scholarly journals A neural network-based model predictive controller for displacement tracking of piezoelectric actuator with feedback delays

2021 ◽  
Vol 18 (6) ◽  
pp. 172988142110576
Author(s):  
Zhangming Du ◽  
Chao Zhou ◽  
Zhiqiang Cao ◽  
Shuo Wang ◽  
Long Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Piezoelectric Actuator ◽  
Tracking Control ◽  
Low Cost ◽  
Moving Average ◽  
Dynamic Performance ◽  
Sampling Rate ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Predictive Controller

Piezoelectric actuators are widely used in micro/nanoscale robotic manipulators. Due to its hysteresis and dynamic-related nonlinearity, accurate displacement tracking control of piezoelectric actuator is challenging. Besides, in some low-cost practical systems with low sampling rate, transmission delay causes mismatches between feedback and real displacement, further increasing the challenge in tracking control. In this article, a neural network-based model predictive controller (MPC) is proposed for precise tracking control of piezoelectric actuator’s displacement in situation where feedback is slow and delayed. The prediction model is based on a nonlinear-autoregressive-moving-average-with-exogenous-inputs framework, which outputs entire prediction horizon of future displacement in a single time, and is fulfilled by a multilayer feedforward neural network. An extended Kalman filter-based estimation for displacement is introduced to relieve the influence of feedback delays so as to improve dynamic performance of the controller. Another neural network is trained to provide initial values for MPC to reduce computation costs and improve performance in dynamic tracking. In a series of tracking experiments, the effectiveness of proposed controller is verified.

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

2020 ◽  
Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan
Keyword(s):  
Trajectory Tracking ◽  
Wind Waves ◽  
Tracking Error ◽  
Tracking Performance ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Surface Vessels ◽  
Waves And Currents ◽  
Predictive Controller ◽  
Disturbance Model

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

scholarly journals Model predictive Controller for Mobile Robot

2017 ◽  
Vol 2 (2) ◽  
pp. 18 ◽  
Author(s):  
Alireza Rezaee
Keyword(s):  
Mobile Robot ◽  
Adaptive Controller ◽  
Quadratic Equation ◽  
Linear Quadratic ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Future Behavior ◽  
Ricatti Equation ◽  
Predictive Controller ◽  
Real Robot

This paper proposes a Model Predictive Controller (MPC) for control of a P2AT mobile robot. MPC refers to a group of controllers that employ a distinctly identical model of process to predict its future behavior over an extended prediction horizon. The design of a MPC is formulated as an optimal control problem. Then this problem is considered as linear quadratic equation (LQR) and is solved by making use of Ricatti equation. To show the effectiveness of the proposed method this controller is implemented on a real robot. The comparison between a PID controller, adaptive controller, and the MPC illustrates advantage of the designed controller and its ability for exact control of the robot on a specified guide path.

scholarly journals Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Panyawut Sri-iesaranusorn ◽  
Attawit Chaiyaroj ◽  
Chatchai Buekban ◽  
Songphon Dumnin ◽  
Ronachai Pongthornseri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Low Cost ◽  
Sampling Rate ◽  
Assessment Procedure ◽  
Semg Signal ◽  
Neural Network Approach ◽  
Assessment Protocol ◽  
Wide Range

Surface electromyography (sEMG) is a non-invasive and straightforward way to allow the user to actively control the prosthesis. However, results reported by previous studies on using sEMG for hand and wrist movement classification vary by a large margin, due to several factors including but not limited to the number of classes and the acquisition protocol. The objective of this paper is to investigate the deep neural network approach on the classification of 41 hand and wrist movements based on the sEMG signal. The proposed models were trained and evaluated using the publicly available database from the Ninapro project, one of the largest public sEMG databases for advanced hand myoelectric prosthetics. Two datasets, DB5 with a low-cost 16 channels and 200 Hz sampling rate setup and DB7 with 12 channels and 2 kHz sampling rate setup, were used for this study. Our approach achieved an overall accuracy of 93.87 ± 1.49 and 91.69 ± 4.68% with a balanced accuracy of 84.00 ± 3.40 and 84.66 ± 4.78% for DB5 and DB7, respectively. We also observed a performance gain when considering only a subset of the movements, namely the six main hand movements based on six prehensile patterns from the Southampton Hand Assessment Procedure (SHAP), a clinically validated hand functional assessment protocol. Classification on only the SHAP movements in DB5 attained an overall accuracy of 98.82 ± 0.58% with a balanced accuracy of 94.48 ± 2.55%. With the same set of movements, our model also achieved an overall accuracy of 99.00% with a balanced accuracy of 91.27% on data from one of the amputee participants in DB7. These results suggest that with more data on the amputee subjects, our proposal could be a promising approach for controlling versatile prosthetic hands with a wide range of predefined hand and wrist movements.

On the Structural Optimization of a Neural Network Model Predictive Controller

2012 ◽  
pp. 121228081703003 ◽  
Author(s):  
Mahsa Sadeghassadi ◽  
Alireza Fatehi ◽  
Ali Khaki Sedigh ◽  
SeyedMehrdad Hosseini
Keyword(s):  
Neural Network ◽  
Structural Optimization ◽  
Network Model ◽  
Neural Network Model ◽  
Model Predictive Controller ◽  
Predictive Controller

Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing

2016 ◽  
Vol 28 (5) ◽  
pp. 695-701 ◽  
Author(s):  
Tomohiro Henmi ◽  
Keyword(s):  
Nonlinear Model ◽  
Tracking Control ◽  
Parameter Tuning ◽  
Reference Trajectory ◽  
Control Input ◽  
Control Parameters ◽  
Model Predictive Controller ◽  
Tuning Method ◽  
Predictive Controller ◽  
System Output

[abstFig src='/00280005/11.jpg' width='300' text='ANMPC controller' ] The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptive nonlinear MPC (ANMPC) for a tracking control problem of nonlinear two-link planar manipulators. This ANMPC uses a new reference trajectory having control parameters that must be tuned based on the desired controlled system’s responses and properties. To reduce troublesome parameter tuning, we propose new parameter-tuning method for ANMPC by a quantitative analysis of the relationship between a system’s behavior and ANMPC parameters. Numerically simulating the two-link nonlinear manipulator’s tracking control under various conditions demonstrates that proposed tuning method tunes the ANMPC effectively.

scholarly journals Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1254 ◽  
Author(s):  
Lingfei Xiao ◽  
Min Xu ◽  
Yuhan Chen ◽  
Yusheng Chen
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Bp Neural Network ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Predictive Controller ◽  
Predictive Controller

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

Control of Continuous Stirred Tank Reactor Using Artificial Neural Networks Based Predictive Control

2012 ◽  
Vol 550-553 ◽  
pp. 2908-2912 ◽  
Author(s):  
Ginuga Prabhaker Reddy ◽  
G. Radhika ◽  
K Anil
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Model Predictive Controller ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
The Neural Network ◽  
Predictive Controller ◽  
Continuous Stirred Tank

In this work, a Neural network based predictive controller is analyzed to a non linear continuous stirred tank reactor (CSTR) carrying out series and parallel reactions: A→B→C and 2A→D. In the first step, the neural network model of continuous stirred tank reactor is obtained by Levenburg- Marquard training. The data for the training the network is generated using state space model of continuous stirred tank reactor. The neural network model of continuous stirred tank reactor is used in model predictive controller design. The performance of present neural network based model predictive controller (NNMPC) is evaluated through simulations for servo & regulatory problems of CSTR. The performance of neural network based predictive controller is found to be superior than conventional PI controller for setpoint tracking problems.

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