Wu-Sung Yao

This study proposes an optimization strategy for a tail-sitter unmanned aerial vehicle (UAV) landing on an autonomous surface vessel (ASV), aiming for minimal risk, shortest time, and lowest energy consumption. Sliding mode control ensures system stability under nonlinear and dynamic conditions. A two-player game model between the UAV and ASV is established, achieving Nash equilibrium to minimize risk and energy. Pareto optimal theory provides a trade-off among multiple objectives, while Karush-Kuhn-Tucker conditions verify the optimality of the solution. An Actor-Critic network generates and evaluates landing strategies through adaptive online learning based on temporal difference errors. Results show that the UAV can dynamically adjust to ship motion, achieving precise, energy-efficient, and low-risk landings. This approach offers practical technical support and theoretical foundations for cooperative operations between UAVs and unmanned ships in diverse environments.

In contrast to polynomial smooth S-curves and typical sine curves, this article presents a revolutionary curve design technique based on the Fibonacci sequence. This technique removes discontinuities that arise during the transition between acceleration segments by adding several fixed-form smooth transition segments into the original curve while maintaining the piecewise structure of the traditional S-curve. The proposed motion profile aims to improve control stability, positioning accuracy, and current performance, while maintaining the performance advantages of a multisegment S-curve design. This article elaborates on the design principles of the modified motion profile and derives its analytical models for acceleration, jerk, velocity, and displacement. Furthermore, it provides complete mathematical formulas for possible profile variants under different parameter settings, as well as profile type selection criteria based on hardware parameters. To evaluate the feasibility of the proposed approach on real control systems and its advantages in accuracy and current behavior, simulations are conducted. This article uses the proposed modified motion profile and the traditional S-curve as motion commands, comparing the speed and acceleration responses under motor models with different damping ratios to verify its feasibility and performance advantages. Furthermore, we conducted experiments on a brushless DC motor and recorded data through the motor's built-in feedback. The results show that, compared to the traditional S-curve, the proposed modified motion profile improves positioning accuracy and current stability.

This study proposes an innovative attitude and guidance control system for unmanned ships, emphasizing an adaptive approach to solve ship navigation and attitude control challenges. By constructing a dynamic state space model of the ship, this research integrates two advanced adaptive algorithms, namely adaptive gradient moment estimation (AMSGrad) and adaptive gradient algorithm (AdaGrad), within the linear quadratic regulator (LQR) framework. These algorithms can be used to enhance on-the-fly convergence of control parameters, ensure robust system performance under dynamic conditions, and reduce modeling errors or installation errors. Unlike traditional control methods that rely heavily on exhaustive modeling and lack on-the-fly adaptability, elastic and flexible systems for autonomous ship operations can be achieved. Research results show that this technology has made a significant contribution to unmanned navigation technology, achieving reliable control, precise navigation, and expanding the scope of applications such as automatic transportation and environmental monitoring. The proposed system highlights key advances in linking theoretical control strategies with practical, real-world implementation in autonomous vessel systems.

This paper proposes a novel approach to fuse the Extended Kalman Filter (EKF) with the Multiscale Fibonacci Filter (MSFibFilter) technique to develop an adaptive filter with adjustable gain characteristics, termed Fibonacci Adaptive Extended Kalman Filter (Fib-AEKF). The innovation of this work lies in enhancing the EKF’s applicability in dynamic environments characterized by high-frequency noise and multi-scale signal processing. By exploiting the multi-scale properties of the Fibonacci sequence to dynamically adjust filter weights, Fib-AEKF can be used to improve noise suppression and signal tracking capabilities. The proposed MSFibFilter uses normalized Fibonacci weights to perform weighted smoothing on the signal to effectively remove noise while retaining the basic signal characteristics. In this study, a Bayesian upper confidence bound modulation strategy is used to enhance the filter’s adaptability. These weights are dynamically adjusted based on the state predictions from the EKF, allowing the filter to adaptively respond to rapid signal changes. The design of the adaptive weights depends on the discrepancy between the predicted state and the actual input signal, enhancing the filter’s sensitivity and robustness to signal fluctuations. By modifying the exponential decay term of the weights, the Fib-AEKF significantly improves the tracking accuracy of nonlinear dynamic system states, especially facing substantial differences between predictions and measurements. Experimental results demonstrate that applying the Fib-AEKF to the processing of acceleration signals in inertial navigation systems leads to a significant improvement in positioning accuracy and stability. The Fib-AEKF outperforms both multi-layer adaptive EKF and single-layer EKF in terms of cumulative error reduction. Its superior performance in inertial navigation applications highlights its potential for practical implementation, offering a more viable and effective solution for real-world dynamic systems. This study provides a new method for handling noise and uncertainties in nonlinear systems, expanding the capabilities of traditional EKF approaches.

In this paper, the modified design of the currently common visual relative motion inertial navigation system (INS) is proposed to enhance the inertial measurement unit (IMU) model algorithm. The aim of the study is to improve the precise landing and indoor positioning-free flight of existing indoor drones. Both the INS and the airborne IMU used for traditional optical flow guidance are designed to input information to the same extended Kalman filter (EKF) functional module, low cost but resulting in signal phase shift and reduced reliability indicators. Unlike large drones, which can easily reduce vibration, for micro drones, the camera usually must be fixed directly to the body, making it very difficult to isolate vibrations. Using traditional methods can easily cause optical flow guidance to drag down the quality of the entire positioning function. In this study, the improved positioning guidance procedure is proposed and is applied to the indoor positioning flight of UAVs. The EKF, based on the kinematics and optical flow characteristic states equations is separated from the main navigation system. The current positioning and heading are predicted in a way where the optical flow INS and IMU are positioned separately. The positioning and heading are estimated through the current trust weighting value of the optical flow INS and the IMU. Experimental results show that the Monte Carlo method is used to simulate the impact of different signal-to-noise ratios (SNR) on acceleration, angular velocity measurement noise, and the number of detected features on the probability of success of the estimation process. At lower SNR, it shows that the proposed method can resist interference more effectively.

Servo dynamic stiffness is a pivotal index in characterizing the rejecting performance for the exogenous periodic disturbances of the repetitive learning control system. In this paper, the mathematical modeling of the servo dynamic stiffness for the well-designed repetitive learning control system with multiple-periodic signals is studied by the frequency analysis method. To analysis the complex form of the servo dynamic stiffness, the magnitudes of the direct (real part) and quadrature (imaginary part) servo dynamic stiffness at harmonics of the disturbance are discussed based on the parameters of the repetitive learning controller. An illustrated example of the two linear servomotors with the mechanical coupling is given in this study. Computer simulation are presented to illustrate the effect of the proposed measurement method of the servo dynamic stiffness for the multiple-periodic repetitive learning control system. The proposed servo dynamic stiffness modeling can be used to accurate design the parameters of the repetitive learning control system, such that the batter control performance of the repetitive learning control system at most of the harmonics of the multi-periodic input signal can be achieved.

In this paper, using automated optical inspection equipment and a thermal imager, the position and the temperature of the heat source or measured object can effectively be grasped. The high-resolution depth camera is with the stereo vision distance measurement and the low-resolution thermal imager is with the long-wave infrared measurement. Based on Planck’s black body radiation law and Stefan–Boltzmann law, the binocular stereo calibration of the two cameras was calculated. In order to improve the measured temperature error at different distances, equipped with Intel Real Sense Depth Camera D435, a compensator is proposed to ensure that the measured temperature of the heat source is correct and accurate. From the results, it can be clearly seen that the actual measured temperature at each distance is proportional to the temperature of the thermal image vanadium oxide, while the actual measured temperature is inversely proportional to the distance of the test object. By the proposed compensation function, the compensation temperature at varying vanadium oxide temperatures can be obtained. The errors between the average temperature at each distance and the constant temperature of the test object at 39 °C are all less than 0.1%.