the ADRC related to the disturbance estimation errors and the variation of the parameters. Indeed, these parameters cannot be included in the existing disturbances and then be estimated by the extended state observer. The proposed control law is based on the sliding mode technique, which considers the uncertainties in the control gains and disturbance estimation errors. Lyapunov theory is used to prove the closed-loop stability of the system. The proposed control strategy is simulated and tested experimentally on one FLM. The effect of the observer bandwidth on the system performance is simulated and studied to select the best values of the bandwidth frequency. The simulation and experimental results show that the proposed robust ADRC has better performance than the traditional ADRC.

This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.

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