A Novel Kinematic Calibration Method for Industrial Robots Based on the Improved Grey Wolf Optimization Algorithm

The grey wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. The GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. This paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. The Levy flight strategy is used to strengthen the efficiency of global search. The adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. The comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms.

The present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

This paper presents an automated calibration method for industrial robots, based on the use of (1) a novel, low-cost, wireless, 3D measuring device mounted on the robot end-effector and (2) a portable 3D ball artifact fixed with respect to the robot base. The new device, called TriCal, is essentially a fixture holding three digital indicators (plunger style), the axes of which are orthogonal and intersect at one point, considered to be the robot tool center point (TCP). The artifact contains four 1-inch datum balls, each mounted on a stem, with precisely known relative positions measured on a Coordinate Measuring Machine (CMM). The measurement procedure with the TriCal is fully automated and consists of the robot moving its end-effector in such as a way as to perfectly align its TCP with the center of each of the four datum balls, with multiple end-effector orientations. The calibration method and hardware were tested on a six-axis industrial robot (KUKA KR6 R700 sixx). The calibration model included all kinematic and joint stiffness parameters, which were identified using the least-squares method. The efficiency of the new calibration system was validated by measuring the accuracy of the robot after calibration in 500 nearly random end-effector poses using a laser tracker. The same validation was performed after the robot was calibrated using measurements from the laser tracker only. Results show that both measurement methods lead to similar accuracy improvements, with the TriCal yielding maximum position errors of 0.624 mm and mean position errors of 0.326 mm.

A six-axis force sensor calibration method for industrial robots is proposed to meet the demand for precise force measurement of end effectors in industrial robots. Firstly, establish a sensor data relationship calculation model, and based on this, focus on considering the interference caused by sensor "zero drift"; Then, the least squares method is used to determine the inclination angle between the robot's end effector and the installation of the six-axis force sensor, the gravity of the end effector, and the coordinate parameters of the center of gravity. Finally, a CR5 robotic arm experimental platform was built to verify the feasibility of the algorithm. The experiment showed that the calibrated sensor maintained a stable force value of 0N without external force, verifying the accuracy of the calibration algorithm.

Radial Basis Function (RBF) neural networks have strong engineering applications. The training of these networks however can be time consuming. In this paper, we examine the calibration of a MOTOMAN industrial robot using an RBF based network. Additionally we examine the acceleration of RBF network using general purpose graphical processing units (GPGPUs). On a data set of 1989 calibration points, we are able to achieve a speedup of over 300 times compared to a MATLAB simulation of the algorithm. Given that MATLAB's simulation required over a week of runtime, the GPGPU acceleration enables reasonable training time for datasets with more calibration points, thus providing better precision.

One of the objectives of electrical distribution networks is to provide customers with access to high‐quality electricity. Because any disruptions in these systems result in voltage disorders, different devices are employed to offset these disruptions on consumers who are more susceptible. One of the most important and contemporary pieces of equipment that is connected in series with the network is dynamic voltage restoration (DVR), which shields delicate loads from network voltage issues by injecting the proper voltage. This article presents a DVR control scheme optimized with improved grey wolf optimization (IGWO) that uses a proportional derivative (PD) controller and adaptive notch filter (ANF). The output LC filter’s resistance has been removed, and the control system has actively engaged in oscillation damping in order to accelerate dynamic responsiveness and lower system losses. The major component of the voltage, which comprises its frequency, amplitude, and phase, is extracted using ANF. The capacitor current of the output filter in this structure is fed back to the control system and from the current mode control in the inner loop to boost stability. Owing to the occasionally complex dynamic behavior in distribution networks, particularly during a fault, the system’s frequency response has been altered and response speed has been accelerated using the PD controller. This kind of controller is distinguished by its accurate functioning in the presence of frequency deviations and its swifter dynamic reaction in the face of voltage swell and sag. In order to improve the THD and voltage sag indicators of the sensitive load, the PD coefficients were adjusted using the IGWO algorithm. As a consequence, the simulation results demonstrated that the suggested controller performed better than traditional controllers.

To overcome the limitations of low hydrogen production efficiency in conventional control approaches for proton exchange membrane (PEM) electrolysis systems, a novel multivariable model predictive control (MPC) strategy integrated with an improved gray wolf optimization (IGWO) algorithm is proposed in this paper. First, a comprehensive mathematical model of the PEM electrolysis system is established, incorporating a dynamic coupling model that captures the complex interactions among current density, operating temperature, system pressure, and hydrogen production efficiency. Second, the nonlinear efficiency model is linearized through a robust weighted least squares approach, incorporating operational constraints for temperature and pressure. This linearized framework facilitates the development of a state-space model with current density, temperature, and pressure as manipulated variables, targeting hydrogen production efficiency as the controlled output. Third, the optimization problem of hydrogen production efficiency is transformed into a quadratic programming problem through the MPC framework enhancement. An upgraded gray wolf optimization algorithm featuring a dynamic convergence factor adjustment mechanism that improves computational efficiency of weight matrix optimization in MPC is proposed. The integration of the IGWO algorithm with the MPC framework establishes a dual-loop collaborative control architecture (IGWO-MPC) that synergistically combines predictive modeling with intelligent optimization. Finally, the performance improvements are validated by simulation experiments. Compared to conventional proportional-integral-derivative control, the IGWO-MPC architecture achieves 4.6% higher hydrogen production power, 5.3% reduction in thermal power loss, 5.3% efficiency enhancement over 24 h, and a 6.7% increase in daily hydrogen yield. When benchmarked against traditional MPC, IGWO-MPC maintains superior performance with 1.9% greater hydrogen production power, 2.1% lower thermal losses, 2.4% efficiency improvement, and 4.89% higher daily output.

An energy-efficient multi-objective scheduling for flexible job-shop-type remanufacturing system

A weighing error compensation method using IGWO–LMBPNN, which combines an improved gray wolf optimization algorithm (IGWO) with the Levenberg–Marquardt backpropagation neural network (LMBPNN), is proposed to enhance truck scale accuracy. It uses output signals from multiple load cells as inputs to the neural network for establishing the error compensation model. The IGWO algorithm is used to initialize the weights and biases of the model, while the LMBPNN algorithm further optimizes these parameters. To evaluate the effectiveness of the IGWO–LMBPNN method, it was compared with the existing compensation methods, including BPNN, LMBPNN, gray wolf optimization with LMBPNN (GWO–LMBPNN), particle swarm optimization with LMBPNN (PSO–LMBPNN), and Fine Gaussian SVM. The experimental results demonstrate a significant improvement in error reduction for truck scale weighing. The mean absolute error (MAE) of IGWO–LMBPNN is 8.5 kg, which was reduced by 38.8%, 29.2%, 19%, 21.3%, and 33.1% compared with BPNN, LMBPNN, GWO–LMBPNN, PSO–LMBPNN, and Fine Gaussian SVM, respectively. These findings not only verify the efficacy of the IGWO–LMBPNN in enhancing weighing accuracy but also highlight its potential for other practical applications.

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

A grey wolf optimization algorithm based on an adaptive adjustment strategy (Improvements-Grey Wolf Optimization, IGWO) is proposed in this paper to address the issues with the Grey Wolf Optimization (GWO) algorithm, which has a slow convergence speed in the later stages and where the local search and global search cannot be taken into effective balance. First, a chaotic logistic map is used to initialize the population. Next, an inverse trigonometric function-based mathematical model is developed to achieve convergence purposes. Finally, a new location update method is used to update the next-generation location to improve search traversability. Additionally, the CEC2013-Benchmark standard test function was used to evaluate the benefits of performance compared to the standard GWO, PSO, and DEGWO (DE-Grey wolf optimization) algorithms. The improved GWO algorithm has greatly increased the convergence speed, search capability, and stability. It performs better than the other three algorithms as well.

This research article thoroughly examines the implementation of an enhanced Grey Wolf Optimization (GWO) method for Multi-Objective Power Flow Optimization (MO-PFO) in the IEEE-118 bus test system. Given the growing complexity and uncertainty in modern power systems, power flow optimization presents a crucial challenge. The suggested enhanced GWO algorithm improves the performance of the traditional GWO for MO-PFO issues while addressing its inherent flaws.
 The goal of this study is to obtain optimal solutions concurrently for multiple objectives, including the reduction of power losses, voltage variations, and the enhancement of system stability. In the Related Works section, I review the research on power flow optimization in the literature, which also showcases the improvements achieved by applying various optimization techniques and discusses the benefits and drawbacks of traditional optimization methods and their effectiveness in dealing with MO-PFO issues.
 The improved GWO algorithm, designed specifically for MO-PFO, is described in detail in the Methodology section. I explain the adjustments made to the regular GWO algorithm and introduce a cutting-edge method for handling multiple objectives. In the IEEE-118 bus test system, I present the mathematical model of the MO-PFO problem and develop fitness functions for each objective.
 Furthermore, I describe the implementation of the enhanced GWO algorithm, including parameter settings and termination criteria. The proposed algorithm demonstrates its capability to effectively handle the multi-objective nature of power flow optimization, thereby paving the way for future power system operations that are more dependable and sustainable

This study presents an innovative method for predicting coal water supply pipeline leakage aperture sizes. Firstly, an Improved Grey Wolf Optimization (IGWO) algorithm is introduced, which combines Bernoulli chaotic initialization, elliptical convergence parameters, and dynamic weight updates. The algorithm’s optimization capabilities are enhanced by IGWO. Secondly, the effectiveness of IGWO is validated through benchmark tests. Finally, IGWO is applied to optimize the initial structural parameters of a Backpropagation (BP) neural network. The effectiveness of IGWO-BP is verified using laboratory pipeline infrasound leakage signals. The optimized IGWO-BP model demonstrates superior performance in predicting leakage apertures, with a substantial 69.12% reduction in mean absolute percentage error (MAPE) and a 66.96% decrease in mean square error (MSE) compared to traditional BP models, offering valuable insights for future leak remediation efforts.

Improved DV-HOP localization algorithm based on grey wolf optimization

Unmanned combat system is one of the important means to capture information superiority, carry out precision strike and accomplish special combat tasks in information war. Unmanned attack strategy plays a crucial role in unmanned combat system, which has to ensure the attack by unmanned surface vehicles (USVs) from failure. To meet the challenge, we propose a task allocation algorithm called distributed auction mechanism task allocation with grey wolf optimization (DAGWO). The traditional grey wolf optimization (GWO) algorithm is improved with a distributed auction mechanism (DAM) to constrain the initialization of wolves, which improves the optimization process according to the actual situation. In addition, one unmanned aerial vehicle (UAV) is employed as the central control system to establish task allocation model and construct fitness function for the multiple constraints of US V attack problem. The proposed DAGWO algorithm can not only ensure the diversity of wolves, but also avoid the local optimum problem. Simulation results show that the proposed DAGWO algorithm can effectively solve the problem of attack task allocation among multiple USVs.