Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches

An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization

Study the Effect of Substitution Filler on performance of Asphalt Mixture

The major distresses in asphalt pavements are rutting, fatigue, and adhesion loss (moisture susceptibility). In this research study, two substitution fillers (Cement and Lime) were used with two different aggregate quarries (based on minerals composition) to evaluate the relatively most beneficial combination of both fillers as well as an aggregate quarry to enhance the performance life of asphalt pavements, especially in under-developed countries. Four basic tests, (Asphalt Pavement Analyzer, Four Points Bending Beam, Dynamic Modulus, and Rolling Bottle Test) that used for the most desired properties of any asphalt pavement, were utilized to access the performance properties of modified asphalt mixture. Based on all laboratory test results this research study concludes that replacement of aggregate filler with hydrated lime and cement has a beneficial effect on asphalt mix performance and to save investment by using raw material. Substitution filler improves the high-temperature rut performance and intermediate temperature fatigue performance of asphaltic concrete mixture up to 25% to that of the conventional mixture. At the same time, substitution filler has more beneficial to improve 70% adhesion properties to that of the conventional mixture

Maximum Power Extraction from a Standalone Photo Voltaic System via Neuro-Adaptive Arbitrary Order Sliding Mode Control Strategy with High Gain Differentiation

In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuroadaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions

Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems

The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation

An application of heuristic optimization algorithm for demand response in smart grids with renewable energy

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms

Identification of GLI1 and KIAA0825 Variants in Two Families with Postaxial Polydactyly

Polydactyly is a rare autosomal dominant or recessive appendicular patterning defect of the hands and feet, phenotypically characterized by the duplication of digits. Postaxial polydactyly (PAP) is the most common form and includes two main types: PAP type A (PAPA) and PAP type B (PAPB). Type A involves a well-established extra digit articulated with the fifth or sixth metacarpal, while type B presents a rudimentary or poorly developed superfluous digit. Pathogenic variants in several genes have been identified in isolated and syndromic forms of polydactyly. The current study presents two Pakistani families with autosomal recessive PAPA with intra- and inter-familial phenotype variability. Whole-exome sequencing and Sanger analysis revealed a novel missense variant in KIAA0825 (c.3572C>T: p.Pro1191Leu) in family A and a known nonsense variant in GLI1 (c.337C>T: p.Arg113*) in family B. In silico studies of mutant KIAA0825 and GLI1 proteins revealed considerable structural and interactional modifications that suggest an abnormal function of the proteins leading to the disease phenotype. The present study broadens the mutational spectrum of KIAA0825 and demonstrates the second case of a previously identified GLI1 variant with variable phenotypes. These findings facilitate genetic counseling in Pakistani families with a polydactyly-related phenotype

Understanding Mobile Tourism Shopping in Pakistan: An Integrating Framework of Innovation Diffusion Theory and Technology Acceptance Model

Consumer adoption of mobile-based tourism shopping is an emerging but overlooked area in tourism research. Given the paybacks and potential scope of this new channel, this study attempts to bridge the gap by proposing a multimediation model investigating mobile tourism shopping (MTS) in a developing country, Pakistan. In particular, we applied structural equation modeling through partial-least-squares structural equation modeling (PLS-SEM) on 396 responses collected from mobile respondents who recently purchased tourism products using a mobile device(s). It was discovered that social presence, directly and indirectly, influences tourist intentions towards MTS. The results further show that the tourists’ perception of compatibility and relative advantages of MTS have insignificant influence on their intention to accept a mobile device(s) for tourism shopping. The findings and implications of the study furnish new vistas to research discourse and managerial significance. Economically, this research contributes to knowledge that could increase income and create jobs in the host country

Factors Determining Consumer Acceptance of NFC Mobile Payment: An Extended Mobile Technology Acceptance Model

The demand for mobile payments using smartphones to substitute the need for cash, credit cards, or checks is swiftly increasing in Pakistan. This study investigates the factors determining consumers’ behavioral intention to adopt near-field communication mobile payment from a developing country’s viewpoint. A conceptual framework was adopted based on the mobile technology acceptance model (MTAM), integrating self-efficacy theory, critical mass theory, flow theory, and system and service quality to elucidate the behavioral intention. Data were collected through a self-administered questionnaire applied to 310 nonusers of near-field communication mobile payment in Pakistan. The analysis was performed using SmartPLS3.0. The results demonstrated that other independent variables are the main predictors of the intention to adopt mobile payment besides technology self-efficacy, perceived critical mass, and mobile ease of use. The study concludes with key implications and future work directions concerning the limitation of this study