Parameter Adaptive Control Research Articles

Voltage stability control strategy for DC microgrid based on adaptive virtual DC motor

The large-scale integration of distributed energy sources and power electronic devices results in the DC microgrid exhibiting significant low inertia and weak damping characteristics. This, in turn, leads to inevitable fluctuations in the DC bus voltage, which endanger the stable operation of the DC system. Energy storage devices can provide equivalent inertia. To enhance the inertia and response speed of the DC bus interface converter, this paper proposes a power allocation parameter adaptive virtual DC motor control strategy based on a hybrid energy storage unit. The strategy introduces power allocation control to regulate the energy storage converter on the basis of virtual inertia parameter adaptive control, thereby enabling the energy storage converter to simulate the inertia and damping characteristics of a DC generator. The small-signal stability of the system is analyzed by establishing a small-signal model of the photovoltaic energy storage system and utilizing the impedance ratio criterion. Finally, the proposed control strategy is validated through simulation. The results demonstrate that the strategy effectively mitigates the fluctuations in bus voltage under varying photovoltaic power and sudden load changes, ensures the power distribution in the hybrid energy storage system, and enhances the dynamic response of the system.

Control parameter optimisation using the evidence framework for the ant colony optimisation algorithm

The ant colony optimization (ACO) algorithm is a metaheuristic initially designed to solve the travelling salesman problem (TSP). The design of experiments, finding the suitable ACO algorithm configuration, and calibrating the adaptive control parameters are exhaustive and time-consuming exercises, especially for TSPs where the number of cities can exceed 1000. This paper presents an evidence framework driven control parameter optimisation (EFCPO) algorithm for an ACO algorithm solving TSPs. EFCPO performs auto-tuning of the adaptive control parameters and makes recommendations about the ACO algorithms that are best suited for the TSPs in question using the log evidence. In addition, with this ability, the algorithm can take a solution provided by an ACO algorithm and improve the results. The EFCPO accomplishes this over a number of cycles through auto-tuning of the control parameters and re-running the ACO until the process is completed. The capabilities of EFCPO are compared to another configuration tool, irace, using benchmark ACO algorithms to test the efficiency of the framework. The benchmark algorithms make use of a local search strategy to solve TSPs. The results show that ACO algorithms are able to find new and improved solution tours within reasonable times. The improvements are also significant. In addition, ACO algorithms that are best suited for the TSP in question are preferred, making the EFCPO an effective tool for real-time configuration of ACO algorithms for solving TSPs .

Parameter adaptive stochastic model predictive control for wind–solar–hydrogen coupled power system

With the increasing global energy scarcity and environmental concerns, the wind–solar–hydrogen (WSH) coupled system has garnered widespread attention as an efficient and eco-friendly renewable energy solution. Addressing the impact of uncertainty on the source and load of the coupled system concerning its power balance, a parameter adaptive stochastic model predictive control (PAS-MPC) power regulation strategy based on the scenario analysis method is proposed herein. In order to characterize the probabilistic information about the uncertainty on both sides of the system, scenario generation and reduction techniques are used to obtain classical scenarios of wind and solar outputs as well as electrical loads as inputs to PAS-MPC. With the aim of optimizing the computational time constant of SMPC, the parameter adaptive method is proposed to change the prediction time domain and control time domain of the system. Simulation results demonstrate the effectiveness of PAS-MPC in addressing uncertainties on the source and load sides of the WSH coupled system. Optimization results reveal that PAS-MPC considerably improves the system power balance compared to SMPC. Compared to the conventional model predictive control (MPC), PAS-MPC effectively mitigates high-power state of the controllable equipment of the system, thereby enhancing its lifecycle.

Microstimulation-based path tracking control of pigeon robots through parameter adaptive strategy

Research on animal robots utilizing neural electrical stimulation is a significant focus within the field of neuro-control, though precise behavior control remains challenging. This study proposes a parameter-adaptive strategy to achieve accurate path tracking. First, the mapping relationship between neural electrical stimulation parameters and corresponding behavioral responses is comprehensively quantified. Next, adjustment rules related to the parameter-adaptive control strategy are established to dynamically generate different stimulation patterns. A parameter-adaptive path tracking control strategy (PAPTCS), based on fuzzy control principles, is designed for the precise path tracking tasks of pigeon robots in open environments. The results indicate that altering stimulation parameter levels significantly affects turning angles, with higher UPN and PTN inducing changes in the pigeons' motion state. In experimental scenarios, the average control efficiency of this system was 82.165%. This study provides a reference method for the precise control of pigeon robot behavior, contributing to research on accurate target path tracking.

Adaptive Control for Underwater Simultaneous Lightwave Information and Power Transfer: A Hierarchical Deep-Reinforcement Approach

In this work, we consider a point-to-point underwater optical wireless communication scenario where an underwater sensor (US) transmits its sensing data to a remotely operated vehicle (ROV). Before the US transmits its data to the ROV, the ROV performs simultaneous lightwave information and power transfer (SLIPT), delivering both control data and lightwave power to the US. Under the considered scenario, our objective is to maximize energy harvesting at the US while supporting predetermined communication performance between the two nodes. To achieve this objective, we develop a hierarchical deep Q-network (DQN)–deep deterministic policy gradient (DDPG)-based online algorithm. This algorithm involves two reinforcement learning agents: the ROV and US. The role of the ROV agent is to determine an optimal beam-divergence angle that maximizes the received optical signal power at the US while ensuring a seamless optical link. Meanwhile, the US agent, which is influenced by the decision of the ROV agent, is responsible for determining the time-switching and power-splitting ratios to maximize energy harvesting without compromising the required communication performance. Unlike existing studies that do not account for adaptive parameter control in underwater SLIPT, the proposed algorithm’s adaptive nature allows for the dynamic fine-tuning of optimization parameters in response to varying underwater environmental conditions and diverse user requirements.

Hybrid Artificial Protozoa-Based JADE for Attack Detection

This paper presents a novel hybrid optimization algorithm that combines JADE Adaptive Differential Evolution with Artificial Protozoa Optimizer (APO) to solve complex optimization problems and detect attacks. The proposed Hybrid APO-JADE Algorithm leverages JADE’s adaptive exploration capabilities and APO’s intensive exploitation strategies, ensuring a robust search process that balances global and local optimization. Initially, the algorithm employs JADE’s mutation and crossover operations, guided by adaptive control parameters, to explore the search space and prevent premature convergence. As the optimization progresses, a dynamic transition to the APO mechanism is implemented, where Levy flights and adaptive change factors are utilized to refine the best solutions identified during the exploration phase. This integration of exploration and exploitation phases enhances the algorithm’s ability to converge to high-quality solutions efficiently. The performance of the APO-JADE was verified via experimental simulations and compared with state-of-the-art algorithms using the 2022 IEEE Congress on Evolutionary Computation benchmark (CEC) 2022 and 2021. Results indicate that APO-JADE achieved outperforming results compared with the other algorithms. Considering practicality, the proposed APO-JADE was used to solve a real-world application in attack detection and tested on DS2OS, UNSW-NB15, and ToNIoT datasets, demonstrating its robust performance.

Immersion and Invariance Adaptive Control through Polynomial Adaptation

Abstract In conventional immersion and invariance (I&I) adaptive control design, control parameter adaptation is typically linear with respect to the parameter-error-induced perturbation, resulting in quadratic-rate dissipation of energy associated with the off-the-manifold variable. Departing from such a convention, this article contributes a novel strategy - polynomial adaptation. As the name suggests, control parameter adaptation in this approach takes the form of a general polynomial in relation to the perturbation. Accordingly, this new design induces polynomial-rate energy dissipation, which is faster than the quadratic one in the conventional scheme, thereby enhancing closed-loop control performance. The theoretical underpinnings of the new approach are demonstrated through the design of an I&I adaptive tracking control law for a general nth-order, single-input-single-output, parametrically uncertain, nonlinear system in the controllable canonical form. Additionally, a numerical study of the proposed method on the second-order forced Duffing oscillator showcases its improved transient performance in comparison to a baseline controller developed with the standard I&I adaptive control technique.

Methodology for assessing the effectiveness of the impact intermittent noise interference to a radar station with adaptive detection threshold formation

Problem statement. For aviation suppression equipment, it is characteristic to separate the modes of interference and reconnaissance radiation in time due to the problem of their electromagnetic compatibility. It is also possible to alternately suppress various objects with a limited bandwidth of the interference station. This leads to periodic interruptions of the noise interference and, consequently, a decrease in its spectral density. A feature of radar stations of the latest and modernized means of intercepting air defense systems aimed at hitting targets in a complex interference environment is adaptive control of parameters and modes of operation of various subsystems. In particular, modern radar stations provide for the adaptive formation of a detection threshold based on estimates of the statistical characteristics of such interference. Such estimates are formed over the interference integration interval, the size of which is generally selected from the condition of insignificant influence of signals reflected from the targets on them. Moreover, for the most correct consideration of the interference situation in the vicinity of the detected target, the strobe pulses of the false alarm probability stabilization circuit, forming the integration interval, are placed on both sides of the analyzed range resolution element. Goal. To evaluate the effect of intermittent noise interference parameters on the operation of radar stations with adaptive detection threshold foration. Assumptions. When developing the methodology, the following typical assumptions were made: in target detection mode, the radar station emits pulse signals with a low or medium pulse repetition rate; the signal reflected from the target, noise interference and internal noise are independent normal random processes; interference and internal noise have a uniform spectral density within the bandwidth of the radar receiver; the receiving path of the radar station has a traditional structure, which includes: an intermediate frequency amplifier, a linear amplitude detector, an integrator and a threshold device. Results. For the first time, the obtained technique takes into account the influence of time and energy parameters of intermittent noise interference on the probability of correct target detection by a radar station with adaptive detection threshold formation. Reducing the duration of the interference pulse in the period of intermittent noise interference leads to a nonlinear increase in the probability of correct target detection. To maximize this indicator, it is necessary to ensure a sufficiently high accuracy in estimating the statistical characteristics of the power of intermittent noise interference. The reliability of the results obtained using the developed methodology is confirmed by the fact that the effectiveness of intermittent noise interference, while excluding pauses in its radiation, is reduced to the effectiveness of continuous noise interference. Practical significance. The developed technique makes it possible to clarify the technical requirements for radar stations that implement flexible control of the target detection mode under the influence of intermittent noise interference of unknown intensity.

Conformable fractional accumulation in triangular fuzzy sequences grey nonlinear model for tertiary industry gross output forecast

The prediction of the tertiary industry's output value is essential for enhancing the market economic system and providing guidance for the government in formulating relevant economic policies. The tertiary industry encompasses various sectors such as finance and transportation, and its total output value exhibits uncertain and nonlinear trends. Triangular fuzzy number sequences contain more comprehensive information than exact number sequences. Therefore, a nonlinear grey Bernoulli model with conformable fractional accumulation for triangular fuzzy sequences is proposed. Firstly, the conformable fractional accumulation generating operation is utilized to mitigate the volatility tendency of the original sequences. Additionally, a time power function term is introduced to capture the power exponential trend. Furthermore, an improved whale optimization algorithm incorporating opposition-based learning strategy and adaptive control parameters is employed to optimize the model parameters, which has been verified through algorithm comparison experiments. Three existing grey models are used as competing models to validate the prediction accuracy of our proposed model. Finally, we utilize this proposed model to predict the tertiary industry's output values in both Yangtze and Pearl River Deltas for 2024–2026.

Adaptive control strategy of VSG parameters based on improved fuzzy control

Abstract A virtual synchronous generator (VSG) enables the converter output to have similar external characteristics to the synchronous generator, but its virtual rotary inertia and damping factor need to be flexibly adapted to strengthen the dynamic stability of the system. Given the shortcomings of existing adaptive strategies, this paper presents a VSG parameter adaptive control strategy based on improved fuzzy control. GRO is used to optimize the quantization factor and scale factor of the fuzzy controller, to strengthen the adaptive ability of the fuzzy controller to the rotary inertia and damping factor. Simulink simulation is used to verify the superiority of the control strategy.

Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

A Novel Method on Recognizing Drum Load of Elastic Tooth Drum Pepper Harvester Based on CEEMDAN-KPCA-SVM

The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. This study proposes a novel method integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), kernel principal component analysis (KPCA), and a support vector machine (SVM) to enhance drum load recognition. The method consists of three principal steps: the initial experiments with ETDPHs to identify the critical factors affecting drum load and to formulate classification criteria; the development of a CEEMDAN-KPCA-SVM model for ETDPH drum load recognition, where drum spindle torque signals are processed by CEEMDAN for decomposition and reconstruction, followed by feature extraction and dimensionality reduction via KPCA to refine the model’s accuracy and training efficiency; and evaluation of the model’s performance on real datasets, highlighting the improvements brought by CEEMDAN and KPCA, as well as comparative analysis with other machine learning models. The results describe four load conditions—no load (mass of pepper intake (MOPI) = 0 kg/s), low load (0 < MOPI ≤ 0.658 kg/s), normal load (0.658 < MOPI ≤ 1.725 kg/s), and high load (MOPI > 1.725 kg/s)—with the CEEMDAN-KPCA-SVM model achieving 100% accuracy on both training and test sets, outperforming the standalone SVM by 6% and 12.5%, respectively. Additionally, it reduced the training time to 2.88 s, a 10.9% decrease, and reduced the prediction time to 0.0001 s, a 63.6% decrease. Comparative evaluations confirmed the superiority of the CEEMDAN-KPCA-SVM model over random forest (RF) and gradient boosting machine (GBM) in classification tasks. The synergistic application of CEEMDAN and KPCA significantly improved the accuracy and operational efficiency of the SVM model, providing valuable insights for load recognition and adaptive control of ETDPH drum parameters.

Investigation of a high-performance control algorithm for a unified chaotic system synchronization control based on parameter adaptive method

With the rapid development of computer technology, parameter adaptive control methods are becoming more and more widely used in nonlinear systems. However, there are still many problems with synchronous controllers with multiple inputs and a single output, uncertainty, and dynamic characteristics. This paper analyzed a synchronization control strategy of uncoupled nonlinear systems based on parameter dynamic factors to adjust the performance of the synchronization controller, and briefly introduced the manifestations of chaotic motion. The characteristics and differences of continuous feedback control methods and transmission and transfer control methods were pointed out. Simple, effective, stable, and feasible synchronous control was analyzed using parameter-adaptive control theory. By analyzing the non-linear relationships between various models at different orders, the fuzzy distribution of the second-order mean and their independent and uncorrelated matrices were obtained, and their corresponding law formulas were established to solve the functional expression between the corresponding state variables and the dynamic characteristics of the system. The error risk test, computational complexity test, synchronization performance score test, and chaos system control effect score test were carried out on the control algorithms of traditional chaos system synchronization methods and chaos system synchronization methods based on parameter adaptive methods. Parameter adaptive methods were found to effectively reduce the error risk of high-performance control algorithms for synchronization of the unified chaos system. The complexity of the calculation process was simplified and the complexity score of the calculation process was reduced by 0.6. The application of parameter adaptive methods could effectively improve the synchronization performance of control algorithms, and the control effectiveness rating of control algorithms was improved. The experimental test results proved the effectiveness of control algorithms, which greatly enriched the field of modern control applications and also drove the vigorous development of nonlinear dynamics research, thus making significant progress in chaos application research.

Adaptive handover control parameters based on cell load capacity in a B5G/6G heterogeneous network

Adaptive handover control parameters based on cell load capacity in a B5G/6G heterogeneous network

Design and implementation of adaptive control for industrial robot servo trajectory tracking

Abstract To cope with the complex external environment of the robot, an online adaptive control method for industrial robots is implemented by using robot dynamic parameters as control variables. A Newton-Euler method is implemented to establish a rigid body dynamics model for the robot. Subsequently, we confirm the possibility of dynamic parameter adaptive control by employing the Lyapunov stability theory and derive the dynamic parameter estimation law. Three methods are proposed to realize the observation matrix containing reference information in the adaptive control algorithm. Through physical experiments, it is verified that the dynamic parameter adaptive control is superior to traditional PPI control, and its position tracking error and anti-interference ability are significantly improved.

High Utility Itemset Extraction using PSO with Online Control Parameter Calibration

This study investigates the use of evolutionary computation for mining high-value patterns from benchmark datasets. The approach employs a fitness function to assess the usefulness of each pattern. However, the effectiveness of evolutionary algorithms heavily relies on the chosen strategy parameters during execution. Conventional methods set these parameters arbitrarily, often leading to suboptimal solutions. To address this limitation, the research proposes a method for dynamically adjusting strategy parameters using temporal difference approaches, a machine learning technique called Reinforcement Learning (RL). Specifically, the proposed IPSO RLON algorithm utilizes SARSA learning to intelligently adapt the Crossover Rate and Mutation Rate within the Practical Swarm Optimization Algorithm. This allows IPSO RLON to effectively mine high-utility itemsets from the data.The key benefit of IPSO RLON lies in its adaptive control parameters. This enables it to discover optimal high-utility itemsets when applied to various benchmark datasets. To assess its performance, IPSO RLON is compared to existing approaches like HUPEUMU-GRAM, HUIM-BPSO, IGA RLOFF, and IPSO RLOFF using metrics like execution time, convergence speed, and the percentage of high-utility itemsets mined. From the evaluation it is observed that the proposed IPSO RLON perfroms better than the other methodology.

Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems.

Out-of-Mold Sensor-Based Process Parameter Optimization and Adaptive Process Quality Control for Hot Runner Thin-Walled Injection-Molded Parts.

Injection molding is a highly nonlinear procedure that is easily influenced by various external factors, thereby affecting the stability of the product's quality. High-speed injection molding is required for production due to the rapid cooling characteristics of thin-walled parts, leading to increased manufacturing complexity. Consequently, establishing appropriate process parameters for maintaining quality stability in long-term production is challenging. This study selected a hot runner mold with a thin wall fitted with two external sensors, a nozzle pressure sensor and a tie-bar strain gauge, to collect data regarding the nozzle peak pressure, the timing of peak pressure, the viscosity index, and the clamping force difference value. The product weight was defined as the quality indicator, and a standardized parameter optimization process was constructed, including injection speed, V/P switchover point, packing, and clamping force. Finally, the optimized process parameters were applied to the adaptive process control experiments using the developed control system operated within the micro-controller unit (MCU). The results revealed that the control system effectively stabilized the product weight variation and standard deviation of 0.677% and 0.0178 g, respectively.

Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments.

The utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial vehicles (UAV) require real-time adjustments for flight safety and efficacy. To improve the attitude estimation accuracy, this article introduces a ATT-Bi-LSTM framework for optimizing UAVs through adaptive parameter control, which integrates the state information gleaned from communication signals. The ATT-Bi-LSTM achieves data feature extraction by means of a two-layer Bidirectional Long Short-Term Memory (BI-LSTM) at its inception to enhance the feature. Subsequently, it harnesses the attention mechanism to amplify the LSTM network's output, thereby enabling the optimal control of UAV positioning. During the empirical phase, we employ optical system data for the comparative validation of the model. The outcomes underscore the commendable performance of the proposed framework in this study, particularly with regard to the three pivotal position indicators: yaw, pitch, and roll. In the comparison of indicators such as RMSR and MAE, the proposed model has the lowest error, which provides algorithm support and important reference for future UAV optimization control research.

Practical fixed‐time composite‐learning control for full‐state constraint strict‐feedback non‐linear systems: A dynamic regressor extension and mixing based approach

AbstractA practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test.