Amirmehdi Yazdani

Efficient global localization of mobile robots in symmetrical indoor environments remains a formidable challenge, given the inherent complexities arising from uniform structures and a dearth of distinctive features. This review paper conducts an in-depth investigation into the nuances of global localization strategies, focusing on symmetrical environments, such as extended corridors, symmetrical rooms, tunnels, and industrial warehouses. The study comprehensively reviews and categorizes key techniques employed in this context, encompassing probabilistic-based approaches, learning-based approaches, Simultaneous Localization and Mapping (SLAM)-based approaches, and optimization-based approaches. The primary goal is to provide a contemporary and thorough literature review, offering insights into existing global localization solutions, followed by extant methods tailored for symmetrical indoor spaces. Also, the paper addresses practical challenges associated with implementing various global localization techniques, contributing to a holistic understanding of their real-world applicability. Comparative experimental results demonstrate that hybrid approaches achieve superior localization accuracy in symmetrical environments compared to any single method alone. These experiments, conducted in indoor settings with different symmetry levels, highlight the hybrid approach’s robustness and precision in resolving symmetry-induced ambiguities. This work signifies a significant step forward in mobile robot global localization, which addresses symmetrical environments’ complexities by leveraging the strengths of hybrid methodologies.

Reliable transfer of control policies from simulation to real-world robotic systems remains a central challenge in robotics, particularly for car-like mobile robots. Digital Twin (DT) technology provides a robust framework for high-fidelity replication of physical platforms and bi-directional synchronization between virtual and real environments. In this study, a DT-based testbed is developed to train and evaluate an imitation learning (IL) control framework in which a neural network policy learns to replicate the behavior of a hybrid Model Predictive Control (MPC)–Backstepping expert controller. The DT framework ensures consistent benchmarking between simulated and physical execution, supporting a structured and safe process for policy validation and deployment. Experimental analysis demonstrates that the learned policy effectively reproduces expert behavior, achieving bounded trajectory-tracking errors and stable performance across simulation and real-world tests. The results confirm that DT-enabled IL provides a viable pathway for Sim2Real transfer, accelerating controller development and deployment in autonomous mobile robotics.

This paper proposes a novel method to tackle the growing problem of system instability in microgrids, which is brought on by the widespread adoption of renewable energy sources (RESs) and distributed generators (DGs). Connecting RESs and DGs to microgrids through power electronic interfaces leads to a decrease and fluctuation in the system's inertia. This reduction in inertia leads to increased uncertainty and instability in the system, necessitating either load shedding or the curtailment of renewable generation, which are undesirable for both utilities and customers. To address these challenges, this paper provides a novel approach for enhancing inertia in microgrids to avoid any instability or load shedding. This approach introduces a multi‐objective optimization algorithm based on the non‐dominated sorting genetic algorithm II (NSGA‐II), implemented in a co‐simulation platform combining MATLAB and DIgSILENT PowerFactory. The algorithm simultaneously determines the minimum required capacity of battery energy storage systems (BESSs) acting as virtual inertia and their optimum droop coefficients to stabilize the grid and prevent load shedding. The proposed approach is formulated within the context of a multi‐objective optimization algorithm, by utilizing the NSGA‐II in an integrated DIgSILENT and MATLAB framework. The simulation results show the positive results of the proposed approach in stabilizing the microgrid of Broome city and avoiding any load shedding. The method is validated under three critical scenarios: a 0.97 MW step load increase, a cloud event reducing PV output by 49%, and a synchronous generator outage. Results show that the optimized BESS configuration successfully maintains frequency stability and avoids any load shedding. The minimum sizing of BESSs and their optimum droop coefficient are obtained for different scenarios including step load change, cloud event, and synchronous generator outage. Compared to conventional approaches, the proposed method significantly reduces the required BESS capacity while ensuring compliance with frequency nadir and RoCoF constraints. This approach provides a cost‐effective and scalable decision‐making tool for microgrid operators to enhance system resilience and customer satisfaction. The outcome of this research is a critical decision‐making tool for the microgrid owner to cost‐effectively decide the virtual inertia sizing and their parameters for stabilization of microgrid and to improve customer satisfaction by avoiding load shedding. This contribution bridges the gap between academic research and field‐level implementation, offering a scalable and adaptable strategy for future renewable‐rich microgrids.

This paper presents the optimal sizing of solar photovoltaic (PV) and battery energy storage systems (BESs) for grid‐connected houses with electric vehicles (EVs) by considering vehicle‐to‐home (V2H) operation. To minimise the cost for residential households, particle swarm optimisation (PSO) is utilised, and a novel rule‐based home energy management system (HEMS) is implemented. Stochastic functions are used to investigate the uncertainties regarding the availability of EVs based on arrival and departure times and initial state of charge (SOC). The impact of V2H integration, maximum daily energy export, and electricity costs are thoroughly examined through simulations and sensitivity analysis. The simulation results confirm that utilising V2H operation in a grid‐connected household can eliminate the need for a BES while still reducing electricity costs about 5% compared to a household equipped with PV, BES, and EV without V2H operation.

This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for vehicular electronic throttle (VET) systems with parametric uncertainties, lumped uncertainty and actuator constraint. The proposed control strategy adopts a fixed-time sliding mode (FTSM) dynamical structure which ensures a fixed time convergence property for both reaching motion and sliding motion. For relaxing the upper bound information constraint of sliding mode control design, an ELM-based mechanism is utilized for learning the lumped uncertainty bound online, while the ELM output weights are updated adaptively in the sense of Lyapunov. Also, considering that the input voltage is normally limited due to the vehicular battery constraint for practical applications, to handle the input saturation scenario, an auxiliary system (AS) is further designed to guarantee a fixed time convergence of auxiliary state and the state is correspondingly fed into the controller design. The global stability analysis of the closed-loop system is rigidly given. Comparative experimental studies are conducted to illustrate the excellent performance of the proposed control.

In this paper, a learning-based nearly optimal control framework with fault-tolerant capability is designed to tackle the tracking control problem of a flexible-link manipulator in the presence of actuator fault and model uncertainties. Initially, the optimal control law is obtained by adopting the dynamic programming and a critic structure as the solution of Hamilton–Jacobi–Bellman equation for the nominal model. Then, by implementing an integral sliding mode control, the robustness against actuator fault and model uncertainty is guaranteed. The adaptive laws are constructed based on radial basis functions neural networks to estimate the upper bound of uncertainty and the actuator bias fault, satisfying both optimal performance and chattering reduction of the sliding surface. Furthermore, the actuator effectiveness loss is handled. The stability of the closed-loop system is analytically proven, and the performance of the proposed framework is investigated against several practical operating conditions. This incorporates the fidelity assessment of tracking precision and trackability of control signal using performance indices such as the integral absolute error and root-mean-square error. The results of extensive simulation studies confirm the effectiveness and robustness of the proposed control framework.

This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA’s transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain.

Integrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO‐based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary‐based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.

This paper proposes a perception-aware online trajectory generation system that facilitates prescribed manoeuvres of an unmanned surface vehicle (USV) in a dynamic unstructured environment. The proposed system is developed based on the principles of the inverse dynamic in the virtual domain (IDVD) method and an event-triggered receding horizon (ETRHC) mechanism. This approach transforms the underlying nonconvex constrained optimization problem into a virtual space with a differentially flat dynamics and uses relatively few decision variables to prototype feasible quasi-optimal trajectories. The closed-loop configuration is provided by a computationally efficient ETRHC mechanism that uses situational awareness of operating environment to trigger trajectory replanning if/when required. This addresses the challenge of continuously updating a closed-loop trajectory which imposes unnecessary computational burden on a system with the limited onboard resources. To investigate the performance of the proposed trajectory generating system, a dynamic unstructured environment including variable and uncertain no-fly zone areas as well as variable current vector fields are modelled. Further, different operating conditions incorporating the uncertainties of environment and sudden failure on the USV propulsion system are introduced to examine the effectiveness, agility, and robustness of the proposed trajectory generating system. A comparative study with benchmark solutions generated by the hp-adaptive Radau pseudo-spectral method is conducted to provide a detailed statistical analysis of the proposed approach robustness, computational complexity, and effectiveness. The simulation results confirm the effectiveness of the proposed trajectory generator and ability to produce a solution for online realization.

A continuous adaptive fast terminal sliding mode (CAFTSM)-based speed regulation control is proposed for a permanent magnet synchronous motor (PMSM) drive, where an improved super twisting algorithm with linear terms (STLT) is constructed in the super-twisting speed observer. Firstly, the STLT observer introduces a first-order term on the basis of conventional super-twisting sliding mode observer to improve the speed estimation convergence rate and to reduce the estimation error. Secondly, an adaptive function is added to the reaching law for the continuous fast terminal sliding mode speed controller to produce a faster response speed and a stronger control robustness. In addition, the total disturbance of the system is estimated via Luenberger disturbance observer (LDOB), which contributes to the feedforward compensation control component. The closed-loop stability analysis is rigorously given by Lyapunov stability theory. Finally, real-time experiments on a PMSM platform are carried out to show the effectiveness and superior performance of the proposed control approach.