Adaptive Control Algorithm Research Articles

A Review of Research on Robot Automatic Control Technology

The promotion and application of robotic technology are of great significance for the improvement of production efficiency and the reduction of production risk and cost, which requires robots to have strong adaptability and flexibility in sophisticated situations, so the study of robot automatic control technology has always been a hot topic of research at home and abroad. This paper combines three stages of the development of automatic control theory, reviews the algorithms applied to robot automatic control technology at different phases of development, describes in detail the principles of PID control algorithms, adaptive control algorithms, sliding mode control algorithms, H control algorithms, neural network control algorithms, and fuzzy control algorithms, and their applications in robotics. The advantages and disadvantages of different algorithms in practical application scenarios are analyzed. Hybrid control algorithms integrate a variety of control techniques, combining the advantages of control schemes and overcoming the limitations that exist in different control techniques. In the future, hybrid control algorithms will be widely used in the field of robot control and the focus of future research are proposed.

Robust Adaptive Control of a Coaxial-Ducted-Fan Aircraft with Uncertainty Model

This paper focuses on the robust adaptive control of a coaxial-ducted-fan aircraft under the context of significant system uncertainty. To ensure accurate tracking even in the presence of model uncertainties and disturbances for a ducted-fan flight platform, a model reference adaptive control architecture is proposed in this paper on the basis of control augmentation. With the nominal closed-loop system as the reference system, this architecture overlays adaptive control inputs on top of the linear time-invariant control inputs to compensate for uncertainties in real time. This allows the reference system to be tracked rapidly and accurately by the output of the uncertain system, which ensures that the performance of the closed-loop system meets design requirements and that the flight envelope of the prototype is further expanded. The superiority of the proposed method is demonstrated by numerical simulation of the ducted-fan aircraft. The simulation results show that the proposed robust adaptive control algorithm is advantageous over the baseline control in rapidly compensating for the closed-loop performance of the system, even in the presence of a more severe disturbance. At the same time, the performance of control algorithms are verified by conducting flight experiments. The results show that when the prototype undergoes low-speed slow-change maneuvering, there is little difference in the tracking performance of the four channels of the two control strategies. However, when the prototype undergoes high-speed fast-change maneuvering, the root mean square (RMS) values of the tracking errors of the robust adaptive control and the baseline control are 0.749 m/s and 1.039 m/s, respectively, and the robust adaptive algorithm reduces the tracking error by about 38% on the basis of the baseline control.

Robust adaptive fuzzy type-2 fast terminal sliding mode control of robot manipulators in attendance of actuator faults and payload variation

Introduction. This study presents a robust control method for the path following problem of the PUMA560 robot. The technique is based on the Adaptive Fuzzy Type-2 Fast Terminal Sliding Mode Control (AFT2FTSMC) algorithm and is designed to handle actuator faults, uncertainties (such as payload change), and external disturbances. The aim of this study is to utilize the Fast Terminal Sliding Mode Control (FTSMC) approach in order to ensure effective compensation for faults and uncertainties, minimize tracking error, reduce the occurrence of chattering phenomena, and achieve rapid transient response. A novel adaptive fault tolerant Sliding Mode Control (SMC) approach is developed to address the challenges provided by uncertainties and actuator defects in real robotics tasks. Originality. The present work combined the AFT2FTSMC algorithm in order to give robust controllers for trajectory tracking of manipulator’s robot in presence parameters uncertainties, external disturbance, and faults. We use an adaptive fuzzy logic system to estimate the robot’s time-varying, nonlinear, and unfamiliar dynamics. A strong adaptive term is created to counteract actuator defects and approximation errors while also guaranteeing the convergence and stability of the entire robot control system. Novelty. The implemented controller effectively mitigates the chattering problem while maintaining the tracking precision and robustness of the system. The stability analysis has been conducted using the Lyapunov approach. Results. Numerical simulation and capability comparison with other control strategies show the effectiveness of the developed control algorithm. References 53, table 1, figures 8.

Adaptive backstepping control for electro-hydraulic servo system in extension sleeve press-fitting process of bearing pressing machine

In the process of extension sleeve press-fitting of the bearing pressing machine, electro-hydraulic servo system of valve-controlled symmetrical cylinder (ESSVSC) is an important and critical control module, and its performance has a significant impact on the working accuracy of such equipment. People have proposed various related control algorithms. However, external disturbances and unmodeled dynamic factors are often inevitable in practical work and may produce significant influences on the performance of the control system. Existing researches on this issue remains to be enriched and the performance of related algorithms still needs further improvement. An adaptive backstepping control algorithm (ABCA) is proposed in this paper. Firstly, a mathematical model of the ESSVSC is established which takes into account external disturbances and unmodeled dynamic factors. Secondly, the adaptive backstepping controller is designed by using the backstepping algorithm, and the control law and adaptive parameter estimation law are given. The stability of the control system is also proved. The analysis results of the numerical examples show that the proposed algorithm can effectively suppress the adverse effects of typical external disturbances and unmodeled dynamic factors and maintain good control accuracy. The output displacement error of the proposed algorithm is smaller and the tracking performance is better. The control accuracy of our algorithm is improved by 48.33% and 94.76% compared to BSMCA and PID, respectively, which illustrates the rationality of the established mathematical model and the effectiveness of the algorithm. This work is expected to provide useful reference for improving the control performance during the pressing process of the extension cylinder and the algorithm design of ESSVSC.

THERMAL COMFORT PREDICTION USING SVM AND RANDOM FOREST MODEL

Predicting thermal comfort is crucial for optimizing built environments for human habitation, as it impacts health, productivity, and overall well-being. To address this imperative, interdisciplinary collaboration among architects, engineers, psychologists, and data scientists is needed to develop reliable predictive models that anticipate occupants’ thermal comfort preferences across diverse environmental conditions and architectural designs. Traditional methods rely on human comfort models, which can be subjective and time-consuming. Machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest (RF), have been utilized to predict thermal comfort with high accuracy and efficiency. The Internet of Things (IoT) is revolutionizing the building management systems industry, with adaptive control algorithms and modular architectures exploring the IoT paradigm. This paper discusses the use of SVM and Random Forest algorithms for predicting thermal comfort in buildings, exploring their strengths and weaknesses and comparing their performance in different scenarios. The study analyzed a dataset of thermal comfort data, filtering by quantity and removing outliers. The data was split into 80% for training and 20% for testing. The study used SVM and Random Forest models to capture complex relationships between environmental parameters and thermal comfort responses. The results showed that the IQR method provided 3-4% accuracy, while the reducing label values method offered 20-23% accuracy. The study also tested the parameters of the models, resulting in a 2-4% difference between the two models. The study concluded that Random Forest appears more stable than SVM and plans to add new features to improve accuracy.

A comprehensive study of actuation, perception and control technologies for dexterous hands

Humanoid five-fingered dexterous hands, known for their agility and versatility, have extensive applications in industrial automation and healthcare fields. This paper reviews advancements in actuation, perception, and control technologies for dexterous hands. Electric, hydraulic, and pneumatic actuators are compared regarding power density, precision, and efficiency. The review also covers internal and external perception technologies, including position sensing and force feedback, which are critical for ensuring accurate and stable manipulation. Control methods such as position, force, and hybrid control are analyzed for their effectiveness in handling multi-degree-of-freedom tasks. Despite significant progress, challenges remain in improving precision, energy efficiency, and real-time response. This review highlights these challenges and suggests future directions, including developing adaptive control algorithms and advanced materials. The findings provide a reference for researchers and engineers, contributing to the development of more advanced dexterous hands.

Reduced-order adaptive synchronization in a chaotic neural network with parameter mismatch: a dynamical system versus machine learning approach

AbstractThis paper addresses the reduced-order synchronization problem between two chaotic memristive Hindmarsh-Rose (HR) neurons of different orders using two distinct methods. The first method employs the Lyapunov active control technique. Through this technique, we develop appropriate control functions to synchronize a 4D chaotic HR neuron (response system) with the canonical projection of a 5D chaotic HR neuron (drive system). Numerical simulations are provided to demonstrate the effectiveness of this approach. The second method is data-driven and leverages a machine learning-based control technique. Our technique utilizes a heuristic combination of reservoir computing (RC) algorithms, incorporating reservoir observer, online control, and online predictive control algorithms. We anticipate our effective heuristic RC adaptive control algorithm to guide the development of more formally structured and systematic, data-driven RC control approaches to chaotic synchronization problems and to inspire more data-driven neuromorphic methods for controlling and achieving synchronization in chaotic neural networks in vivo.

ANALYSIS OF THE FUNCTIONAL POSSIBILITIES OF AUTOMATED DOSING SYSTEMS FOR FINISHED MEDICINES

In many modern productions, it is necessary to implement the process of dosing certain materials, liquids, reagents, etc. When automating such lines, the parameters of the dosing process can be very important. For example, in automated lines for dosing liquids into containers, the dosing process determines the efficiency of the entire production. The dosing process is essential in producing finished medicines (FM). Particularly, the advantages and disadvantages of methods of vacuum, syringe, weight dosing of finished medicines, and dosing with the use of flow meters were analyzed. A dosing machine for FM is one of the components of finished medicines production. Therefore, the overall productivity of the production facilities depends on the dosing parameters, particularly the accuracy and time of filling the container. The quantitative output of the produced products will depend on the accuracy of dosing, which affects the economic indicators of the enterprise. In this paper, the authors have analyzed the dosing methods, features of the dosing process and automated FM dosing systems. Based on the analysis, we proposed a weight dosing method for dosing pharmaceutical solutions into vials with 100 - 300 ml volume. It has been established that this method has many advantages, particularly high dosing accuracy, relatively low cost of implementation, and high equipment productivity. We carried out an analysis of the stages of the dosing process using the weight-filling method and proposed to develop an algorithm for choosing the duration of each stage based on the criteria of achieving the minimum filling time and the minimum filling error. We also proposed implementing an adaptive control algorithm with self-learning elements based on previous dosing cycles to control the dosing process. The proposed algorithm aims to optimize the dosing process parameters and achieve the best possible quality indicators, namely high dosing accuracy and minimum dosing time.

Algorithm for adaptive control of turning process using neural network technology

The problem of optimal adaptive control of the turning process taking into account the wear of the cutting tool is considered. The optimality criterion is the volume of material cut during processing. The constraint of the inequality type is the constraint on the value of surface roughness. Neural network approximators are built to take into account the dependence of roughness on cutting modes and tool wear. The initial data are time signals of tool vibration acceleration during cutting. An effective algorithm for solving the adaptive control problem is presented, which ensures the selection of optimal cutting modes taking into account incoming signals about tool vibration.

Momentum-Based Adaptive Laws for Identification and Control

In this paper, we develop momentum-based adaptive update laws for parameter identification and control to improve parameter estimation error convergence and control system performance for uncertain dynamical systems. Specifically, we introduce three novel continuous-time, momentum-based adaptive estimation and control algorithms and evaluate their effectiveness via several numerical examples. Our proposed adaptive architectures show faster parameter convergence rates as compared to the classical gradient descent and model reference adaptive control methods.

Pengembangan Sistem Pengendalian Lampu Otomatis Berbasis Deteksi Gerak dan Optimalisasi Konsumsi Energi Menggunakan Pendekatan IoT

This research aims to design and develop a motion detection-based automatic light control system with a light intensity setting feature using IoT technology. The system utilizes a combination of PIR sensor, MOSFET module, and Arduino Uno microcontroller to control lighting based on the presence and position of the user. With an adaptive control algorithm, the lamp can dynamically adjust the lighting intensity to several levels as needed. Test results show that the system successfully reduces energy consumption optimally without reducing user comfort. The implementation of this technology has great potential in supporting energy efficiency in public areas, such as parks, office hallways, and sidewalks.

Real-time economic following velocity and gear planning for commercial vehicles based on the bi-level optimization

ABSTRACT Current Predictive Adaptive Cruise Control (PACC) systems pose significant challenges. These include poor collaborative optimisation between economic velocity and the powertrain system, conservative energy-saving strategies and underutilisation of map data due to real-time constraints. A computationally efficient collaborative optimisation method is proposed for economic following velocity and gear shifting based on Bi-level optimisation theory. Based on the Bi-level theory, economic following velocity and gear shifting are decoupled and hierarchically planned. The lower-level subproblem constructs the objective function based on a dynamic-weight safety distance model within the Model Predictive Control (MPC) framework, solving the economic following velocity by the Continuous Generalized Minimal Residual Method (C/GMRES). The upper-level subproblem solves the economic gear shifting by the optimal control law based on the economic following velocity. Finally, comparative experiments are conducted between the proposed PACC algorithm, a standard Adaptive Cruise Control (ACC) algorithm, and a benchmark Dynamic Programming-Model Predictive Control (DP-MPC) PACC algorithm. The results show that the proposed PACC algorithm reduces fuel consumption by approximately 5.76% compared to the ACC. Compared to the benchmark algorithm, the proposed PACC algorithm also demonstrates significantly improved computational efficiency while achieving comparable energy savings.

Control of Parallel Quadruped Robots Based on Adaptive Dynamic Programming Control

With the rapid development of robotics technology, quadruped robots have shown significant potential in navigating complex terrains due to their excellent stability and adaptability. This paper proposes an adaptive dynamic programming control method based on policy iteration, aimed at improving the motion performance and autonomous adaptability of parallel quadruped robots in unknown environments. First, the study establishes a kinematic model of the robot and performs inverse kinematics calculations to determine the angular functions for each joint of the robot’s legs. To improve the robot’s mobility on challenging terrains, we design an optimal tracking controller based on Generalized Policy Iteration (GPI). This approach reduces the model’s dependency on strict requirements and is applied to the control of quadruped robots. Finally, kinematic simulations are conducted based on pre-planned robot gaits. In addition, experiments are then conducted based on the simulation results. The results of simulation experiments indicate that the quadruped robot, under the adaptive optimal control algorithm, can achieve smooth walking on complex terrains, verifying the rationality and effectiveness of the parallel quadruped robot in handling such conditions. The experimental results further demonstrate that this strategy significantly improves the stability and robustness of the robot across various terrains.

Fuzzy Adaptive Event-Triggered Consensus Control for Nonlinear Multiagent Systems Under Jointly Connected Switching Networks.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

A Modified Dynamic Movement Primitive Algorithm for Adaptive Gait Control of a Lower Limb Exoskeleton

A Modified Dynamic Movement Primitive Algorithm for Adaptive Gait Control of a Lower Limb Exoskeleton

EXPLORING ELECTROMECHANICAL SYSTEMS IN THE DEVELOPMENT OF NEXT-GENERATION ELECTRIC POWERTRAINS

In the rapidly evolving electric vehicle industry, the study of electromechanical systems in next-generation electric powertrains is of particular relevance. This is driven by the need to enhance energy efficiency, reduce costs, and improve the reliability of vehicles. The article examines the role of innovative materials, advanced design methods, and control algorithms in creating high-efficiency and reliable electric powertrains. Specifically, the impact of nanomaterials such as graphene and carbon nanotubes on reducing motor weight and increasing heat dissipation is analyzed, contributing to improved efficiency and extended driving range. The adoption of additive manufacturing (3D printing) and digital twins accelerates prototyping and component optimization in electric powertrains. Adaptive control algorithms ensure optimal system performance in real-time, enhancing overall efficiency and reliability. Research methods included the analysis of modern materials and technologies, prototyping using additive manufacturing, and the implementation of predictive and adaptive control algorithms. The results demonstrate significant improvements in the performance of electric powertrains due to the use of innovative materials and cutting-edge design technologies. Conclusions confirm that the application of advanced materials and adaptive control algorithms is critical for ensuring the sustainable development of the electric vehicle industry. Future research prospects focus on further refining materials and technologies to enhance scalability and reduce production costs, making electric powertrains more accessible and efficient.

Hybrid adaptive control for vibration reduction in the floating raft system based on FxLMS

To enhance the real-time performance of multi-frequency line spectrum vibration control within the floating raft system and minimize the vibration of the floating raft, a hybrid adaptive active vibration control algorithm based on the FxLMS algorithm is introduced. Focusing on the bottom degree of freedom in the floating raft system, a horizontal two-degree of freedom system is established. Initially, the dynamic model of this system is analyzed; subsequently, the hybrid adaptive control algorithm is analyzed theoretically, and its stability is verified by simulation under normal condition, shock disturbance condition, and random signal disturbance condition. The results reveal that the average control effects of feedforward control across each line spectrum under these conditions are −9.55 dB, −10.88 dB, and −9.91 dB, respectively. In contrast, hybrid adaptive control demonstrates stronger performance with average control effects of −23.19 dB, −21.65 dB, and −21.48 dB. Lastly, an experimental platform was established to assess the performance of the proposed control algorithm. The experimental results demonstrate that the hybrid adaptive control successfully mitigates vibrations arising from multiple frequency spectra. The hybrid adaptive control achieves average control effects of −14.77 dB, −15.13 dB, and −14.63 dB across the three conditions, demonstrating superior control efficacy.

An Adaptive Cruise Control Algorithm Based on DDPG Algorithm Based on Deep Reinforcement Learning Under Variable Acceleration Conditions

Adaptive cruise control (ACC) dynamically regulates a vehicle's speed to preserve a secure gap from the preceding vehicle, enhancing road safety. In this study, ACC is examined through the lens of deep reinforcement learning, with a focus on the Deep Deterministic Policy Gradient (DDPG) technique. The reward function takes into account the speed error, and two modesspeed control and distance controlare implemented. The proposed ACC strategy is trained and validated through simulations on the MATLAB/Simulink platform. The experimental results indicate that the reward function converges rapidly, confirming the suitability of the DDPG algorithm for automotive ACC research.

Tracking control strategy of tendon driven robotic arm under adaptive neural network

IntroductionWith the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention.MethodsTo improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed.Results and DiscussionThe research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control.

Real-time position path smoothing for robotic manipulators by constructing Composite Trajectory Spline

Real-time position path smoothing for robotic manipulators by constructing Composite Trajectory Spline