Underwater Vehicle System Identification Using Laser Light, Decoupled Dynamics and Evolutionary Algorithm

Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

We present a new parameterization of quintessence potentials for dark energy based directly upon the dynamical properties of the equations of motion. Such parameterization arises naturally once the equations of motion are written as a dynamical system in terms of properly defined polar variables. We have identified two different classes of parameters, and we dubbed them as dynamical and passive parameters. The dynamical parameters appear explicitly in the equations of motion, but the passive parameters play just a secondary role in their solutions. The new approach is applied to the so-called thawing potentials and it is argued that only three dynamical parameters are sufficient to capture the evolution of the quintessence fields at late times. This work reconfirms the arbitrariness of the quintessence potentials as the recent observational data fail to constrain the dynamical parameters.

A model-based fault diagnosis in a nonlinear bioreactor using an inverse problem approach and evolutionary algorithms

The characterization and monitoring of buildings is an issue that has attracted the interest of many sectors over the last two decades. With the increasing use of permanent, continuous and real-time networks, ambient vibrations can provide a simple tool for the identification of dynamic building parameters. This study is focused on the long-term variation of frequency and damping in several buildings, using the Random Decrement Technique (RDT). RDT provides a fast, robust and accurate long-term analysis and improves the reliability of frequency and damping measurements for structural health monitoring. This reveals particularly useful information in finding out precisely how far changes in modal parameters can be related to changes in physical properties. This paper highlights the reversible changes of the structure's dynamic parameters, correlated with external forces, such as temperature and exposure to the sun. Contrasting behaviors are observed, including correlation and anti-correlation with temperature variations.

A three-degree-of-freedom flight simulator platform with a translational and two rotational movement capabilities is studied. Based on the Lagrange method the dynamic model is established and then simplified by eliminating the sub-diagonal elements of the matrixes. A washout algorithm is employed to qualify the platform for flight simulation in limited space. In order to improve the modeling and experimental accuracies further when some dynamic parameters of an actual platform are unknown, the stepwise identification method is proposed to solve this problem. By dividing the driving torque into the load torque, reducer driving torque, screw driving toque and integrated friction torque, and main dynamic parameters are identified by applying proper trajectories and estimated by the weighed least square method. The procedures of identification are verified through comparing the driving forces from the identified model and those of servo motor encoders.

An evolutionary search strategy utilizing two normal distributions to generate children is presented. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (μ+μ) evolutionary strategies but with fewer parameters to adjust. Extensive tests regarding the sensitivity of BCB parameters to performance are provided. The test suite includes continuous variable constrained hub design problems, mixed discrete and continuous variable constrained hub design problems, and an unconstrained highly multimodal discrete optimization problem.

Soft robots present significant modelling challenges due to their non-linearity, complex dynamics and potentially intricate geometries. These difficulties in accurate system identification and dynamics modelling limit their applications in precise robotics tasks. Prior modelling approaches typically suffer from trade-offs in accuracy, computational efficiency, or speed. The differentiable finite element method (FEM) offers a promising balance between these desiderata for soft robot modelling, and enables the use of gradients for efficient calibration and trajectory optimisation. In this paper, we propose an end-to-end differentiable FEM-based framework designed to streamline modelling and system identification for pneumatic soft robots, enabling the generation of reliable models for downstream tasks. The framework automates the conversion of computer-aided designs into voxelised tetrahedral viscoelastic FEM meshes that accurately represent the robot's geometry and actuation mechanism. By integrating easily acquired point cloud data with differentiable FEM, the framework achieves precise identification of material and dynamic actuation parameters using a minimal experimental setup. Additionally, the differentiable nature of the model facilitates trajectory optimisation by leveraging gradients from robot dynamics and contact interactions. We validate the framework by modelling a complex bellow-shaped pneumatic soft robot and demonstrate its efficacy in real-world motion planning tasks. Experimental results indicate high modelling accuracy, with a maximum positional error of less than 3 mm, and successful application in tasks such as path following and grasping.

The Dynamic FEM Models Results Comparison of the New Bridge Over Danube River in Bratislava

In the paper, evolutionary optimization strategy, represented by the genetic algorithm (GA) is considered as a multiuser detection (MUD) method for a multiple-input multiple-output (MIMO) wireless system. With the aim to boost lacking GA convergence, Zero-Forcing (ZF) detection is proposed as an initial processing phase. Additionally, a multi-stage GA routine is considered as a method to make the search for data estimates more effective.

This study investigates the optimization of a two-degree-of-freedom PID (2DOF-PID) controller using the Ant Lion Optimizer (ALO) for enhanced frequency regulation in a hybrid Photovoltaic (PV)–Reheat Thermal Power System under sudden load disturbances. To benchmark performance, conventional PID and PI controllers were also tested to assess their ability to minimize the frequency fluctuations and manage the inter-area power exchanges. Simulations were carried out in MATLAB/Simulink using a dual-area dynamic model, and the performance was evaluated based on standard control metrics, including the settling time, overshoot/undershoot, and the Integral of Absolute Error (IAE). The results indicate that the ALO-optimized 2DOF-PID controller reduced the average settling time by nearly 40% and IAE by 12.6%, compared to the best-performing PID controller in the benchmark group. These findings highlight the advantages of combining the 2DOF-PID architecture with an evolutionary optimization strategy, offering improved dynamic and nonlinear control performance. The proposed approach shows strong potential for application in next-generation renewable energy systems and smart grids.

To configure the indirect hot stamping process, a finite-element-based prediction of the parts geometry and mechanical properties is required. In case of indirect hot stamping, inhomogeneous cooling schedules cause different phase transformation points and products. The volume expansion caused by phase transformation of fcc into bcc leads to transformation induced stresses that are important for the calculation of overall stresses in press hardened components. To calculate theses stresses correctly, it is necessary to study the kinetics of phase transformation in consideration of the cooling path of an indirect hot stamping process. Dilatometer tests are employed to obtain the kinetics of phase transformation is determined in dilatometer tests. These results are used to identify the parameters for the phase transformation models implemented in the material model *MAT_244 [ that is implemented in the finite-element-code LS-DYNA [. In this context the material model parameters are identified by using evolutionary optimization strategies. Based on the identified parameters the predictive quality of the implemented phase transformation models will be studied in order to improve their prediction accuracy for the indirect hot stamping process.

Dynamic models play a critical role in the design of model-based controllers, which has a significant effect on the dynamic characteristics of motion equipment. This paper mainly focuses on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, with which a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

The flow field surrounding an underwater vehicle will change due to the influence from seabed terrains, when it navigates near seabed. The hydrodynamic coefficients of the underwater vehicle will change, and then produce a force and pitch moment, which could pull the underwater vehicle to the seabed. So it's very important for the safety of underwater vehicles while navigating near seabed to predict the motion and study the manoeuvring rules. In this paper, a 3DVR simulation system is developed to provide an intuitionistic, real-time, reality 3D GUI tools for researching the manoeuvring motion and rules of underwater vehicles navigating near the seabed and in infinite water. The slide controllers for pitch and heave motion are designed. Several terrains are investigated to obtain the different control method. The simulations show that these two slide controllers are robust, and can adapt to the variety of the hydrodynamic coefficients of underwater vehicles. With the controllers, the underwater vehicle can trace seabed terrains maneuverably. And the further research object is also presented.

Dynamics simulation of parallel kinematic manipulators (PKM) and non-linear control methods require a precisely identified dynamics model and explicit generalized mass matrix. Standard methods, which identify so-called dynamic base-parameters, are not sufficient to this end. Algorithms for identifying the complete set of dynamic parameters were proposed for serial manipulators. A dedicated identification method for PKM does not exist, however. Such a method is introduced here for the large class of Delta-like PKM exploiting the parallel structure and making use of model simplifications specific to this class. The proposed method guarantees physical consistency of the identified parameters, and in particular a positive definite generalized mass matrix. The method is applied to a simulated model with exactly known parameters, which allows for verification of the obtained dynamic parameters. The results show that the generalized mass matrix, the acceleration, and the Coriolis, gravitation and friction terms in the equations of motion (EOM) are well approximated. The second example is a real 4-DOF industrial Delta robot ABB IRB 360-6/1600. For this robot, a physically consistent set of inertia and friction parameters is identified from measurements. The method allows prescribing estimated parameters, but does not rely on such data, e.g. from manufacturer or CAD.

This paper proposes a hybrid evolutionary algorithm to solve the maintenance-scheduling problem for thermal generating units. The proposed approach uses a hybrid Fuzzy-Genetic Algorithm that implements Fuzzy Knowledge Based System to emulate the power plant personnel's experience, and uncertainties in the constraints, while a Genetic Algorithm optimizes the total generating cost and the maintenance cost as the objective functions. Two other effective and practical methods based on Evolution Strategy and Particle Swarm Optimization were also applied for the same problem. Simulations were carried out on a practical thermal power plant consisting of 19 generating units, over a six-month planning horizon.