Search Results (1,419)

This paper introduces a nonlinear Lyapunov-based Finite-Time State-Dependent Differential Riccati Equation (FT-SDDRE) control scheme, considering actuator saturation constraints and ensuring that the control system operates within safe operational limits designed for satellite reconfiguration and formation-keeping in low Earth orbit (LEO) missions. This control approach addresses the challenges of reaching the relative position and velocity vectors within a defined timeframe amid various orbital perturbations. The proposed approach guarantees precise formation control by utilizing a high-fidelity relative motion model that incorporates all zonal harmonics and atmospheric drag, which are the primary environmental disturbances in LEO. Additionally, the article presents an optimization methodology to determine the most efficient State-Dependent Coefficient (SDC) form regarding fuel consumption. This optimization process minimizes energy usage through a hybrid genetic algorithm and simulated annealing (HGASA), resulting in improved performance. In addition, this paper includes a sensitivity analysis to identify the optimized SDC parameterization for different satellite reconfiguration maneuvers. These maneuvers encompass radial, along-track, and cross-track adjustments, each with varying baseline distances. The analysis provides insights into how different parameterizations affect reconfiguration performance, ensuring precise and efficient control for each type of maneuver. The finite-time controller proposed here is benchmarked against other forms of SDRE controllers, showing reduced error margins. To further assess the control system’s effectiveness, an input saturation constraint is integrated, ensuring that the control system operates within safe operational limits, ultimately leading to the successful execution of the mission. Full article

In this paper, we consider the application of brute force computational techniques (BFCTs) for solving computational problems in mathematical analysis and matrix algebra in a floating-point computing environment. These techniques include, among others, simple matrix computations and the analysis of graphs of functions. Since BFCTs are based on matrix calculations, the program system MATLAB^® is suitable for their computer realization. The computations in this paper are completed in double precision floating-point arithmetic, obeying the 2019 IEEE Standard for binary floating-point calculations. One of the aims of this paper is to analyze cases where popular algorithms and software fail to produce correct answers, failing to alert the user. In real-time control applications, this may have catastrophic consequences with heavy material damage and human casualties. It is known, or suspected, that a number of man-made catastrophes such as the Dharhan accident (1991), Ariane 5 launch failure (1996), Boeing 737 Max tragedies (2018, 2019) and others are due to errors in the computer software and hardware. Another application of BFCTs is finding good initial guesses for known computational algorithms. Sometimes, simple and relatively fast BFCTs are useful tools in solving computational problems correctly and in real time. Among particular problems considered are the genuine addition of machine numbers, numerically stable computations, finding minimums of arrays, the minimization of functions, solving finite equations, integration and differentiation, computing condensed and canonical forms of matrices and clarifying the concepts of the least squares method in the light of the conflict remainders vs. errors. Usually, BFCTs are applied under the user’s supervision, which is not possible in the automatic implementation of computational methods. To implement BFCTs automatically is a challenging problem in the area of artificial intelligence and of mathematical artificial intelligence in particular. BFCTs allow to reveal the underlying arithmetic in the performance of computational algorithms. Last but not least, this paper has tutorial value, as computational algorithms and mathematical software are often taught without considering the properties of computational algorithms and machine arithmetic. Full article

This paper investigates the dynamic event-triggered (ET) sliding mode control (SMC) of Markov jump delayed systems (MJDSs) with partially known transition probabilities. Firstly, a dynamic ET scheme is introduced for the Markov SMC system, and the effect of time delays is considered. In addition, the Razumikhin condition is used to deal with the time delay. Moreover, in the case of a Markov jump system with partially known transition probabilities, using the vertex method, weak infinitesimal generator, and Dynkin’s formula, the finite-time boundness (FTB) problem of a class of ET SMC systems with stochastic delay is studied. Finally, a numerical example is given to illustrate the viability of our results. Full article

This study aims to facilitate a comparison between construction monitoring data and simulation results, focusing on the dynamic adjustment of safety monitoring parameters in shield construction. First, a finite element simulation was performed to define a reasonable range for shield parameters based on settlement control values, thereby determining the theoretical settlement value. An early warning system was then developed integrating two key factors: theoretical and control settlement values. Finally, Dynamo was used to merge the digital and analog data, enhancing the visual representation of the monitoring information. The findings show that combining simulations with an early warning system effectively addresses the dynamic control challenges of shield construction parameters. Furthermore, integrating digital and analog monitoring significantly improves the efficiency of real-time visualization in monitoring data. This research provides a novel and effective methodology for enhancing shield tunnel construction safety, precision, and efficiency, offering critical insights for large-scale infrastructure projects and contributing to more reliable monitoring systems in complex construction environments. Full article

This research addresses minimum-fuel pinpoint lunar landing at the South Pole, focusing on trajectory design and near-optimal guidance aimed at driving a spacecraft from a circular low lunar orbit (LLO) to an instantaneous hovering state above the lunar surface. Orbit dynamics is propagated in a high-fidelity ephemeris-based framework, which employs spherical coordinates as the state variables and includes several harmonics of the selenopotential, as well as third-body gravitational perturbations due to the Earth and Sun. Minimum-fuel two-impulse descent transfers are identified using Lambert problem solutions as initial guesses, followed by refinement in the high-fidelity model, for a range of initial LLO inclinations. Then, a feedback Lambert-based impulsive guidance algorithm is designed and tested through a Monte Carlo campaign to assess the effectiveness under non-nominal conditions related to injection and actuation errors. Because the last braking maneuver is relatively large, a finite-thrust, locally flat, near-optimal guidance is introduced and applied. Simplified dynamics is assumed for the purpose of defining a minimum-time optimal control problem along the last thrust arc. This admits a closed-form solution, which is iteratively used until the desired instantaneous hovering condition is reached. The numerical results in non-nominal flight conditions testify to the effectiveness of the guidance approach at hand in terms of propellant consumption and precision at landing. Full article

This study proposes a frequency-based finite element updating method for an effective physics-based digital twin (DT). One approach to constructing a physics-based DT is to develop a mechanics-based mathematical model that accurately simulates the behavior of an actual structure. The proposed method utilizes finite element updating, adjusting model parameters to improve model accuracy. Unlike simple modal analysis, which focuses on vibration characteristics, this method recognizes that accurate dynamic transient-based vibration analysis requires considering the damping effect, as well as mass and stiffness, during the updating process. Moreover, a frequency-based analysis is employed instead of the computationally expensive time-based analysis for more efficient dynamic modeling. By transforming data into the frequency domain, the method efficiently represents dynamic behavior within relevant frequency ranges. We further enhance the computational efficiency using the model reduction technique. To validate the method’s accuracy and efficiency, we compare the analysis results and computation time using a numerical example of the control rod drive mechanism. The proposed method shows significantly reduced computation time, by a factor of 8.9 compared to conventional time-based methods, while preserving high accuracy. Therefore, the proposed method can effectively support the development of physics-based DTs. Full article

The multi-objective path planning and robust continuous path-following method for the autonomous marine surface vehicle (AMSV) is employed. By incorporating the position and direction constraints into the optimization cost function, the spiral path planner obtains a continuous path with smooth path tangency and curvature and ensures strict adherence to the desired multi-objective points. An improved A* and optimization algorithm are combined with the global path planning to avoid obstacles in real-time. For the path-following controller, the unknown sideslip angle and uncertainties are added to build the system model, based on which observation technique is adopted to estimate all the uncertainties online. Based on the kinematic system, a finite time extended state observer (ESO) is put forward to estimate the sideslip angle accurately. The nonlinear line-of-sight (LOS) guidance scheme is designed for the model, effectively compensating for the observed values and achieving convergence in a finite time. The finite-time ESO is adopted to estimate the uncertainty for the surge and heading controller design, and the terminal sliding mode technique is introduced to achieve the final finite-time convergence. Through extensive experiments, the proposed approach demonstrates its effectiveness, feasibility, and the advantage of fast convergence and accurate control. Full article

Soft robots with compliant bodies offer safe human–robot interaction as well as adaptability to unstructured dynamic environments. However, the nonlinear dynamics of a soft robot with infinite motion freedom pose various challenges to operation and control engineering. This research explores the motion of a pneumatic soft robot under diverse loading conditions by conducting finite element analysis (FEA) and using machine learning. The pneumatic soft robot consists of two parallel hyper-elastic tubular chambers that convert pneumatic pressure inputs into soft robot motion to mimic an elephant trunk and its motion. The body of each pneumatic chamber consists of a series of bellows to effectively facilitate the expansion, contraction, and bending of the body. The first chamber spans the entire length of the soft robot’s body, and the second chamber spans half of it. This unique asymmetric design enables the soft robot to bend and curl in various ways. Machine learning is used to establish a forward kinematic relationship between the pressure inputs and the motion responses of the soft robot using data from FEA. Accordingly, this research employs an artificial neural network that is trained on FEA data to estimate the reachable workspace of the soft robot for given pressure inputs. The trained neural network demonstrates promising estimation accuracy with an R-squared value of 0.99 and a root mean square error of 0.783. The workspaces of asymmetric double-chamber and single-chamber soft robots were compared, revealing that the double-chamber robot offers approximately 185 times more reachable workspace than the single-chamber soft robot. Full article

Optimal control theory aims to find an optimal protocol to steer a system between assigned boundary conditions while minimizing a given cost functional in finite time. Equations arising from these types of problems are often non-linear and difficult to solve numerically. In this article, we describe numerical methods of integration for two partial differential equations that commonly arise in optimal control theory: the Fokker–Planck equation driven by a mechanical potential for which we use the Girsanov theorem; and the Hamilton–Jacobi–Bellman, or dynamic programming, equation for which we find the gradient of its solution using the Bismut–Elworthy–Li formula. The computation of the gradient is necessary to specify the optimal protocol. Finally, we give an example application of the numerical techniques to solving an optimal control problem without spacial discretization using machine learning. Full article

This paper delves into the exploration of the indefinite linear quadratic optimal control (LQOC) problem for discrete-time stochastic singular systems driven by discrete-time Markov chains. Initially, the conversion of the indefinite LQOC problem mentioned above for stochastic singular systems into an equivalent problem of normal stochastic systems is executed through a sequence of transformations. Following this, the paper furnishes sufficient and necessary conditions for resolving the transformed LQOC problem with indefinite matrix parameters, alongside optimal control strategies ensuring system regularity and causality, thereby establishing the solvability of the optimal controller. Additionally, conditions are derived to verify the definiteness of the transformed LQOC problem and the uniqueness of solutions for the generalized Markov jumping algebraic Riccati equation (GMJARE). The study attains optimal controls and nonnegative cost values, guaranteeing system admissibility. The results of the finite horizon are extended to the infinite horizon. Furthermore, it introduces the design of an output feedback controller using the LMI method. Finally, a demonstrative example demonstrates the validity of the main findings. Full article

This article focuses on the problem of finite-time and fixed-time synchronization for Cohen–Grossberg neural networks (CGNNs) with time-varying delays and memristor connection weights. First, through a nonlinear transformation, an alternative system is derived from the Cohen–Grossberg memristor-based neural networks (MCGNNs) considered. Then, under the framework of the Filippov solution and by adjusting a key control parameter, some novel and effective criteria are obtained to ensure finite-time or fixed-time synchronization of the alternative networks via the unified control framework and under the same conditions. Furthermore, the two types of synchronization criteria are derived from the considered MCGNNs. Finally, some numerical simulations are presented to test the validity of these theoretical conclusions. Full article

Through-transmission pulsed thermography (PT) is an effective non-destructive testing (NDT) technique for assessing material thermal diffusivity. However, the current literature indicates that the technique has lagged behind the reflection mode in terms of technique development despite it offering better defect resolution and the detection of deeper subsurface defects. Existing thermal diffusivity measurement systems require costly setups, including temperature-controlled chambers, multiple calibrations, and strict sample size requirements. This study presents a simple and repeatable methodology for determining thermal diffusivity in a laboratory setting using the through-transmission approach by incorporating both finite element analysis (FEA) and laboratory experiments. A full-factorial design of experiments (DOE) was implemented to determine the optimum flash energy and sample thickness for a reliable estimation of thermal diffusivity. The thermal diffusivity is estimated using the already established Parker’s half-rise equation and the recently developed new least squares fitting (NLSF) algorithm. The latter not only estimates thermal diffusivity but also provides estimates for the input flash energy, reflection coefficient, and the time delay in data capture following the flash event. The results show that the NLSF is less susceptible to noise and offers more repeatable values for thermal diffusivity measurements compared to Parker, thereby establishing it as a more efficient and reliable technique. Full article

For multi-input multi-output (MIMO) nonlinear discrete-time bipartite formation multiagent systems (BFMASs) performing trajectory tracking tasks with unknown dynamics, a dynamic event-triggered model-free adaptive iterative learning control (DET-MFAILC) algorithm is proposed to address periodic denial-of-service (DoS) attacks. First, using the pseudo-partial derivative, a compact format dynamic linearization (CFDL) method is employed to construct an equivalent CFDL data model for the MIMO multi-agent system. A DoS attack model and its corresponding compensation algorithm are developed, while a dynamic event-triggered condition is designed considering both the consensus error and the tracking error. Subsequently, the proposed DoS attack compensation algorithm and the dynamic event-triggered mechanism are integrated with the model-free adaptive iterative learning control algorithm to design a controller, which is further extended from fixed-topology systems to time-varying topology systems. The convergence of the control system is rigorously proven. Finally, simulation experiments are conducted on bipartite formation multi-agent systems (BFMASs) under fixed and time-varying communication topologies. The results demonstrate that the proposed algorithm effectively mitigates the impact of DoS attacks, reduces controller updates, conserves network resources, and ensures that both the tracking error and consensus error converge to an ideal range close to zero within a finite number of iterations while maintaining a good formation shape. Full article

This paper presents a passivity-based twisting sliding mode control (PBSMC) approach for series elastic actuators (SEAs). To address the time-varying position trajectory tracking control problem in SEAs, a fourth-order dynamic model is developed to accurately characterize the system. The control framework comprises an internal loop and an external loop controller, each designed to ensure precise trajectory tracking. The internal loop controller manages the second derivative of the joint trajectory position error, while the external loop focuses on the error itself. Both controllers are based on the PBSMC methodology to reduce complex nonlinear disturbances and minimize tracking errors. The finite-time convergence of the proposed method is rigorously analyzed. The performance and advantages of the method are evaluated and compared through various simulations. Full article

Additive manufacturing, such as the 3D printing of composite materials for electronics is rapidly evolving, enabling the production of advanced electric and magnetic composites with tailored properties. These materials require special printing conditions and advanced control to maintain the desired material properties during the 3D printing process and in the final product design. Hence, determining the heating and energy consumption and estimating the efficiency of 3D printing is essential. This work modeled the fused filament fabrication 3D printing of composite materials with a polymer carrier matrix. A 3D time-dependent thermal model of a 3D printer extruder was developed and implemented using the finite element method to study and improve the efficiency of 3D printing. As the filler content influences the operational parameters and process energy consumption of the 3D printing process, the transient heating process parameters were estimated using different composite modifier contents. Two types of modifiers were considered: Fe₂O₃ and CaO, both mixed in a PLA carrier material. The volumetric fill ratio of the two modifiers did not exceed 45%, as the mixing dependency of the material properties is linear in this range. The power fluxes and power efficiency were estimated. The results provide new possibilities for better control methodologies and advanced additive manufacturing for new materials in electronics. Operational control can accelerate the 3D printing process, speeding up the heating of 3D-printed composite materials and reducing the printing time and total energy consumption. Furthermore, this research provides directions for new advanced 3D printing extruder designs with better power and energy heating efficiency. Full article