Adaptive Parameter Tuning Research Articles

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

The paper is devoted to the problem of the decentralized control of unmanned aerial vehicle (UAV) formation in the case of parametric uncertainty. A new version of the feedback linearization approach is proposed and used for a point mass UAV model transformation. As result, a linear model is obtained containing an unknown value of the UAV mass. Employing the speed-gradient design method and the implicit reference model concept, a combined adaptive control law is proposed for a single UAV, including the UAV’s mass estimation and adaptive tuning of the controller parameters. The obtained new algorithms are then used to address the problem of consensus-based decentralized control of the UAV formation. Rigorous stability conditions for control and identification are derived, and simulation results are presented to demonstrate the quality of the closed-loop control system for various conditions.

Randomized Moving Target Approach for MAC-Layer Spoofing Detection and Prevention in IoT Systems

MAC-layer spoofing, also known as identity spoofing, is recognized as a serious problem in many practical wireless systems. IoT systems are particularly vulnerable to this type of attack as IoT devices (due to their various limitations) are often incapable of deploying advanced MAC-layer spoofing prevention and detection techniques, such as cryptographic authentication. Signal-level device fingerprinting is an approach to identity spoofing detection that is highly suitable for sensor-based IoT networks but can be also utilized in many other types of wireless systems. Previous research works on signal-level device fingerprinting have been based on rather simplistic assumptions about both the adversary’s behavior and the operation of the defense system. The goal of our work was to examine the effectiveness of a novel system that combines signal-level device fingerprinting with the principles of Randomized Moving Target Defense (RMTD) when dealing with a very advanced adversary. The obtained results show that our RMTD-enhanced signal-level device fingerprinting technique exhibits far superior defense performance over the non-RMTD techniques previously discussed in the literature and, as such, could be of great value for practical wireless systems subjected to identity spoofing attacks. We have also introduced a novel proof-of-concept adaptive parameter tuning approach for system practitioners with the ability to encode their risk profile and compute the most efficient hyper-parameters of our proposed defense system.

An Adaptive Control of Remote Hybrid Microgrid based on the CMPN Algorithm

• The paper proposes a decentralized droop-based control technique power flow through the ILC, meanwhile, to keep the dc microgrid voltage and the ac microgrid frequency within their acceptable ranges. • To overcome the continuous operation of ILC, The modified droop control based CMPN algorithm is used to determine the necessity of operation of hybrid microgrid by estimating the status of the ac and dc microgrids by comparing local measurements with their critical values at both sides. • An adaptive filtering algorithm based on continuous mixed P-norm (CMPN) algorithm is utilized for the proposed autonomous control strategy to insure adaptive tuning of the controller parameters. The variable step size feature of the CMPN algorithm results in fast response compared to other techniques such as robust mixed-norm and the least absolute deviation techniques. The robustness against impulsive noise while having fast convergence, compared to other algorithms, render the CMPN algorithm to be an effective technique for online adaptation of the PI controller used for the ILC, especially in an islanded archetype to maintain the stability of the network. • A comparison between the proposed strategy based on both CMPN algorithm and adaptive PI control under different loading conditions are implemented to validate the proposed methodology. This paper proposes a new adaptive communication-less control strategy to enhance the dynamic performance of the interlinking converter (ILC) of a remote hybrid microgrid. The proposed autonomous strategy is based on the continuous mixed P-norm (CMPN) algorithm to simultaneously maintain the frequency of ac-side and the voltage of dc-side of the ILC within their acceptable ranges under various conditions. The variable step nature of the CMPN algorithm and robustness ensure stable operation of the proposed strategy for the ILC. In addition, the power sharing between both sides of the ILC is achieved by a modified ac-dc droop control to avoid using any communication systems between different distributed generators (DG). Moreover, a simple algorithm is used to determine the desired power flow direction based on local measurements of dc voltage and frequency. The proposed CMPN algorithm provides an online adaption of the controller's parameters to avoid the need for fine tuning or optimization techniques under various operating conditions. The dynamic performance of the proposed autonomous control strategy is evaluated using the PSCAD/EMTDC simulation package under different operating modes. The results demonstrate the superior dynamic performance of the proposed strategy based on the CMPN algorithm compared to the adaptive PI (API) controller in terms of fast response and tight regulation of power flow, voltage and frequency under different conditions. Compared to API, it has been found that the proposed controller results in more improvement of the dc-side voltage within the operating frequency range of the ac-side of the ILC.

Adaptive Parameter Tuning for Constructing Storage Tiers in an Autonomous Distributed Storage System

We are developing an autonomous distributed storage system to make effective use of networked heterogeneous storage devices. It dynamically configures the storage tiers using effective performance values measured during operation, rather than static performance indicators of the storage device. Data blocks are transferred to a more appropriate storage tier according to their read/write tendencies. In the previous paper, the storage tiers were set using statically defined thresholds, but in this paper, we propose a method to set the thresholds adaptively. This method eliminates the need to adjust the runtime parameters for threshold setting according to the runtime environment. It has also been improved so that appropriate parameter values can be set for individual storage, rather than applying a single parameter value to the entire system. In the previous system, the administrator had to readjust the parameter values periodically to match the usage status of the storage device and the behavior of the process. However, in the proposed method, the parameter values are fine-tuned during operation to reduce the workload of the administrator. The simulation results show that the proposed method can reduce the execution time by up to about 30% compared to the previous method with optimum parameters configured statically.

Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments.

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.

Reliability sensitivity analysis based on a two-stage Markov chain Monte Carlo simulation

The reliability sensitivity index based on the safety/failure classification of model predictions is a valuable tool to measure how uncertain parameters affect the failure of engineering systems. The key to estimate this index is to estimate the failure-conditional probability density function (PDF) of parameters, which can be regarded as a posterior PDF from the perspective of Bayesian inference. Due to the binary property of the failure indicator function, it is difficult to directly sample the failure-conditional PDF. In this work, a new efficient sampling method is proposed to estimate the failure-conditional PDF and the reliability sensitivity index through a two-stage Markov chain Monte Carlo (MCMC) simulation. In the first stage, a different criterion based on the distance to the failure domain is adopted to update the chain and finally get a first sample in the failure domain. Then, starting with this sample, a normal Markov chain is run in the second stage to simulate the failure-conditional PDF and estimate the reliability sensitivity index. The preconditioned Crank-Nicolson algorithm (a special MCMC method) is adopted to deal with high-dimensional problems (with many uncertain parameters), and adaptive parameter tuning is used to enhance the performance of the proposed two-stage MCMC method. Several test examples show the high efficiency of the proposed two-stage MCMC method compared to subset simulation. The method is currently designed for problems with a single failure domain, yet it could be extended.

Adaptive reachability algorithms for nonlinear systems using abstraction error analysis

In many reachability algorithms for nonlinear ordinary differential equations (ODEs), the tightness of the computed reachable sets mainly depends on abstraction errors and the choice of the set representation. One has to mitigate the resulting wrapping effects by suitable tuning of internally-used algorithm parameters since there exists no wrapping-free algorithm to date. In this work, we investigate the fundamentals governing the abstraction error in reachability algorithms – which we also refer to as set-based solvers – and its dependence on the time step size, leading to the introduction of a gain order. This order is related to measures for local and global abstraction errors and thus relates the well-known concept of convergence order from classical ODE solvers to set-based solvers. Furthermore, the simplification of the set representation is tackled by limiting the Hausdorff distance between the original and reduced sets; we demonstrate this for zonotopes. Both these theoretical advancements are incorporated in a modular adaptive parameter tuning algorithm suited for multiple classes of nonlinear ODEs whose efficiency is demonstrated on a wide range of benchmarks.

Illegal Intrusion Detection for In-Vehicle CAN Bus Based on Immunology Principle

The controller area network (CAN) bus has become one of the most commonly used protocols in automotive networks. Some potential attackers inject malicious data packets into the CAN bus through external interfaces for implementing illegal operations (intrusion). Anomaly detection is a technique for network intrusion detection which can detect malicious data packs by comparing the normal data packets with incoming data packets obtained from the network traffic. The data of a normal network is in a symmetric and stable state, which will become asymmetric when compromised. Considering the in-vehicle network, the CAN bus is symmetrically similar to the immune system in terms of internal network structure and external invasion threats. In this work, we use an intrusion detection method based on the dendritic cell algorithm (DCA). However, existing studies suggest the use of optimization methods to improve the accuracy of classification algorithms, and the current optimization of the parameters of the detection method mostly relies on the manual tuning of the parameters, which is a large workload. In view of the above challenges, this paper proposes a new detection algorithm based on the particle swarm optimization algorithm (PSO) and gravitational search algorithm (GSA) to improve the dendritic cell algorithm (PSO-GSA-DCA). PSO-GSA-DCA achieves adaptive parameter tuning and improves detection accuracy by mixing optimization algorithms and using them to optimize the dendritic cell algorithm classifier. Additionally, DCA-based CAN message attribute matching rules (measured by information gain and standard deviation of CAN data) are proposed for matching the three input signals (PAMP, DS, SS) of the DCA. The experimental results show that our proposed scheme has a significant improvement in accuracy, which can reach 91.64%, and lower time loss compared with other correlation anomaly detection schemes. Our proposed method also enables adaptive tuning, which solves the problem that most models now rely on manual tuning.

GENEmops: Supervised feature selection from high dimensional biomedical dataset

Identification of differentially expressed genes, lying beneath the carcinogenic expression, is still very crucial for accurate detection and treatment of the disease. The challenge of a large number of attributes compared to a small number of instances and the prediction of highly discriminative genes requires an effective method. It can be regarded as a multi-objective problem that involves minimization of the number of selected genes and maximization of the classification performance. It is expected to find the optimal count of the most significant genes which are strongly associated with the classification of cancer. In this paper, we have proposed a framework entitled as GENEmops for gene selection and subsequent classification of the disease. The core of the GENEmops is inspired by multi-objective player selection strategy based hybrid population search (MOPS-HPS). The proposed system uses a multi-filtering and adaptive parameter tuning approach for gene selection. A new graded rotational blending operator is introduced to enhance the exploitation capability of the hybrid wrapper based scheme. Unlike the most current existing methods which employ some strategy to transmute the continuous search space to binary search space, it uses an adaptive way for binary conversion which is stochastically updated during the search phase. GENEmops also improves the performance of the classifier by tuning its parameters. The efficiency of the proposed GENEmops is tested on sixteen biological datasets (eight binary class and eight multi-class) and compared with state-of-the-art computationally intelligent multi-objective approaches. Experimental results demonstrate the efficiency of the proposed work.

Adaptive Asymptotic Tracking With Global Performance for Nonlinear Systems With Unknown Control Directions

This article presents a global adaptive asymptotic tracking control method, capable of guaranteeing prescribed transient behavior for uncertain strict-feedback nonlinear systems with arbitrary relative degree and unknown control directions. Unlike most existing funnel controls that are built upon time-varying feedback gains, the proposed method is derived from a tracking error-dependent normalized function and a barrier function, together with a time-varying scaling transformation, leading to an improved prescribed performance control solution with the following features: 1) the developed control is embedded with normalized error based adaptive parameter tuning and is able to ensure asymptotic tracking; 2) given transient performance is guaranteed in that the tracking error preserves in the prescribed boundary for <inline-formula><tex-math notation="LaTeX">$\forall t\geq 0$</tex-math></inline-formula>; and 3) it is able to cope with nonlinear systems with arbitrary relative degree, mismatched uncertainties, and unknown control directions. Both theoretical analysis and numerical simulations verify the effectiveness and benefits of the proposed method.

A Comparative Study of Meta-Heuristic and Conventional Search in Optimization of Multi-Dimensional Feature Selection

Algorithmic – based search approach is ineffective at addressing the problem of multi-dimensional feature selection for document categorization. This study proposes the use of meta heuristic based search approach for optimal feature selection. Elephant optimization (EO) and Ant Colony optimization (ACO) algorithms coupled with Naïve Bayes (NB), Support Vector Machin (SVM), and J48 classifiers were used to highlight the optimization capability of meta-heuristic search for multi-dimensional feature selection problem in document categorization. In addition, the performance results for feature selection using the two meta-heuristic based approaches (EO and ACO) were compared with conventional Best First Search (BFS) and Greedy Stepwise (GS) algorithms on news document categorization. The comparative results showed that global optimal feature subsets were attained using adaptive parameters tuning in meta-heuristic based feature selection optimization scheme. In addition, the selected number of feature subsets were minimized dramatically for document classification.

A PID Control Algorithm With Adaptive Tuning Using Continuous Artificial Hydrocarbon Networks for a Two-Tank System

Owing to their ease of implementation, proportional-integral-derivative (PID) control systems are widely used to control physical systems. However, when environmental disturbances or changes in system parameters occur, the complexity of tuning the gains of PID controllers increases because, in these cases, their performance decreases. To solve this problem, an AI-based online self-tuning algorithm adjusts the PID gains when system parameters are changed. Thus, the objective of this study was to develop a PID control algorithm with adaptive parameter tuning using artificial hydrocarbon networks, which is a supervised learning artificial intelligence technique inspired by hydrocarbon networks. Artificial hydrocarbon networks were designed to be trained using a prior set of data. Therefore, the motivation of this study was to demonstrate that they can be trained in real time for use in the control of nonlinear systems. Because this type of network has not been commonly used for this specific application, existing studies on adaptive control based on artificial neural networks were taken as a reference. The AMSGrad optimization algorithm was used to train the parameters in real time, for which a “continuous” model of artificial hydrocarbon networks was also proposed. Finally, an algorithm capable of adapting to variations in the operating conditions of a tank system was successfully designed, although its performance was similar to that of the Ziegler-Nichols method.

Enhancement of Tracking and Disturbance Suppression Performance via Adaptive Parameter Tuning of Two-degree-of-freedom Control System Using On-line FRIT

In general, the characteristics of the control object changes over time due to aging, breakdowns, and changes in operating conditions and environment. In such cases, tracking and disturbance suppression performance of the entire closed-loop system may deteriorate. Based on a data-driven approach (online FRIT), we propose a method for simultaneously and sequentially tuning the control parameters of the feedforward and feedback controller of a two-degree-of-freedom control system in real time. We set two reference models as indicators of control performance: a target response model and a sensitivity characteristic model. The adaptive computation mechanism of the proposed method is based on the RLS (recursive least-squares) algorithm with a forgetting factor which can appropriately reflect changes in the characteristics of the controlled object. The proposed method enhances target tracking and disturbance suppression performance for time-varying systems without control system shutdown in real time. Finally, the effectiveness of the proposed method is demonstrated through simulation on benchmark process system.

An adaptive hyper parameter tuning model for ship fuel consumption prediction under complex maritime environments

An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of environmental factors on fuel consumption has been lacking. In addition, although recent research efforts have widely focused on machine learning methods to predict fuel consumption, studies on hyperparameter values that are suitable for these prediction models are limited. To compensate for this deficiency in existing literature, an adaptive hyperparameter tuning method is proposed, and the effects of maritime environmental factors on fuel consumption are taken into account. Through experimentation, the proposed adaptive hyperparameter tuning method was validated via artificial neural network (ANN), support vector regression (SVR), random forest (RF), and least absolute shrinkage and selection operator (Lasso). The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination (R2) of these algorithms. The increase of the amplitude demonstrated the following trend, in the order of the largest increase to the lowest increase: ANN, Lasso, SVM, and RF. The rates of increase were between 0.0773% and 2.1653%. Furthermore, after the environmental factors were considered, the prediction accuracies of the ANN and Lasso increased; however, the opposite was observed for the SVR and RF. As such, we confirmed that the use of Bayesian optimisation for hyperparameter tuning can effectively improve the fuel consumption prediction accuracy, and our proposed model can therefore serve as a significant reference for calculating fuel consumption.

A fast constrained state transition algorithm

When solving constrained optimization problems in real industrial processes, both optimality and computational efficiency need to be considered. However, most existing meta-heuristic algorithms are slow to find the global optimum. The first reason is that the way to generate and select candidate solutions is time-consuming. The low probability to generate and select potential solutions in assisting the computational efficiency is another reason. In this paper, a simplified state transition algorithm (STA) and a novel constraint-handling technique are proposed to address the above issues for small size constrained optimization problems. Firstly, three out of four operators in basic STA to produce candidate solutions are selected and two operators are modified with adaptive parameter tuning, which have a large probability to generate potential solutions, but consumes less time. Secondly, the constraint-handling technique considers not only the objective function value and the constraint violation but also the difference among candidate solutions. Thirdly, the sequential quadratic programming embedded into the simplified STA can further speed up the convergence. Experiments are conducted on 22 well-known test functions from IEEE CEC2006 and 4 engineering constrained optimization problems, in comparison with state-of-the-art algorithms. The experimental results show that the proposed method is competitive in finding the optimum faster.

Adaptive Estimation of Vehicle Velocity From Updated Dynamic Model for Control of Anti-Lock Braking System

The main challenge for designing the automotive control systems like the anti-lock braking system (ABS) is to access a reliable model because of uncertainties and variations in vehicle dynamics and tire forces. In this article, the initial dynamic model of the vehicle braking system is updated at each instant by estimation of model compensatory terms using a novel prediction approach. The proposed estimation method uses the global navigation satellite system (GNSS) as a reference to adaptively estimate the vehicle velocity with high frequency in the presence of variable bias of acceleration sensor. The proposed estimation algorithm is mathematically analyzed and experimentally evaluated to show its high accuracy obtained by the adaptive tuning of parameters. Accordingly, a nonlinear controller based on the estimated dynamic model is designed to prevent the tire from being locked by tracking the desired slip. The designed control system is tested within the CarSim software as a virtual vehicle experiment platform. The obtained results reveal that the designed model-based control system remarkably improves the braking performance compared with the conventional sensor-based ABS controllers.

Decomposition-Based Multiobjective Evolutionary Algorithm With Genetically Hybrid Differential Evolution Strategy

In the decomposition-based multiobjective evolutionary algorithms (MOEA/Ds), a set of subproblems are optimized by using the evolutionary search to exploit the feasible regions. In recent studies of MOEA/Ds, it was found that the design of recombination operators would significantly affect their performances. Therefore, this paper proposes a novel genetically hybrid differential evolution strategy (GHDE) for recombination in MOEA/Ds, which works effectively to strengthen the search capability. Inspired by the existing studies of recombination operators in MOEA/Ds, two composite operator pools are introduced, each of which includes two distinct differential evolution (DE) mutation strategies, one emphasizing convergence and the other focusing on diversity. Regarding each selected operator pool, two DEs are applied on parents' genes to hybridize offspring by adaptive parameters tuning. Moreover, a fitness-rate-rank-based multiarmed bandit (FRRMAB) is embedded into our algorithm to select the best operator pool by collecting their recently achieved fitness improvement rates. After embedding GHDE into an MOEA/D variant with dynamical resource allocation, a variant named MOEA/D-GHDE is presented. Various test multiobjective optimization problems (MOPs), i.e., UF, F test suites, and MOPs with difficult-to-approximate (DtA) PF boundaries, are used to assess performances. Compared to several competitive MOEA/D variants, the comprehensive experiments validate the superiority of our algorithm.

A particle swarm optimization algorithm for mixed-variable optimization problems

Abstract Many optimization problems in reality involve both continuous and discrete decision variables, and these problems are called mixed-variable optimization problems (MVOPs). The mixed decision variables of MVOPs increase the complexity of search space and make them difficult to be solved. The Particle Swarm Optimization (PSO) algorithm is easy to implement due to its simple framework and high speed of convergence, and has been successfully applied to many difficult optimization problems. Many existing PSO variants have been proposed to solve continuous or discrete optimization problems, which make it feasible and promising for solving MVOPs. In this paper, a new PSO algorithm for solving MVOPs is proposed, namely P S O m v , which can deal with both continuous and discrete decision variables simultaneously. To efficiently handle mixed variables, the P S O m v employs a mixed-variable encoding scheme. Based on the mixed-variable encoding scheme, two reproduction methods respectively for continuous variables and discrete variables are proposed. Furthermore, an adaptive parameter tuning strategy is employed and a constraints handling method is utilized to improve the overall efficiency of the P S O m v .The experimental results on 28 artificial MVOPs and two practical MVOPs demonstrate that the proposed P S O m v is a competitive algorithm for MVOPs.

Multi-sensor fusion approach based on nonlinear H∞ filter with interval type 2 fuzzy adaptive parameters tuning for unmanned vehicle localization

In most applications of autonomous navigation, the state of a system must be estimated from noisy sensors. Accurate estimation of the true system state can be achieved using data fusion algorithms. Furthermore, the fusion scheme can be affected by many factors such as modeling errors and parameters uncertainties. The gaps and inconsistencies due to the sensors noise and modeling errors can be reached with robust nonlinear filtering. In this article, a new framework has been developed for data fusion algorithms based on nonlinear NH∞ filter with fuzzy adaptive bound and adaptive disturbances attenuations. Type-1 Fuzzy Adaptive NH∞ algorithm has been proposed and compared with the Interval Type 2 Fuzzy Adaptive NH∞, for unmanned vehicle localization. The proposed algorithms fuse data from low-cost sensors using inertial navigation system, Global Positioning System and monocular vision. Type-1 Fuzzy Adaptive NH∞ and Interval Type 2 Fuzzy Adaptive NH∞ algorithms, adaptively, handle the effects of noisy sensors, parameters uncertainties and modeling errors. Both algorithms use adaptive bounds [Formula: see text] and adaptive disturbance attenuation [Formula: see text] for higher-order terms of the Taylor development. The adaptive bounds consider issues associated to Gyro drift, image distortion and Global Positioning System dilution of precision which make the proposed algorithms more accurate than the classical NH∞. The validating experiment and the efficiency of Type-1 Fuzzy Adaptive NH∞ and Interval Type 2 Fuzzy Adaptive NH∞ have been conducted in real scenario and in unstructured outdoor environment without any a priori knowledge of the evolution of the vehicle. The experimental results have demonstrated the robustness and efficiency of these two filtering strategies against uncertainties and their online sensor switching capability. The Interval Type 2 Fuzzy Adaptive NH∞ algorithm provides more accuracy and robustness compared to the Type-1 Fuzzy Adaptive NH∞.

Enhanced battery controller for inertia support in residential microgrid based on active disturbance rejection control

Lack of system inertia is becoming a big concern in modern power systems with a high penetration of inverter-interfaced generation. In this paper, we argue that PV-battery systems owned by end-users can be used as a source of inertia to improve the frequency performance of future grids. To that end, we implement grid-forming control on battery converters in a residential microgrid powered by prosumer-owned PV-battery systems and a back-up generator. To improve performance, we propose a novel controller based on active disturbance rejection control (ADRC). This controller observes and rejects the “total disturbance” of the system, thereby increasing the response speed and enhancing the stability of the system. In addition, we incorporate an adaptive parameter tuning algorithm into the ADRC controller, which automatically calculates and updates the optimal control parameters in every sampling period. Simulation results are provided to verify the effectiveness and feasibility of the proposed approach.