Adaptive Control Algorithm Research Articles

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.

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

Aiming at smoothing discontinuous corners in robotic tool paths, a novel spline named “Composite Trajectory Spline”(CT-Spline) is proposed in this paper. Based on CT-Spline, the real-time position path smoothing for robotic manipulators is realized. CT-Spline enables analytical trajectory planning and interpolation without curve length computation, significantly enhancing trajectory generation efficiency and avoiding numerical errors. By directly controlling resultant trajectory acceleration, CT-Spline avoids decomposing it into tangential and normal accelerations, ensuring adherence to the maximum acceleration constraint while fully utilizing acceleration potential. The geometric shape of CT-Spline is determined by three control points and a trajectory model. This paper further develops C1 and C3 continuous CT-Splines based on two different trajectory models, combined with a velocity look-ahead algorithm to achieve real-time local smoothing of path corners. Additionally, an adaptive control algorithm of smoothing error is introduced to dynamically maximize trajectory velocity. Simulations and experiments validate the effectiveness of CT-Spline, and demonstrate that the proposed C3 continuous trajectory smoothing method has several advantages over the method based on Pythagorean-hodograph splines in terms of kinematic constraints, velocity, smoothing errors, and real-time performance.

Anti-Jamming Power Control Algorithm for Wireless Communication Systems Based on MPC

In complex electromagnetic environments, wireless communication system reliability can be compromised by various types of jamming. To improve reliability in the presence of malicious jamming, this paper introduces an anti-jamming power control algorithm for wireless communication systems, grounded in model predictive control (MPC) principles. The algorithm models the Yescommunication system as a linear control system, using the current signal-to-jamming-and-noise ratio (SJNR) to predict future system states and transmission power over a defined time horizon. Only the first element of the optimal control sequence is then applied to manage system power. Simulation results indicate that, compared to traditional adaptive power control algorithms, the proposed algorithm responds more swiftly to jamming variations, significantly enhancing communication reliability in high-jamming environments.

PID control algorithm based on multistrategy enhanced dung beetle optimizer and back propagation neural network for DC motor control.

Traditional Proportional-Integral-Derivative (PID) control systems often encounter challenges related to nonlinearity and time-variability. Original dung beetle optimizer (DBO) offers fast convergence and strong local exploitation capabilities. However, they are limited by poor exploration capabilities, imbalance between exploration and exploitation phases, and insufficient precision in global search. This paper proposes a novel adaptive PID control algorithm based on enhanced dung beetle optimizer (EDBO) and back propagation neural network (BPNN). Firstly, the diversity of exploration is increased by incorporating a merit-oriented mechanism into the rolling behavior. Then, a sine learning factor is introduced to balance the global exploration and local exploitation capabilities. Additionally, a dynamic spiral search strategy and adaptive [Formula: see text]-distribution disturbance are presented to enhance search precision and global search capability. The BPNN is employed to fine-tune both PID and network parameters, leveraging its powerful generalization and learning ability to model nonlinear system dynamics. In the simplified motor experiments, the proposed controller achieved the lowest overshoot (0.5%) and the shortest response time (0.012s), with a settling time of 0.02s and a steady-state error of just 0.0010. In another set of experiments, the proposed controller recorded an overshoot and response time of 0.7% and 0.0010s, across five DC motor tests. These results demonstrate the proposed adaptive PID control algorithm has superior performance in optimizing control system parameters, as well as improving system robustness and stability.

Adaptive algorithms for drone flight control under communication constraints and information incompleteness

Abstract With the rapid increase in the use of drones in various applications, including commercial and governmental, and the increasing probability of communication failures and contingencies, research becomes critical to ensure the safety and efficiency of their operations. The aim of this research is to develop adaptive drone flight control algorithms capable of operating effectively under conditions of limited communication and incomplete information to ensure reliable and safe autonomous operation of these systems. The applied methods include computer modelling and simulation, analytical, statistical, functional, deductive and descriptive methods. The study found that the use of performance evaluation methods for complex systems enables the identification of safety and performance criteria for drones, and drone flight control provides basic principles and methods that can be adapted for drones, including autopiloting and navigation. In addition, analyses of satellite communication and navigation prove the need to consider the limitations of this technology when developing drone control algorithms. The combination of these techniques allows for more robust and adaptive drone control systems that can function effectively in complex environments such as communication limitations and incomplete information. Additionally, it was found that the integration of adaptive control algorithms based on these methods allows drones to effectively adapt to variable environmental conditions and make decisions quickly even when communication is lost or information is limited.

Advances in flight control systems for modern commercial aircraft

Abstract. In consequence of the accelerated evolution of aviation technology, the flight control systems of commercial aircraft have undergone substantial technological advancement and system optimization. This paper presents a comprehensive analysis of the recent advancements and future trends in flight control systems for commercial aircraft. The fundamental concepts and historical evolution of flight control systems are delineated, with a particular emphasis on the evolution of autopilot systems and the technological advancements of flight control systems (FCS). Furthermore, it examines innovations in flight control algorithms, including adaptive control and intelligent algorithms, and investigates the impact of fault-tolerant design on flight safety. In terms of system integration, the integration of flight control systems with navigation and communication systems, as well as performance enhancements resulting from system optimization, is explored. In addition, it examines the potential impact of intelligence and networking on future flight control systems, with a particular focus on the prospects for the application of artificial intelligence, quantum computing, and new material technologies. The synthesis of these areas provides a systematic understanding of the technological evolution and future development of flight control systems for commercial aircraft.

Shaking Table Test Studies on Adaptive-Passive Variable Frequency Gas-Spring DVA for Structural Seismic Response Mitigation

The control performance of a passive dynamic vibration absorber (DVA) is known to be sensitive to its frequency deviation. This study upgrades the conventional device and proposes an adaptive-passive variable frequency gas-spring DVA (APVF-GSDVA) for controlling the structural seismic response, while an adaptive control algorithm is developed that requires only a single input signal. The DVA’s vibration frequency is precisely modulated by adjusting the gas pressure in the gas-spring, aligning it with the inherent frequency of the multi-degree-of-freedom (MDOF) structure. The working principle and control algorithm of the APVF system is introduced and derived in detail, the corresponding gas-spring DVA and implementation are designed, and the configuration of the entire control system is realized. An adaptive variable frequency test scheme is designed and the frequency retuning function of the prototype APVF-GSDVA device installed on an MDOF structure is experimentally validated. The seismic response control effectiveness of the retuned DVA is demonstrated by comparing the detuned DVA, highlighting the necessity of implementing adaptive frequency adjustment of the gas-spring DVA. Experimental findings demonstrate that APVF-GSDVA accurately identifies the inherent frequency of the MDOF structure in ambient excitation, it then adjusts the pressure of the gas-spring according to the frequency-pressure relationship, optimally retuning the DVA. A retuned GSDVA can effectively suppress the structural seismic responses, compared to a detuned DVA, its control effectiveness on peak and RMS responses can be improved by a maximum of nearly 13.5% and 53.3% at a frequency deviation of 10%. Furthermore, results from two “detuning-retuning” test paths with distinct seismic excitations indicate that the retuned DVA exhibits superior control effectiveness, underscoring the significance of the adaptive frequency adjustment function in passive DVAs.

Fuzzy control system design for landscape lamp of environment art based on multi-function

With the gradual satisfaction of material needs, people began to pay more attention to the pursuit of the spiritual level, so the environmental art landscape light came into being. At present, most of the landscape lamp controllers are realised through the full hardware circuit, and there are some defects such as troublesome installation, high maintenance cost and poor control effect. In view of this situation, this study uses the fuzzy control algorithm to establish the fuzzy control system of environmental art landscape lamps, aiming to optimise the accuracy and effect of light control. First, the composition structure and lighting scene mode of the environmental art landscape lamp are researched, and then the intelligent light control system is designed by the adaptive fuzzy control algorithm. The results showed that the control accuracy of adaptive fuzzy control algorithm was above 95, and the running time of the system was within 1 min. In addition, both experts and users scored above 9.5 in terms of satisfaction with the control system. To sum up, the light fuzzy control system designed in this study has good control performance and certain practical application value.

Design of PV Battery Hybrid Inverter.

This research explores a hybrid renewable energy system integrating solar and additional renewable sources with energy storage for a resilient, eco-friendly power solution. Through adaptive control algorithms, the system autonomously optimizes energy flow based on environmental and grid conditions, ensuring uninterrupted power. Simulations under varying demand scenarios demonstrate enhanced reliability and a reduced carbon footprint. The findings highlight the system's potential as a sustainable model for future energy infrastructure.

Adaptive Path-Tracking Control Algorithm for Autonomous Mobility Based on Recursive Least Squares with External Condition and Covariance Self-Tuning

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications.

Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×10−15 and 2.30×10−15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.

Redundancy resolution of a variable base frame of a 3-DoF cable-driven serial chain by using an adaptive neuro-fuzzy controller

This paper presents a novel approach using adaptive neuro-fuzzy techniques to design controllers for planar cable-driven serial chain robots with variable configurations. The approach consists of two key components: (1) deriving dynamic models for cable-driven serial chain robots which are independent of their structure, and (2) adaptively determining the optimal cable connection points. Traditional methods face challenges in obtaining accurate dynamic equations for cable-driven serial chain robots with high degrees-of-freedom, hence neural networks are employed to estimate the model. In order to handle the variability in cable connection points, adaptive fuzzy methods are utilized. The proposed adaptive neuro-fuzzy controller algorithm introduces two new indices, namely cost-of-redundancy and degree-of-redundancy, to effectively address redundancy concerns. Additionally, the algorithm efficiently reduces the search space for finding the optimal configuration. Simulation results for a planar 3 degrees-of-freedom cable-driven serial chain robot using this algorithm showcase a noteworthy 42% reduction in cost-of-redundancy and an impressive 53.125% reduction in search space.

Neural Network Adaptive Inverse Control of Flexible Joint Space Manipulator Considering the Influence of Gravity.

With the aim of correcting the problem of trajectory tracking control of a flexible joint space manipulator in environments with different gravity, a neural network adaptive inverse control algorithm based on singular perturbation theory is proposed to resist the disturbance caused by system uncertainty. Firstly, the dynamic model of a flexible joint space manipulator with the influence of gravity is established, and then the system is divided into a fast subsystem and a slow subsystem using singular perturbation theory. The velocity feedback control rate is designed for the fast subsystem to suppress the elastic vibration caused by the joint flexibility. For the slow subsystem, the uncertain term and known term are separated by the inverse control algorithm, where the uncertain term is approximated online by the RBF neural network, and the robust control rate is designed to compensate for the approximation error. The simulation results show that the control method can not only effectively reduce the high-frequency vibration caused by the flexible joint but also resist the system disturbance so that a good track control effect is achieved.

Cluster formation tracking of networked perturbed robotic systems via hierarchical fixed-time neural adaptive approach

This paper investigates the fixed-time cluster formation tracking (CFT) problem for networked perturbed robotic systems (NPRSs) under directed graph information interaction, considering parametric uncertainties, external perturbations, and actuator input deadzone. To address this complex problem, a novel hierarchical fixed-time neural adaptive control algorithm is proposed based on a hierarchical fixed-time framework and a neural adaptive control strategy. The objective of this study is to achieve accurate CFT of NPRSs within a fixed time. Specifically, we design a distributed observer algorithm to estimate the states of the virtual leader within a fixed time accurately. By using these observers, a neural adaptive fixed-time controller is developed in the local control layer to ensure rapid and reliable system performance. Through the use of the Lyapunov argument, we derive sufficient conditions on the control parameters to guarantee the fixed-time stability of NPRSs. The theoretical results are eventually validated through numerical simulations, demonstrating the effectiveness and robustness of the proposed approach.

Fuzzy adaptive asynchronous sampled-data consensus control for nonlinear multi-agent systems under DoS attacks

This article investigates the problem of fuzzy adaptive resilient asynchronous sampled-data consensus control (SDCC) for a class of uncertain nonlinear strict-feedback multi-agent systems (MASs) under denial-of-service (DoS) attacks and with a directed graph. Firstly, the upper bounds on the sampling intervals and a distributed sampled-data observer are proposed to estimate the leader's output and its high-order derivatives under DoS attacks. Then, the sufficient conditions to ensure the asymptotic convergence of the designed distributed sampled-data observer are established. Based on the distributed sampled-data observer and backstepping control design technique, a distributed fuzzy adaptive resilient control algorithm is developed. It is proved that the proposed control scheme can guarantee that the controlled MASs are stable, and the consensus errors converge to a small neighbourhood of zero. Finally, a simulation example on heterogeneous vehicular platoons verifies the feasibility of the proposed control scheme.