Improve Control Performance Research Articles

A Distributed Actor‐Critic Learning Approach for Affine Formation Control of Multi‐Robots With Unknown Dynamics

ABSTRACTFormation maneuverability is particularly important for multi‐robots (MRs), especially when the robots are operating cooperatively in complex and dynamic environments. Although various methods have been developed for affine formation, it is still a difficult problem to design an affine formation controller for MRs with unknown dynamics. In this paper, a distributed actor‐critic learning approach (DACL) in a look‐ahead rollout manner is proposed for the affine formation of MRs under local communication, which improves the online learning efficiency. In the proposed approach, a distributed data‐driven online optimization mechanism is designed via the sparse kernel technique to solve the near‐optimal affine formation control issue of MRs with unknown dynamics as well as improve control performance. The unknown dynamics of MRs are learned offline based on precollected input‐output datasets, and the sparse kernel‐based approach is employed to increase the feature representation capability of the samples. Then, the proposed distributed online actor‐critic algorithm for each robot in the formation includes two neural networks, which are utilized to approximate the costate functions and the near‐optimal policies. Moreover, the convergence analysis of the proposed approach has been conducted. Finally, numerical simulation and KKSwarm‐based experiment studies are performed to verify the effectiveness of the proposed approach.

Benefits of Using Functional Joint Coordinate Systems in In Vitro Knee Testing.

To measure knee joint kinematics, coordinate systems (CS) must be assigned to the tibia and femur. Functional CS have been shown to be more reproducible than Anatomical. This study aims to quantify the benefits of using Functional CS in in vitro testing. Seven cadaveric knee joints were loaded in a 6-Degree of Freedom (DOF) joint simulator. Anatomical CS were established for each joint and Functional CS were calculated based on joint kinematics during passive motion. Loading profiles were applied to the knee joints using different CS definitions. Resulting kinematics and kinetics were obtained to quantify the 1) reduction in intra-knee kinematic response variation, 2) reduction in kinematic cross-talk, 3) reduction in inter-knee kinematic response variation, and 4) improvement in force control performance, when using Functional CS compared to Anatomical. Functional CS, compared to Anatomical, 1) significantly reduced intra-knee kinematic response variation across 12 combined loading conditions for nearly all DOF, 2) significantly reduced kinematic cross-talk during anterior-posterior, varus-valgus and internal-external rotation laxity testing across many DOF, 3) significantly reduced inter-knee kinematic response variation for all DOFs over a gait profile and combined loading conditions, and 4) significantly improved Anterior-Posterior and Varus-Valgus force/torque control performance during dynamic loading profiles. The advantage of using Functional CS for in vitro testing has been demonstrated across all considered domains. Functional CS should be used when performing in vitro knee joint testing.

Fractional Order Adaptive Fixed-Time Sliding Mode Controller for Synchronization of Fractional Order Chaotic Permanent Magnet Synchronous Motors

This paper presents a fractional-order adaptive fixed-time sliding mode controller for the synchronization of chaotic dynamics in permanent magnet synchronous motors (PMSMs). PMSMs, commonly used in electric vehicles, robotics, and aerospace, are prone to chaotic behavior under parameter variations and external disturbances, which can degrade performance and stability. Existing control strategies, such as conventional sliding mode control (SMC) and fractional-order controllers, have limitations, including chattering, slow convergence, and sensitivity to uncertainties. The proposed controller integrates fractional calculus into the sliding mode framework to improve control performance by accounting for the memory effects of PMSM dynamics. The controller ensures fixed-time convergence, guaranteeing that the system reaches the desired state within a fixed-time, regardless of initial conditions. Additionally, an adaptive mechanism adjusts the control parameters online, providing robustness against disturbances and parameter uncertainties. Simulation results demonstrate the superior performance of the proposed controller compared to existing methods, showing faster convergence, improved stability, and reduced chattering. The proposed controller proves effective in both synchronization and control scenarios, making it a promising solution for chaotic suppression in PMSMs across various operating conditions.

Control Performance Improvement of PMSM Direct-Drive Servo System at Low Speeds

Control Performance Improvement of PMSM Direct-Drive Servo System at Low Speeds

Camera-based control system of a planar cable-driven parallel robot intended for functional rehabilitation

Abstract This paper investigates a closed-loop visual servo control scheme for controlling the position of a fully constrained cable-driven parallel robot (CDPR) designed for functional rehabilitation tasks. The control system incorporates real-time position correction using an Intel RealSense camera. Our CDPR features four cables exiting from pulleys, driven by AC servomotors, to move the moving platform (MP). The focus of this work is the development of a control scheme for a closed-loop visual servoing system utilizing depth/RGB images. The developed algorithm uses this data to determine the actual Cartesian position of the MP, which is then compared to the desired position to calculate the required Cartesian displacement. This displacement is fed into the inverse kinematic model to generate the servomotor commands. Three types of trajectories (circular, square, and triangular) are used to test the controller’s compliance with its position. Compared to the open-loop control of the robot, the new control system increases positional accuracy and effectively handles cable behavior, various perturbations, and modeling errors. The obtained results showed significant improvements in control performance, notably reduced root mean square error and maximal error in terms of position.

Modeling and Control of a Coaxial Parallel Spherical Mechanism

This paper presents a method for modeling and controlling a coaxial parallel spherical mechanism. This mechanism consists of elements connected to allow them to move relative to each other in a parallel and coaxial manner, forming a spherical structure capable of rotating and moving in multiple directions. The operation of the mechanism is controlled by a high-order sliding controller to improve control performance, reduce chattering phenomena, and mitigate disturbances during operation. The system’s stability using the suggested control approach is demonstrated and analyzed in detail through experimental validation on a real-time controller, TMS320F28379D.

Adaptive Neural Network-Based PID Controller Design for Velocity Control of an Internal Combustion Engine Using Back Propagation Technique

Precise velocity control in internal combustion engines (ICEs) is essential for optimizing performance, improving fuel efficiency, and minimizing emissions. However, the nonlinear dynamics and inherent uncertainties within ICE systems present substantial challenges for traditional control methods. In this paper, we propose an adaptive control strategy that integrates a Proportional-Integral-Derivative (PID) controller with Back Propagation Neural Networks (BPNNs) to effectively address these complexities. The proposed controller architecture consists of two key components: a BPNN to estimate modelling uncertainties, such as unknown friction and external disturbances affecting the ICE, and a primary PID controller responsible for velocity regulation. The BPNN functions as a dynamic estimator, continuously learning and adapting to changes in system dynamics, thereby enhancing the robustness and adaptability of the control system. By accurately capturing the nonlinearities and uncertainties inherent in ICEs, the BPNN contributes to improved control performance and system stability. To validate the effectiveness of the proposed approach, extensive numerical simulations are performed using MATLAB Simulink. These simulations encompass a range of operating conditions and scenarios to thoroughly evaluate the controller's performance. Additionally, the proposed method is compared to conventional PID control techniques found in the literature, with a focus on robustness, tracking accuracy, and disturbance rejection. The results indicate that the adaptive PID controller, incorporating BPNNs, outperforms traditional PID methods, delivering superior velocity regulation and disturbance rejection. Furthermore, the proposed approach demonstrates significant potential for real-world applications in ICE systems, providing enhanced control performance and efficiency. This study advances the field of control engineering by introducing an innovative adaptive control strategy tailored specifically for velocity control in internal combustion engines. Leveraging the capabilities of BPNNs to address uncertainties, this approach contributes to improved system performance and offers a promising direction for future advancements in engine control technologies.

Review on Advanced Model Predictive Control Technologies for High-Power Converters and Industrial Drives

Model predictive control (MPC) has emerged as a very popular control technology. This paper presents and discusses the major applications and recent research advances of MPC in the field of high-power converters and industrial drives. The classic concepts and implementation principles of MPC for power electronic control are reviewed, followed by the classification and application schemes of MPC in electrical drive systems. This paper discusses typical MPC techniques. Based on the results of extensive discussions on the latest research, this paper analyzes some key application issues of MPC in industrial control solutions, including the optimization and elimination of weighting factors, improvement of steady-state performance, and robustness improvement. It also covers research trends in advanced MPC. By reviewing advanced MPC solutions, this paper contributes to enhancing the performance in applications of high-power converters and industrial drives, and providing multiple perspectives to improve control performance of MPC. The presented content aims to provide readers with a precise overview and facilitate their future research in this attractive area.

Active Vibration Control of a Cantilever Beam Structure Using Pure Deep Learning and PID with Deep Learning-Based Tuning

Vibration is a major problem that can cause structures to wear out prematurely and even fail. Smart structures are a promising solution to this problem because they can be equipped with actuators, sensors, and controllers to reduce or eliminate vibration. The primary objective of this paper is to explore and compare two deep learning-based approaches for vibration control in cantilever beams. The first approach involves the direct application of deep learning techniques, specifically multi-layer neural networks and RNNs, to control the beam’s dynamic behavior. The second approach integrates deep learning into the tuning process of a PID controller, optimizing its parameters for improved control performance. To activate the structure, two different input signals are used, an impulse signal at time zero and a random one. Through this comparative analysis, the paper aims to evaluate the effectiveness, strengths, and limitations of each method, offering insights into their potential applications in the field of smart structure control.

Design and Simulation of Steering Control Strategy for Four-Wheel Steering Hillside Tractor

Steering control is vital for hillside tractors, and four-wheel-steering technology significantly enhances stability and flexibility. However, existing methods often overlook the slope–stability relationship. In order to address this issue, considering the impact of tractor wheels on the steering characteristics, a nonlinear three-degree-of-freedom dynamic model based on the basic requirements within a 15° range of slope angles was derived. Subsequently, a four-wheel steering control strategy for hillside tractors was designed based on this model, incorporating a fuzzy PID control algorithm. The dynamics model was validated on the high-fidelity Carsim/Simulink co-simulation platform, and relevant experiments were conducted in Matlab/Simulink. Results showed that fuzzy PID control reduced the yaw rate’s average settling time from 0.36 s to 0.1 s and the response value from 0.28 rad/s to 0.038 rad/s. In addition, the lateral velocity and sideslip angle responses closely matched ideal values. Thus, the tractor with four-wheel steering under fuzzy PID control exhibited improved wheel angle flexibility and higher tracking accuracy. Finally, hardware-in-the-loop-experiments were conducted, confirming the algorithm’s effectiveness in ensuring stability and meeting the requirements of semi-physical simulation scenarios. This research provides a foundation for potential applications in tractor manufacturing to improve control performance on hilly terrains.

The relationship between acute aerobic exercise and inhibitory control in college students: The impact of physical and cognitive engagement.

Evidence suggests that acute exercise is an effective way for improving inhibition control, however, the effect of different types of Acute Aerobic Exercise and Inhibitory Control (IC) remains unclear. Using a crossover design, 25 participants (M = 20.20year, SD=0.91) completed 20 min of interventions at 1) moderate intensity exercise, 2) moderate intensity exercise with high cognitive demand (high cognitive-demand exercise), 3) moderate intensity exercise with high physical demand (high physical-demand exercise), and 4) rest condition (low physical and cognitive demand) in a counterbalanced way. Flanker task was completed before and after each intervention to evaluate their inhibitory control ability. The four interventions significantly reduce reaction times for both congruent and incongruent trials with Flanker task (all P < 0.05). Compared to acute moderate intensity exercise, high physical-demand exercise induced a greater reduction in reaction times for both trial types, whereas high cognitive-demand exercise led to faster reaction times in incongruent trials. Although each intervention effectively enhanced inhibitory control, the exercise demanding high cognitive and physical effort contributed to a more pronounced improvement in inhibitory control performance. This indicates that both increased physical and cognitive engagement can significantly improve the level of inhibitory control in young adults.

Refinement of Control Strategies for Wheel-Fan Systems in High-Speed Air-Floating Vehicles Operating in Atmospheric Pressure Pipelines

This study explored the optimization of control systems for atmospheric pipeline air-floating vehicles traveling at ground level by introducing a novel composite wheel-fan system that integrates both wheels and fans. To evaluate the control impedance, the system simulates road conditions like inclines, uneven surfaces, and obstacles by using fixed, random, and high torque settings. The hub motor of the wheel fan is managed through three distinct algorithms: PID, fuzzy PID, and the backpropagation neural network (BP). Each algorithm’s control strategy is outlined, and tracking experiments were conducted across straight, circular, and curved trajectories. Analysis of these experiments supports a hybrid control approach: initiating with fuzzy PID, employing the PID algorithm on straight paths, and utilizing the BP neural network for sinusoidal and circular paths. The adaptive capacity of the BP neural network suggests its potential to eventually supplant the PID algorithm in straight path scenarios over extended testing and operation, ensuring improved control performance.

Adaptive compensation using long short‐term memory networks for improved control performance in real‐time hybrid simulation

AbstractReal‐time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost‐effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short‐term memory (LSTM) network, termed CATS‐LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS‐LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS‐LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS‐LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.

Design of the predictive management and control system for combined propulsion complex

The object of this researching is the process of maneuvering a sea-based vehicle under compressed conditions, which requires one hundred percent reserve of thrusters (THRs) of various modifications and locations. The main problem is the provision of energy-efficient control over the ship's motion at low speed in the horizontal plane using a high-level predictive controller. The hierarchy of the motion control system (MCS) is usually divided into several levels with the help of a high-level motion controller and the THR motor control distribution algorithm. This allows for a modular software structure where a high-level controller (HLC) can be designed without using comprehensive information about the THR motors. However, for a certain reference of THR configurations, such a decoupling can lead to reduced control performance due to the limitations of HLC regarding the physical constraints of the vessel and the behavior of MCS. The main results of the researching are methods to improve control performance using a nonlinear model predictive control (MPC) as a basis for the designed motion controllers due to its optimized solution and ability to consider constraints. A decoupled system was implemented for two simple motor tasks showing dissociation problems. The shortcomings were eliminated through the development of a nonlinear MPC controller, which combines the motion controller and the distribution of control over THR motors. To preserve the discrete modularity of the control system and achieve adequate performance, a nonlinear MPC controller with time-varying constraints was designed. This has made it possible to take into account the current limitations of the THR control system, increase the accuracy of control, and reduce the response time of the system by 10 %.

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Online decoupling of the time-varying longitudinal feedback loops for improved performance in Advanced Virgo Plus*

Abstract Gravitational Wave detectors require low-noise sensors combined with high-performance feedback loops to maximize the detector sensitivity in the low-frequency detection range. Some feedback loops in the detector are strongly coupled and their coupling varies over time, which is inherent to the optical configuration and the optical readout scheme used to obtain error signals for the feedback loops. This paper presents a control-based approach to deal with the time-varying interaction among three of the longitudinal degrees of freedom in the Advanced Virgo Plus detector, using the pre-existing infrastructure of the detector. An intuitive Multiple-Input Multiple-Output stability criterion is presented that illustrates how the time-varying coupling affects the stability of the feedback loops, as well as a method that identifies the coupling online and updates a decoupling matrix accordingly. Experiment results of the proposed method on the Advanced Virgo Plus detector illustrate continuous minimization of the coupling over time. Using the derived stability criterion, it is furthermore shown that the coupling is sufficiently minimized such that the interaction terms can be neglected in the control design, resulting in an improved controller performance.

Nonlinear Flux Linkage Observer with Model Reference Adaptive System for Improved Permanent Magnet Synchronous Motor Control

This paper addresses performance degradation in nonlinear flux linkage algorithms, arising from estimation errors in rotor flux linkage due to fluctuations in current and temperature. We introduce a parameter-adaptive flux linkage model using MRAS, which dynamically adjusts the rotor flux linkage, significantly minimizing estimation errors and improving control performance. When the rotor flux linkage of the motor undergoes sudden changes, the nonlinear flux linkage model shows a speed fluctuation of 5%, whereas the parameter-adaptive flux linkage model reduces the error to 0.6%. The algorithm demonstrates strong robustness when the stator resistance undergoes a sudden change. The control effectiveness under conditions such as load start and load mutation is excellent. Simulations and experiments demonstrate that the parameter-adaptive flux linkage model improves the estimation accuracy of the rotor position when motor parameters change.

Exploring The Potentials and Drawbacks of Hysteresis Current Controller For SRM: A Case Study Of Wind Energy Conversion System

Switched Reluctance Machines (SRMs) have drawn interest in renewable energy systems because of their special qualities and applicability. This study examines the idea of hysteresis current regulation in a wind energy conversion system with a Switched Reluctance Generator (SRG). The system is modeled in MATLAB/SIMULINK. The model is simulated and analyzed to ascertain the system’s benefits and shortcomings, focusing on reducing torque ripples in SRG. Considering most classical controllers are complicated to construct, the hysteresis current control is straightforward and effective. However, this controller's two primary shortcomings are that it produces a variable switching frequency and increases current ripples at a steady state, both enhancing acoustic noise in SRM. Lastly, the report suggests that improving controller performance should be the primary emphasis of future research in this field.

Enhanced Impedance Control of Cable-Driven Unmanned Aerial Manipulators Using Fractional-Order Nonsingular Terminal Sliding Mode Control with Disturbance Observer Integration

The article presents a novel control strategy for cable-driven aerial manipulators (UAMs) aimed at enhancing impedance control during contact operations in complex environments. A fractional-order nonsingular terminal sliding mode control (FONTSMC) integrated with a disturbance observer (DOB) is proposed to improve the robustness and precision of the UAM under lumped disturbances. This developed approach utilizes the flexibility of fractional calculus, the finite-time stability of nonsingular terminal sliding mode, and the real-time disturbance estimation capabilities of the DOB to ensure smooth and compliant contact interactions. The effectiveness of the proposed control strategy is validated through comprehensive simulation studies, which demonstrate significant improvements in control performance, stability, and disturbance rejection when compared to traditional methods. The results indicate that the FONTSMC-DOB framework is highly suitable for complex aerial manipulation tasks, offering both theoretical and practical insights into the design of advanced control systems for UAMs.

Trajectory tracking control based on deep reinforcement learning and ensemble random network distillation for robotic manipulator

Abstract In general, the trajectory tracking of robotic manipulator is exceptionally challenging due to the complex and strongly coupled mechanical architecture. In this paper, precise track control of the robotic manipulator is formulated as a dense reward problem for reinforcement learning(RL). A deep RL(DRL) approach combining the soft actor-critic (SAC) algorithm and ensemble random network distillation (ERND) is proposed to address the tracking control problem for robotic manipulator. Firstly, an ERND model is designed, consisting of a module list of multiple RND models. Each RND model obtains the error by learning the target features and the predicted features of the environment. The resulting error serves as internal rewards that drive the robotic agent to explore unknown and unpredictable environmental states. The ensemble model obtains the total internal reward by summing the internal rewards of each RND model, thereby obtaining more accurately reflecting the characteristics of the manipulator in tracking control tasks and improving control performance. Secondly, combining the SAC algorithm with ERND facilitates more robust exploration capabilities in environments with input saturation and joint angle constraints, thereby enabling faster learning of effective policies and enhancing the performance and efficiency of robotic manipulator tracking control tasks. Finally, the simulation results demonstrate that the robotic manipulator tracking control task is effectively completed in dense reward problems through the combination of the SAC algorithm and ERND.