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The use of additive manufacturing in many branches of industry is increasing significantly because of its many advantages, such as being able to produce complex parts that cannot be produced by classical methods, using fewer materials, easing the supply chain with on-site production, being able to produce with all kinds of materials and producing lighter parts. The binder jetting technique, one of the additive manufacturing methods researched within the scope of this work, is predicted to be the additive manufacturing method that will grow the most in the next decade, according to many economic reports. Although additive manufacturing methods have many advantages, they can be slower than classical manufacturing methods regarding production speed. For this reason, this study aims to increase the manufacturing speed in the binder jetting method.

Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical.

As a result of the tests, the optimum printing condition was decided to be 180–250 µm for layer thickness and 50% for saturation. A separate test sample was then designed to implement adaptive slicing. This test sample was produced in three pieces: adaptive (180–250 µm), thin layer (180 µm) and thick layer (250 µm) with the determined parameters. The roughness values of the adaptive sliced sample and the thin layer sample were similar and better than the thick layer sample. A similar result was obtained using 12.31% fewer layers in the adaptive sample than in the thin layer sample.

The use of adaptive slicing in binder jetting has become more efficient. In this way, it will increase the use of adaptive slicing in binder jetting. In addition, a cheap and straightforward image processing method has been developed to calculate the surface roughness of the parts.

This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

The purpose of this paper is to propose an accurate and practical imitation learning for robotics. The modified dynamic movement primitives (DMPs), global fitting DMPs (GLDMPs), is presented. Framework design, theoretical derivation and stability proof of GLDMPs are discussed in the paper.

Based on the DMPs, the hierarchical iterative parameter adaptive framework is developed as the hierarchical iteration stage of the GLDMPs to tune the designed parameters adaptively to extract richer features. Inspired by spatial transformations, the coupling analytical module which can be regarded as a reversible transformation is proposed to analyze the high-dimensional coupling information and transfer it to trajectory.

With the proposed framework and module, DMPs derive majority features of the demonstration and cope with three-dimensional rotations. Moreover, GLDMPs achieve favorable performance without specialized knowledge. The modified method has been demonstrated to be stable and convergent through inference.

GLDMPs have an advantage in accuracy, adaptability and practicality for it is capable of adaptively computing parameters to extract richer features and handling variations in coupling information. With demonstration and simple parameter settings, GLDMPs can exhibit excellent and stable performance, accomplish learning and generalize in other regions. The proposed framework and module in the paper are useful for imitation learning in robotics and could be intuitive for similar imitation learning methods.

This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

Asteroids have the characteristics of noncooperative, irregular gravity and complex terrain on the surface, which cause difficulties in successful landing for conventional landers. The purpose of this paper is to study the trajectory tracking problem of a multi-node flexible lander with unknown flexible coefficient and space disturbance.

To facilitate the stability analysis, this paper constructs a simplified dynamic model of the multi-node flexible lander. By introducing the nonlinear transformation, a concurrent learning-based adaptive trajectory tracking guidance law is designed to ensure tracking performance, which uses both real-time information and historical data to estimate the parameters without persistent excitation (PE) conditions. A data selection algorithm is developed to enhance the richness of historical data, which can improve the convergence rate of the parameter estimation and the guidance performance.

Finally, Lyapunov stability theory is used to prove that the unknown parameters can converge to their actual value and, meanwhile, the closed-loop system is stable. The effectiveness of the proposed algorithm is further verified through simulations.

This paper provides a new design idea for future asteroid landers, and a trajectory tracking controller based on concurrent learning and preset performance is first proposed.

The Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system.

The idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively.

From the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system.

Compared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system.

Based on the general formula, which depends on the length and difficulty of the test, the number of respondents and the number of ability levels, this study aims to provide a closed formula for the adaptive tests with medium difficulty (probability of solution is p = 1/2) to determine the accuracy of the parameters for each item and in the case of calibrated items, determine the required test length given number of respondents.

Empirical results have been obtained on computerized or multistage adaptive implementation. Simulation studies and classroom/experimental results show that adaptive tests can measure test subjects’ ability to the same quality over half the test length compared to linear versions. Due to the complexity of the problem, the authors discuss a closed mathematical formula: the relationship between the length of the tests, the difficulty of solving the items, the number of respondents and the levels of ability.

The authors present a closed formula that provides a lower bound for the minimum test length in the case of adaptive tests. The authors also present example calculations using the formula, based on the assessment framework of some student assessments to show the similarity between the theoretical calculations and the empirical results.

With this formula, we can form a connection between theoretical and simulation results.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

This paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.

The authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.

Numerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.

The paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.