Development of advanced autonomous learning algorithms for nonlinear system identification and control

Identification of nonlinear dynamical systems, data stream analysis, etc. is usually handled by autonomous learning algorithms like evolving fuzzy and evolving neuro-fuzzy systems (ENFSs). They are characterized by the single-pass learning mode and open structure-property. Such features enable their effective handling of fast and rapidly changing natures of data streams. The underlying bottleneck of ENFSs lies in its design principle, which involves a high number of free parameters (rule premise and rule consequent) to be adapted in the training process. This figure can even double in the case of the type-2 fuzzy system. From this literature gap, a novel ENFS, namely Parsimonious Learning Machine (PALM) is proposed in this thesis. To reduce the number of network parameters significantly, PALM features utilization of a new type of fuzzy rule based on the concept of hyperplane clustering, where it has no rule premise parameters. PALM is proposed in both type-1 and type-2 fuzzy systems where all of them characterize a fully dynamic rule-based system. Thus, it is capable of automatically generating, merging, and tuning the hyperplane-based fuzzy rule in a single-pass manner. Moreover, an extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the concept of teacher-forcing mechanism in the deep learning literature. The efficacy of both PALM and rPALM have been evaluated through numerical study with data streams and to identify nonlinear unmanned aerial vehicle system. The proposed models showcase significant improvements in terms of computational complexity and the number of required parameters against several renowned ENFSs while attaining comparable and often better predictive accuracy. The ENFSs have also been utilized to develop three autonomous intelligent controllers (AICons) in this thesis. They are namely Generic (G) controller, Parsimonious controller (PAC), and Reduced Parsimonious Controller (RedPAC). All these controllers start operating from scratch with an empty set of fuzzy rules, and no offline training is required. To cope with the dynamic behavior of the plant, these controllers can add, merge or prune the rules on demand. Among three AICons, the G-controller is built by utilizing an advanced incremental learning machine, namely Generic Evolving Neuro-Fuzzy Inference System. The integration of generalized adaptive resonance theory provides a compact structure of the G-controller. Consequently, the faster evolution of structure is witnessed, which lowers its computational cost. Another AICon namely, PAC is rooted with PALM's architecture. Since PALM has a dependency on user-defined thresholds to adapt the structure, these thresholds are replaced with the concept of bias- variance trade-off in PAC. In RedPAC, the network parameters have further reduced in contrast with PALM-based PAC, where the number of consequent parameters has reduced to one parameter per rule. These AICons work with very minor expert domain knowledge and developed by incorporating the sliding mode control technique. In G-controller and RedPAC, the control law and adaptation laws for the consequent parameters are derived from the SMC algorithm to establish a stable closed-loop system, where the stability of these controllers are guaranteed by using the Lyapunov function and the uniform asymptotic convergence of tracking error to zero is witnessed through the implication of an auxiliary robustifying control term. While using PAC, the boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Their efficacy is evaluated by observing various trajectory tracking performance of unmanned aerial vehicles. The accuracy of these controllers is comparable or better than the benchmark controllers where the proposed controllers incur significantly fewer parameters to attain similar or better tracking performance

Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial Intelligence and Soft Computing Researc

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images

Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV's ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 15 times in terms of classification accuracy and $38.3\%$ in terms of computing efficiency as measured by Floating Point Operations per second (FLOPs). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at \url{https://github.com/TanmDL/WATT-EffNet}.Comment: This paper is accepted in IEEE Trans. GRS

Unlocking the capabilities of explainable fewshot learning in remote sensing

Recent advancements have significantly improved the efficiency and effectiveness of deep learning methods for imagebased remote sensing tasks. However, the requirement for large amounts of labeled data can limit the applicability of deep neural networks to existing remote sensing datasets. To overcome this challenge, fewshot learning has emerged as a valuable approach for enabling learning with limited data. While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies. In this review, we provide an up to date overview of both existing and newly proposed fewshot classification techniques, along with appropriate datasets that are used for both satellite based and UAV based data. Our systematic approach demonstrates that fewshot learning can effectively adapt to the broader and more diverse perspectives that UAVbased platforms can provide. We also evaluate some SOTA fewshot approaches on a UAV disaster scene classification dataset, yielding promising results. We emphasize the importance of integrating XAI techniques like attention maps and prototype analysis to increase the transparency, accountability, and trustworthiness of fewshot models for remote sensing. Key challenges and future research directions are identified, including tailored fewshot methods for UAVs, extending to unseen tasks like segmentation, and developing optimized XAI techniques suited for fewshot remote sensing problems. This review aims to provide researchers and practitioners with an improved understanding of fewshot learnings capabilities and limitations in remote sensing, while highlighting open problems to guide future progress in efficient, reliable, and interpretable fewshot methods.Comment: Under review, once the paper is accepted, the copyright will be transferred to the corresponding journa

Temperature effect analysis on magneto-rheological damper’s performance

Magnetorheological (MR) damper is one of the most advanced applications of semi active damper in controlling vibration. Its use is increasing day by day in the vehicle suspension system due to its continuous controllability in both on and off state. MR damper’s damping force can be controlled by changing the viscosity of its internal magnetorheological fluids (MRF). Applying an external magnetic field viscosity of MRF can be controlled. Electromagnet such as solenoid coil is normally used as external magnetic field source. These coils are attached inside the damper’s piston head. When the damper operates these coils create heat. That’s why the conventional MR dampers normally face wide variations in temperature. This change of temperature results decay in MRF viscosity as well as post-yield damping of the damper. In this paper temperature effect on MR fluids viscosity and on MR dampers performance represented analytically and experimentally. Due to this temperature effect the deviation of MR dampers performance has shown here experimentally. Also a novel technique for solving the problem explained clearly

ANSYS finite element design of an energy saving magneto-rheological damper with improved dispersion stability

The magnetorheological (MR) damper is one of the utmost progressive applications of a semi-active damper. Uninterrupted controllability in both on and off state is an important factor of its plenitude application. Current research is attempting to make the damper more effective and efficient by minimizing the existing limitations such as MR fluid’s sedimentation, power consumption and temperature rising, and design optimization. We have broadly analyzed the optimization of MR dampers design with finite element simulation where various parameters of the MR damper have been considered for more accurate results. A prototype MR fluid has been prepared by coating the carbonyl iron particles with xanthan gum to reduce sedimentation. The SEM and Turbiscan results noticeably verify the improved sedimentation stability. In addition, a power-saving MR damper model has been developed by finite element analysis using ANSYS software. Prolonged operation raises the damper’s body temperature and degrades the performance. However, in this energy-saving MR damper model the temperature is not rising to a higher value compared to the conventional dampers, and consequently promotes damper efficiency