Robot Control Research Articles

Study on dexterous structure and control of bio-inspired musculoskeletal robots in artificial intelligence environment

Study on dexterous structure and control of bio-inspired musculoskeletal robots in artificial intelligence environment

Model inversion for trajectory control of reconfigurable underactuated cable-driven parallel robots

Model inversion for trajectory control of reconfigurable underactuated cable-driven parallel robots

Stabilization control of wheeled mobile robot based on neural sliding mode under wheel slip conditions

Stabilization control of wheeled mobile robot based on neural sliding mode under wheel slip conditions

Modeling and control of a sperm-inspired robot with helical propulsion.

Efficient propulsion has been a central focus of research in the field of biomimetic underwater vehicles. Compared to the prevalent fish-like reciprocating flapping propulsion mode, the sperm-like helical propulsion mode features higher efficiency and superior performance in high-viscosity environments. Based on the previously developed sperm-inspired robot, this paper focuses on its dynamic modeling and depth control research. The helical propulsion performance of the sperm-inspired robot is analyzed by resistance-theory-based force analysis, followed by the application of Kirchhoff rod theory to determine the helical waveform parameters. The dynamic model of the sperm-inspired robot is established using the Kirchhoff equation, and its validity is verified through experiments. To enhance the practical application capability of the sperm-inspired robot, this study develops an ADRC (Active Disturbance Rejection Control) depth controller for roll-spin coupling motion based on the constructed dynamics model. The effectiveness of the controller is thoroughly validated through a combination of simulation and experiment. Experimental results demonstrate the excellent depth control ability of the robot, with an average depth error controlled within 0.19 cm. This superior performance lays a foundation for the future application of our robot in underwater operations.

Research on force feedback technology for industrial robot

Force feedback technology plays a crucial role in the field of robot control, especially in scenarios involving precise task execution. With the increasing application of robots in industrial production and automation, traditional control methods based on preset paths can no longer meet the requirements of complex and changing working environments. The force feedback system senses the force information of the robot during operation, adjusts the robot's actions and force in real time, ensures that appropriate force can be maintained during operation, and prevents damage to the working object or reduced task accuracy. While this technology primarily enhances robotic flexibility in complex operations, it has also become instrumental in advancing human-robot collaboration. By combining human perception and regulation abilities, robots are able to perform more challenging tasks. In recent years, force feedback systems that combine multi-sensor fusion, visual control, and remote operation technologies have gradually become one of the core solutions for solving force control problems in robot task execution, providing strong guarantees for safety, accuracy, and efficiency in automated production.

Active Cables Selection for Collocated Vibration Control of Small-Sized Overconstrained Cable-Driven Parallel Robots

Active Cables Selection for Collocated Vibration Control of Small-Sized Overconstrained Cable-Driven Parallel Robots

Adaptive Robust Formation Tracking Control for Traffic Cone Robots Under Uncertain Disturbances: With Leakage and Dead Zone Types

Adaptive Robust Formation Tracking Control for Traffic Cone Robots Under Uncertain Disturbances: With Leakage and Dead Zone Types

A Review on the Recent Development of Planar Snake Robot Control and Guidance

A Review on the Recent Development of Planar Snake Robot Control and Guidance

Applications of micro-electromechanical systems in robot control

Applications of micro-electromechanical systems in robot control

Adaptive Dynamic Programming-Based Fixed-Time Optimal Control for Wheeled Mobile Robot

Adaptive Dynamic Programming-Based Fixed-Time Optimal Control for Wheeled Mobile Robot

End-Effector Trajectory Tracking Control of Stacking Robot Based on LuGre Model

End-Effector Trajectory Tracking Control of Stacking Robot Based on LuGre Model

FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots

FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots

FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots

FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots

Super-Twisting Sliding Mode- and Iterative Learning-Based Synchronization Control for 2R1T Parallel Robots

Super-Twisting Sliding Mode- and Iterative Learning-Based Synchronization Control for 2R1T Parallel Robots

Adaptive Prescribed-Time Optimal Control for Flexible-Joint Robots via Reinforcement Learning

Adaptive Prescribed-Time Optimal Control for Flexible-Joint Robots via Reinforcement Learning

Observer-based adaptive neural network robust 𝐻∞ control of quadrotor aerial robot

Observer-based adaptive neural network robust 𝐻∞ control of quadrotor aerial robot

Pulse width modulation control of Mecanum-wheeled mobile robot with random noise

Pulse width modulation control of Mecanum-wheeled mobile robot with random noise

Model-free robust reinforcement learning via Polynomial Chaos

In recent years, the Robust Markov Decision Process (RMDP) has become an important modeling framework to address the discrepancies between simulated and real-world environments in Reinforcement Learning (RL) training. The purpose of RMDP is to accommodate the uncertainty of the real-world environments, employing a conservative approach to enhance the robustness of policy decisions. However, due to the difficulty of robust value function estimation, the RMDP framework is challenging to generalize to environments with large continuous state–action spaces. Our work focuses on model-free robust RL and proposes a model-free algorithm for continuous space setting. We adopt a new perspective on uncertainty sets such that the uncertainty sets are parameterized and the parameters obey specific stochastic distributions. We present a novel approach RPC to estimate the robust value function utilizing generalized Polynomial Chaos(gPC). We provide a proof to guarantee the convergence of the algorithm. Our training framework is based on off-policy RL, which reduces the computation overhead by gPC and improves learning stability. Our algorithm can handle continuous tasks and guarantee the robustness of the algorithm without incurring excessive computational overhead. We combine RPC with the TD3 method and conduct several experiments to evaluate its performance in a continuous robot control task, and the experimental results provide further evidence of the robustness of our algorithm.

Industrial robot control system research based on the direction of programmable logic controllers

Industrial robot control system research based on the direction of programmable logic controllers

Constraint-oriented formation control of multi-robot system in leaderless consensus under confined conditions

In this paper, constraint-oriented coordination control of a first-order multi-robot system has been considered in a leaderless consensus where rectangular velocity components of participating robots are subject to constraints while also avoiding inter-robots collisions. The desired formation control of different robots and their inter-robots collision avoidance in a team of robots are achieved by solving an optimization problem based on control barrier functions whereas graph theory concepts are used to represent the interaction relations among robots. A devised quadratic programme subject to the velocity and safety conditions, minimizes the desired cost function encoded in the control barrier function and generates separate control inputs for each robot. The rectangular components of the velocity against each robot are kept constrained during the entire operation of the formation control. The optimization-technique-based decentralized controllers were simulated in MATLAB and the corresponding results were recorded. The robots in the team successfully attained the desired formation in a leaderless consensus, deploying themselves in a plane under constrained rectangular velocities without colliding with each other. Several simulation examples with different values of velocity constraints have been shown to illustrate the operation of constrained controllers while ensuring that the desired leaderless-consensus-based formation remains attainable in a safe manner.