Model-free Control Research Articles

Moisture content control strategy of magnetorheological fluid based on model-free adaptive control algorithm

Moisture content control strategy of magnetorheological fluid based on model-free adaptive control algorithm

Safety-Critical Model-Free Adaptive Iterative Learning Control for Multi-Agent Consensus Using Control Barrier Functions

Safety-Critical Model-Free Adaptive Iterative Learning Control for Multi-Agent Consensus Using Control Barrier Functions

Improved Triple-Voltage-Vector Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Improved Triple-Voltage-Vector Model-Free Predictive Current Control for Synchronous Reluctance Motor Drives

Model-Free Predictive Current Control of DTP-PMSM Based on Ultra-Local Model Considering Back EMF Harmonics

Model-Free Predictive Current Control of DTP-PMSM Based on Ultra-Local Model Considering Back EMF Harmonics

Model-Free Intelligent Control for Space Soft Robotic Manipulators

Given the advantages of softness, lightness, low cost, and interaction safety, inverse kinematic modeling and control of soft actuators has caused a research boom. However, in realizing dexterous manipulation of space large soft manipulators, it is much more difficult to achieve precise control not only because of the greater accumulation of errors in the multiple degrees of freedom and nonlinear properties of soft materials at large scales but also because of the inability of directly solving the inverse kinematics in the cases of singular pure elongation. In this work, a model-free intelligent kinematic control strategy is proposed for these problems that exhibit a mapping relationship between the output end-effector position and the input pressure. For multiple-degree-of-freedom robots, especially pneumatic soft manipulators, traditional inverse kinematic modeling methods are complex and inverse Jacobian matrix solution often encounters geometric singularities. To address this issue, this paper proposes an inverse kinematics–multilayer perceptron (IK-MLP) method for soft manipulators. In this strategy, the trained intelligent controller can be applied to control pneumatic manipulators without establishing a traditional inverse kinematic model. The control algorithm is experimentally tested based on the ground experiment system of the space soft manipulator. Simulations and experiments are carried out to validate the given model-free intelligent controller, proving that the IK-MLP method can effectively solve the singularity of inverse kinematics.

Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set

Model-Free Predictive Current Controller for Common Mode Voltage Stabilization by Finite odd Virtual Vector set

Model-Free Prescribed-Time Control Under Input Amplitude and Rate Saturations for Uncertain Mechatronic Systems With Mismatched Disturbances

Model-Free Prescribed-Time Control Under Input Amplitude and Rate Saturations for Uncertain Mechatronic Systems With Mismatched Disturbances

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors.

This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefined-time convergence property guarantees that the regulation task can be finished by robotic manipulators in a preset time, in spite of the initial state of manipulators. This feature will facilitate the scheduling of a series of tasks in industrial applications. To this end, a varying-parameter predefined-time convergent zeroing neural dynamics (ZND) model is first proposed and employed to solve the regulation problem. As well as the primary task, a conventional ZND model is utilized to achieve the avoidance of obstacle. The stability of the proposed controller is analyzed based on the Lyapunov stability theory. For the sake of dealing with the unknown kinematic model of robotic manipulators, gradient neural dynamics (GND) models are exploited to adapt the Jacobian matrices just relying on the control signal and sensory output, which enables us to control robotic manipulators in a model-free manner. Finally, the efficacy and merits of the proposed control method are verified by simulations and experiments, including a comparison with the existing method.

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

Model-Free Force Control of Cable-Driven Parallel Manipulators for Weight-Shift Aircraft Actuation

Model-Free Force Control of Cable-Driven Parallel Manipulators for Weight-Shift Aircraft Actuation

Event-triggered data-driven load frequency control for multi-area power system with wind penetration under deregulation environment

In order to solve the problem of power exchange between areas of multi-area power systems under the power market, this paper designs a power trading contract based on the generation participation matrix, so as to simulate the specific process of power change in each area. With the integration of wind power into the large-scale power system, it is difficult to model the multi-area power system. For this situation, this paper designs a data-driven model-free adaptive load frequency control algorithm based on collecting input and output data, which gets rid of the dependence of the power system on the model. Along with the frequent transmission of input and output data in each area, the communication load of the power system also increases. Aiming at saving communication resources, this paper designs an event-triggered mechanism to reduce the communication bandwidth. The stability of the control algorithm is demonstrated theoretically. Finally, a three-area power system with wind penetration is used as an example to simulate and verify the effectiveness of the proposed algorithm in this paper.

Applications and Challenges of Fieldbus Technology in Intelligent Manufacturing Systems

Abstract This study advances the field of intelligent manufacturing by integrating automation, information technology, and digitalization into a novel “five horizontal and three vertical” system architecture. We introduce an enhanced model-free adaptive control (MFAC) algorithm that utilizes a dynamic linearization method alongside pseudo-partial derivatives (PPD) for optimizing data transfer within intelligent manufacturing systems. Using CANoe software for simulation, our research demonstrates the improved MFAC algorithm’s superior bus message transmission efficiency by 0.0224 to 0.026 and a significant reduction in the network load factor by 0.2068. These advancements highlight the efficacy of fieldbus technology in reducing network congestion and enhancing real-time performance in intelligent manufacturing environments.

Distributed Model-Free Adaptive Control Strategy for Hybrid AC/DC Microgrid With Event-Triggered Mechanism

Distributed Model-Free Adaptive Control Strategy for Hybrid AC/DC Microgrid With Event-Triggered Mechanism

A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles

A Novel Enhanced Data-Driven Model-Free Adaptive Control Scheme for Path Tracking of Autonomous Vehicles

Model-Free Online Four-Dimensional Ripple Correlation Control for Loss Optimization of Isolated Quad-Active Bridge Converters

Model-Free Online Four-Dimensional Ripple Correlation Control for Loss Optimization of Isolated Quad-Active Bridge Converters

Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer

Model-Free Predictive Current Control of PMSM Using Modified Extended State Observer

Model-Free Control-Based Trajectory Tracking Control of a Tail-Sitter UAV in Hovering Mode

Model-Free Control-Based Trajectory Tracking Control of a Tail-Sitter UAV in Hovering Mode

Adaptive Inertia Observer-based Model-Free Predictive Current Control for PMSM Driving System of Electric Vehicles

Adaptive Inertia Observer-based Model-Free Predictive Current Control for PMSM Driving System of Electric Vehicles

Variable Switching Point Model-Free Predictive Current Control Strategy for SRM

Variable Switching Point Model-Free Predictive Current Control Strategy for SRM