scholarly journals Distributed Adaptive Coordinated Control of Multiple Euler–Lagrange Systems considering Output Constraints and Time Delays

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Hongde Qin ◽  
Xiaojia Li ◽  
Yanchao Sun
Keyword(s):  
Coordinated Control ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Simulation Experiments ◽  
Control Laws ◽  
The Neural Network ◽  
Output Constraints ◽  
Control Schemes ◽  
Virtual Leader ◽  
Coordinated Tracking

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws.

Modeling and trajectory tracking control of a two-wheeled mobile robot: Gibbs–Appell and prediction-based approaches

Robotica ◽  
2018 ◽  
Vol 36 (10) ◽  
pp. 1551-1570 ◽  
Author(s):  
Hossein Mirzaeinejad ◽  
Ali Mohammad Shafei
Keyword(s):  
Trajectory Tracking ◽  
Dynamic Models ◽  
Sliding Mode ◽  
Tracking Accuracy ◽  
Wheeled Mobile Robots ◽  
Tracking Errors ◽  
Trajectory Tracking Control ◽  
Control Laws ◽  
Control Approach ◽  
Prediction Time

SUMMARYThis study deals with the problem of trajectory tracking of wheeled mobile robots (WMR's) under non-holonomic constraints and in the presence of model uncertainties. To solve this problem, the kinematic and dynamic models of a WMR are first derived by applying the recursive Gibbs–Appell method. Then, new kinematics- and dynamics-based multivariable controllers are analytically developed by using the predictive control approach. The control laws are optimally derived by minimizing a pointwise quadratic cost function for the predicted tracking errors of the WMR. The main feature of the obtained closed-form control laws is that online optimization is not needed for their implementation. The prediction time, as a free parameter in the control laws, makes it possible to achieve a compromise between tracking accuracy and implementable control inputs. Finally, the performance of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulations of some trajectory tracking maneuvers.

scholarly journals Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
S. Puga-Guzmán ◽  
J. Moreno-Valenzuela ◽  
V. Santibáñez
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Gravitational Force ◽  
Tracking Accuracy ◽  
Adaptive Neural Network ◽  
Tracking Errors ◽  
The Neural Network ◽  
Proportional Derivative Controller ◽  
Velocity Tracking ◽  
Uniformly Bounded

A nonlinear proportional-derivative controller plus adaptive neuronal network compensation is proposed. With the aim of estimating the desired torque, a two-layer neural network is used. Then, adaptation laws for the neural network weights are derived. Asymptotic convergence of the position and velocity tracking errors is proven, while the neural network weights are shown to be uniformly bounded. The proposed scheme has been experimentally validated in real time. These experimental evaluations were carried in two different mechanical systems: a horizontal two degrees-of-freedom robot and a vertical one degree-of-freedom arm which is affected by the gravitational force. In each one of the two experimental set-ups, the proposed scheme was implemented without and with adaptive neural network compensation. Experimental results confirmed the tracking accuracy of the proposed adaptive neural network-based controller.

scholarly journals The application of Deep Reinforcement Learning in Coordinated Control of Nuclear Reactors

2021 ◽  
Vol 2113 (1) ◽  
pp. 012030
Author(s):  
Jing Li ◽  
Yanyang Liu ◽  
Xianguo Qing ◽  
Kai Xiao ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Reactor ◽  
Coordinated Control ◽  
Level Control ◽  
Simulation Experiments ◽  
Reinforcement Learning Model ◽  
Reactor Control System

Abstract The nuclear reactor control system plays a crucial role in the operation of nuclear power plants. The coordinated control of power control and steam generator level control has become one of the most important control problems in these systems. In this paper, we propose a mathematical model of the coordinated control system, and then transform it into a reinforcement learning model and develop a deep reinforcement learning control algorithm so-called DDPG algorithm to solve the problem. Through simulation experiments, our proposed algorithm has shown an extremely remarkable control performance.

scholarly journals Development of a magnetic tracking system for monitoring soil movements induced by geohazards

2019 ◽  
Vol 92 ◽  
pp. 17007 ◽  
Author(s):  
Xiaoyu Chen ◽  
Rolando P. Orense
Keyword(s):  
Magnetic Fields ◽  
Permanent Magnets ◽  
Tracking System ◽  
Soil Liquefaction ◽  
Small Scale ◽  
Interior Point Algorithm ◽  
Tracking Accuracy ◽  
Tracking Errors ◽  
Magnetic Tracking ◽  
Soil Movements

In the study of geotechnical hazards, such as soil liquefaction and landslides, the analysis of soil movements is always one of the major preoccupations. An efficient movement sensing technique requires the tracking of subsurface soil for the purpose of examining the mechanism involved. A magnetic tracking system is therefore proposed, with permanent magnets as trackers and magnetometers as receivers. When permanent magnets, deployed within the soil to serve as excitation sources, move with soil body during a geotechnical event, they generate static magnetic fields whose flux densities are related with the positions and orientations of the magnets. Magnetometers are used as receivers to detect the generated magnetic fields, which can be further used in calculating the magnets' locations and orientations based on appropriately developed algorithms. Comparison between situations where the trackers are exposed to air and embedded within soil was conducted to evaluate the influence of soil (wet and dry) on the tracking accuracy. Also, multi-objective tracking is realized by using the particle swarm optimization (PSO) technique combined with interior-point algorithm. The tracking errors are evaluated and applications of the proposed system in small-scale laboratory tests for geohazards are discussed.

scholarly journals Coordinated Control Schemes of Super-Capacitor and Kinetic Energy of DFIG for System Frequency Support

Energies ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 103 ◽  
Author(s):  
Liansong Xiong ◽  
Yujun Li ◽  
Yixin Zhu ◽  
Ping Yang ◽  
Zhirong Xu
Keyword(s):  
Kinetic Energy ◽  
Coordinated Control ◽  
Super Capacitor ◽  
Control Schemes ◽  
System Frequency

Repetitive Control Based Disturbance Cancellation Using Iterative Basis Function Feedback With Wavelet Filtering

2008 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Chih-Wei Huang ◽  
Hao-Ping Wen
Keyword(s):  
Repetitive Control ◽  
Basis Functions ◽  
Projection Algorithm ◽  
Tracking Errors ◽  
Wavelet Filtering ◽  
Control Laws ◽  
Periodic Disturbance ◽  
Output Tracking ◽  
Disturbance Cancellation ◽  
Periodic Error

This paper presents repetitive control laws in real time using matched basis functions. These laws adjust the command given a feedback control system in order to eliminate tracking errors, resulting from in general a periodic disturbance and a non-periodic disturbance. The periodic error can be reduced by linear basis functions while the non-periodic error by the projection algorithm along with the wavelet filtering. The control laws do not use a system model, but instead the control action is chosen to be a linear combination of chosen input basis functions, and the corresponding output basis functions are obtained, nominally by experiment. The repetitive control laws use the projection algorithm to compute the output components on the output basis functions, and then the corresponding input components are adjusted accordingly. The output signals are reconstructed via the wavelet filtering before they are feedback to the controller. Numerical experiments show that the repetitive controllers are quite effective. In particular, the output tracking errors are further reduced because of the introduction of the wavelet filtering when compared to the previous work. In general, the repetitive control laws developed here can be used for the purpose of precision machinery control.

scholarly journals Motion Planning and Coordinated Control of Underwater Vehicle-Manipulator Systems with Inertial Delay Control and Fuzzy Compensator

2020 ◽  
Vol 10 (11) ◽  
pp. 3944
Author(s):  
Han Han ◽  
Yanhui Wei ◽  
Xiufen Ye ◽  
Wenzhi Liu
Keyword(s):  
Motion Planning ◽  
Trajectory Tracking ◽  
Feedback Linearization ◽  
Nonlinear Function ◽  
Coordinated Control ◽  
Underwater Vehicle ◽  
Redundancy Resolution ◽  
Tracking Errors ◽  
Delay Control ◽  
Fuzzy Compensator

This paper presents new motion planning and robust coordinated control schemes for trajectory tracking of the underwater vehicle-manipulator system (UVMS) subjected to model uncertainties, time-varying external disturbances, payload and sensory noises. A redundancy resolution technique with a new secondary task and nonlinear function is proposed to generate trajectories for the vehicle and manipulator. In this way, the vehicle attitude and manipulator position are aligned in such a way that the interactive forces are reduced. To resist sensory measurement noises, an extended Kalman filter (EKF) is utilized to estimate the UVMS states. Using these estimates, a tracking controller based on feedback Linearization with both the joint-space and task-space tracking errors is proposed. Moreover, the inertial delay control (IDC) is incorporated in the proposed control scheme to estimate the lumped uncertainties and disturbances. In addition, a fuzzy compensator based on these estimates via IDC is introduced for reducing the undesired effects of perturbations. Trajectory tracking tasks on a five-degrees-of-freedom (5-DOF) underwater vehicle equipped with a 3-DOF manipulator are numerically simulated. The comparative results demonstrate the performance of the proposed controller in terms of tracking errors, energy consumption and robustness against uncertainties and disturbances.

scholarly journals Design of Augmented Nonlinear PD Controller of Delta/Par4-Like Robot

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Amjad J. Humaidi ◽  
Ahmed Ibraheem Abdulkareem
Keyword(s):  
Trajectory Tracking ◽  
Lyapunov Method ◽  
Dynamic Performance ◽  
Parallel Robot ◽  
Pd Controller ◽  
Comparison Study ◽  
Tracking Accuracy ◽  
Control Schemes ◽  
The Stability ◽  
Made In

This work presents the design of two control schemes for a Delta/Par4-like parallel robot: augmented PD (APD) controller and augmented nonlinear PD (ANPD) controller. The stability of parallel robot based on nonlinear PD controller has been analyzed and proved based on Lyapunov method. A comparison study between APD and ANPD controllers has been made in terms of performance and accuracy improvement of trajectory tracking. Also, another comparison study has been presented between augmented nonlinear PD (ANPD) controller and nonaugmented nonlinear PD (NANPD) controller in order to show the enhancement of introducing the augmented structure on dynamic performance and trajectory tracking accuracy. The effectiveness of augmented PD controllers (APD and ANPD) and nonaugmented nonlinear PD (NANPD) controller for the considered parallel robot are verified via simulation within the MATLAB environment.

Simulation and Control of a Commercial Double Effect Evaporator: Tomato Juice

2010 ◽  
Vol 5 (1) ◽  
Author(s):  
Praveen Yadav ◽  
Amiya K Jana
Keyword(s):  
Double Effect ◽  
Open Loop ◽  
Simulation Experiments ◽  
Loop Control ◽  
Process Dynamics ◽  
Point Tracking ◽  
Tomato Paste ◽  
Control Schemes ◽  
And Control ◽  
Gain Scheduled

This work aims to present a detailed study on a commercial double-effect tomato paste evaporation system. The modeling equations formulated for process simulation belong to backward feeding arrangement. Open-loop process dynamics has been studied by rigorous simulation of the model structure. In the next, three multi-loop control schemes, namely conventional proportional integral (PI), gain-scheduled PI (GSPI) and nonlinear PI (NLPI), have been synthesized for the sample process. Finally, several simulation experiments have been conducted to investigate the comparative closed-loop performance based on set point tracking and disturbance rejection.

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