The large number of interdigitated electrodes (IDEs) in a macro fiber composite (MFC) piezoelectric actuator dictates using a very fine finite element (FE) mesh that requires extremely large computational costs, especially with a large number of actuators. The situation becomes infeasible if repeated finite element simulations are required, as in control tasks. In this paper, an efficient technique is proposed for modeling MFC using a finite element method. The proposed technique replaces the MFC actuator with an equivalent simple monolithic piezoceramic actuator using two electrodes only, which dramatically reduces the computational costs. The proposed technique was proven theoretically since it generates the same electric field, strain, and displacement as the physical MFC. Then, it was validated with the detailed FE model using the actual number of IDEs, as well as with experimental tests using triaxial rosette strain gauges. The computational costs for the simplified model compared with the detailed model were dramatically reduced by about 74% for memory usage, 99% for result file size, and 98.6% for computational time. Furthermore, the experimental results successfully verified the proposed technique with good consistency. To show the effectiveness of the proposed technique, it was used to simulate a morphing wing covered almost entirely by MFCs with low computational cost.

Accurate multi-object tracking (MOT) is essential for autonomous vehicles, enabling them to perceive and interact with dynamic environments effectively. Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in unreliable tracking. Recent multi-modal approaches have improved performance but rely heavily on complex, deep-learning-based fusion techniques. In this work, we present CLF-BEVSORT, a camera-LiDAR fusion model operating in the bird’s eye view (BEV) space using the SORT tracking framework. The proposed method introduces a novel association strategy that incorporates structural similarity into the cost function, enabling effective data fusion between 2D camera detections and 3D LiDAR detections for robust track recovery during short occlusions by leveraging LiDAR depth. Evaluated on the KITTI dataset, CLF-BEVSORT achieves state-of-the-art performance with a HOTA score of 77.26% for the Car class, surpassing StrongFusionMOT and DeepFusionMOT by 2.13%, with high precision (85.13%) and recall (80.45%). For the Pedestrian class, it achieves a HOTA score of 46.03%, outperforming Be-Track and StrongFusionMOT by (6.16%). Additionally, CLF-BEVSORT reduces identity switches (IDSW) by over 45% for cars compared to baselines AB3DMOT and BEVSORT, demonstrating robust, consistent tracking and setting a new benchmark for 3DMOT in autonomous driving.

In recent research works, morphing wings were studied as an interesting field for a small unmanned aerial vehicle (UAV). The previous studies either focused on selecting suitable material for the morphing wings or performing experimental tests on UAVs with morphing wings. Though, the dynamic modeling of active flexible morphing wings and their involved interactions with the aerodynamics of the UAV body are challenging subjects. Using such a model to control a small UAV to perform specific maneuvering is not investigated yet. The dynamic model of UAV with active morphing wings generates a multi-input multi-output (MIMO) system which rises the difficulty of the control system design. In this paper, the aeroelastic dynamic model of morphing wing activated by piezocomposite actuators is established using the finite element method and modal decomposition technique. Then, the dynamic model of the UAV is developed taking into consideration the coupling between the wing and piezocomposite actuators, as well as the dynamic properties of the morphing actuators with the aerodynamic wind disturbances. A model predictive control (MPC) is designed for the MIMO control system to perform specific flight maneuvering by tracking desired trajectories of UAV altitude and yaw angle. Additionally, the MPC achieves constrained behavior of pitch and roll angles to get satisfactory UAV motion. Also, the behaviors of the UAV control system using MPC are evaluated after adding Dryden wind turbulence to the UAV outputs. Finally, a UAV flight simulation is conducted which shows that the control system successfully rejects the applied disturbances and tracks the reference trajectories with acceptable behavior of pitch and roll angles.

Autonomous Underwater Vehicles (AUVs) are essential for applications such as seabed mapping, environmental monitoring, offshore infrastructure inspection, and search-and-rescue operations. However, achieving accurate trajectory tracking remains a fundamental challenge due to nonlinear and strongly coupled six-degree-of-freedom dynamics, hydrodynamic parameter uncertainties, environmental disturbances, and limitations introduced by sensor noise, actuator faults, and imperfect modeling of added mass, damping, and Coriolis–centripetal forces. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review systematically classifies trajectory-tracking control strategies for AUVs into three main categories. (i) Classical controllers, including PID and Sliding Mode Control (SMC), are analyzed for their simplicity, robustness, and ease of implementation. (ii) Intelligent controllers, such as Fuzzy Logic Control (FLC), Reinforcement Learning (RL), and Physics-Informed Neural Networks (PINNs), are reviewed for their adaptability, learning capabilities, and effectiveness in handling nonlinearities and time-varying disturbances, while addressing challenges related to data availability and computational cost. (iii) Hybrid approaches, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Physics-Informed Reinforcement Learning (PI-RL), Fault-Tolerant Control (FTC), and Sim-to-Real Transfer (SRT) techniques, are examined for their ability to integrate model-based reliability with data-driven adaptability and improve resilience under uncertainties, noise, and faults. Classical controllers provide structural simplicity and robustness but suffer reduced accuracy in highly nonlinear and noisy environments. Intelligent methods like RL and PINNs improve adaptability and reduce tracking errors but demand extensive data and computational resources. Hybrid approaches, particularly ANFIS and PI-RL, achieve high tracking accuracy and maintain robust performance under various uncertainties. Sim-to-Real Transfer (SRT) techniques further enhance real-world deployment. A meta-analysis of 120 peer-reviewed studies quantifies performance trends in terms of root-mean-square error (RMSE), settling time, robustness, and computational cost. The review highlights future research opportunities in domain randomization, adaptive fault-tolerant control, and physics-guided hybrid learning to enable reliable real-world AUV operations.

Complex systems, considered in control engineering, are often modeled in the form of a multi-input-multi-output (MIMO) coupled model. These systems pose definite challenges, due to their coupling behavior and the associated high computational costs. This paper describes a proposal for a technique for designing proportional-integral-derivative plus filter (PIDF) controllers for high order MIMO systems, while compensating the inherited coupling effect. The high-order MIMO system is decoupled into single-input-single- output (SISO) subsystems, which are then investigated by analyzing their time and frequency responses, pole-zero maps, and closed-loop stability. PIDF controllers with phase lead compensators are designed for each subsystem model so as to satisfy the desired closed-loop specifications. Additional controllers are introduced in order to eliminate the coupling effects in the coupled control system, while respecting the physical constraints. The proposed technique is validated through numerical simulations on a coupled control system with a high-order MIMO model, which demonstrate that the proposed approach can achieve the control requirements with acceptable performance for various reference inputs.

Autonomous exploration requires local navigation that reacts to newly observed geometry under strict runtime constraints while respecting non-holonomic motion. This paper investigates whether waypoint execution must rely on finite-horizon local optimization or can instead use reactive geometric control with hierarchical perception. We present \emph{RMP-Nav}, a reactive local planner for differential-drive robots that composes Riemannian Motion Policies for goal attraction, yaw regulation, and obstacle avoidance on a robot-centric hierarchical wavemap. A virtual lookahead--pullback mechanism converts obstacle-induced lateral repulsion into steering commands, enabling non-holonomic waypoint execution without trajectory sampling. The method is evaluated in simulation within the Autonomous Exploration Development Environment as a drop-in replacement for the optimization-based FALCO planner across Campus, Forest, and Garage scenarios under identical closed loop exploration pipelines. RMP-Nav reduces mean local-planning runtime from the millisecond range to below 0.1~ms per invocation while achieving waypoint-completion scores comparable to FALCO in Campus ($88.68\pm7.51\%$ vs.\ $87.74\pm7.69\%$) and Forest ($80.61\pm7.98\%$ vs.\ $76.83\pm8.49\%$), with a moderate decrease in Garage ($88.54\pm7.40\%$ vs.\ $92.90\pm5.37\%$). Across trials, RMP-Nav also exhibits lower jerk, increased obstacle clearance, and greater distance traveled per CPU-second. These results indicate that reactive RMP-based control with hierarchical volumetric perception is effective in geometry-dominated environments, while finite-horizon optimization remains advantageous in topologically constrained settings.