Model-predictive reference trajectory planning for redundant pneumatic collaborative robots

2020 ◽  
Vol 68 (5) ◽  
pp. 360-374
Author(s):  
Annika Mayer ◽  
Daniel Müller ◽  
Adrian Raisch ◽  
Oliver Sawodny
Keyword(s):  
Nonlinear Model ◽  
Joint Space ◽  
Reference Trajectory ◽  
Control Loop ◽  
Collaborative Robots ◽  
Model Control ◽  
Continuum Robot ◽  
Predictive Controller ◽  
Human Operators ◽  
Working Day

AbstractCollaborative robots have the potential to simplify the working day of the future. The goal in the development of these robots is to assist human operators by handling all sorts of tasks. A common underlying problem is to move the robot’s tool center point in a desired way. In this work we consider the generation of a feasible trajectory in joint space given a reference in task space. This is done at the example of the Bionic Handling Assistant (BHA), a compliant, redundant and pneumatically driven continuum robot. The trajectory for the BHA is obtained using a model control loop (MCL) which internally realizes a nonlinear model predictive controller (NMPC). We simplify the high dimensional and nonlinear model of the BHA to a computational efficient model which still covers the major effects of the original dynamics. This results not only in a feasible trajectory but also enables the model control loop to be real-time applicable. The proposed method is validated in simulation.

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 Behavioural Study of the Force Control Loop Used in a Collaborative Robot for Sanding Materials

Materials ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 67
Author(s):  
Rodrigo Pérez Ubeda ◽  
Santiago C. Gutiérrez Rubert ◽  
Ranko Zotovic Stanisic ◽  
Ángel Perles Ivars
Keyword(s):  
Force Control ◽  
Feed Rate ◽  
Significant Variation ◽  
Industrial Robots ◽  
Control Loop ◽  
Saturation State ◽  
Collaborative Robots ◽  
Increase Productivity ◽  
Different Characteristics ◽  
Collaborative Robot

The rise of collaborative robots urges the consideration of them for different industrial tasks such as sanding. In this context, the purpose of this article is to demonstrate the feasibility of using collaborative robots in processing operations, such as orbital sanding. For the demonstration, the tools and working conditions have been adjusted to the capacity of the robot. Materials with different characteristics have been selected, such as aluminium, steel, brass, wood, and plastic. An inner/outer control loop strategy has been used, complementing the robot’s motion control with an outer force control loop. After carrying out an explanatory design of experiments, it was observed that it is possible to perform the operation in all materials, without destabilising the control, with a mean force error of 0.32%. Compared with industrial robots, collaborative ones can perform the same sanding task with similar results. An important outcome is that unlike what might be thought, an increase in the applied force does not guarantee a better finish. In fact, an increase in the feed rate does not produce significant variation in the finish—less than 0.02 µm; therefore, the process is in a “saturation state” and it is possible to increase the feed rate to increase productivity.

scholarly journals Delayed Trilateral Teleoperation of a Mobile Robot

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
D. Santiago ◽  
E. Slawiñski ◽  
V. Mut
Keyword(s):  
Stability Analysis ◽  
Mobile Robot ◽  
Shared Environment ◽  
Time Varying ◽  
Control Parameters ◽  
Simple Test ◽  
Control Loop ◽  
Teleoperation System ◽  
Human Operators ◽  
The Stability

This paper analyzes the stability of a trilateral teleoperation system of a mobile robot. This type of system is nonlinear, time-varying, and delayed and includes a master-slave kinematic dissimilarity. To close the control loop, three P+d controllers are used under a position master/slave velocity strategy. The stability analysis is based on Lyapunov-Krasovskii theory where a functional is proposed and analyzed to get conditions for the control parameters that assure a stable behavior, keeping the synchronism errors bounded. Finally, the theoretical result is verified in practice by means of a simple test, where two human operators both collaboratively and simultaneously drive a 3D simulator of a mobile robot to achieve an established task on a remote shared environment.

Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

Implementation of Real-Time Concentration Process Control System Design Based on PID and Fuzzy Logic Controller

2015 ◽  
Vol 772 ◽  
pp. 147-153
Author(s):  
Mohamed Amir Gabir Elbakri
Keyword(s):  
Fuzzy Logic ◽  
Process Control ◽  
Real Time ◽  
Fuzzy Logic Controller ◽  
Control Loop ◽  
Time System ◽  
Real Time System ◽  
Concentration Process ◽  
Labview Software ◽  
Model Control

Concentration process is commonly process in industries for chemicals and products that want to reach desired amount of matter in product, so concentration process control is important to reach the desired concentrate of product.Concentration in streams are affected by physical variables around it like pressure, temperature and amount of matter that solvent in streams, so the amplitude of concentration is randomly change with time that make control process not easy.In this project controller was designed for concentration process and was implemented in real-time system that constructed to verify the response of controller, in prototype concentrated solutions-juice and sugar-used to control in it separately with water then mixed to produce juice with desired property.The Mathematical model of concentrated process was evolved and Matlab used to analyze and design control loop for these model, control algorithms used as PID & Fuzzy logic controller to build controller that achieve the specification requirements of a system process.The Fuzzy PI-controller designed to control that for characteristic of nonlinearity of real-time system, and implement simulation of control loops in LABVIEW software with appropriate interface.The DAQ hardware with LABVIEW software are used to implement the control loop that designed for real-time prototype to produce the juice with desired concentrated.

scholarly journals Nonlinear Model and Dynamic Behavior of Photovoltaic Grid-Connected Inverter

2020 ◽  
Vol 10 (6) ◽  
pp. 2120 ◽  
Author(s):  
Zhi-Xian Liao ◽  
Dan Luo ◽  
Xiao-Shu Luo ◽  
Hai-Sheng Li ◽  
Qin-Qin Xiang ◽  
...  
Keyword(s):  
Bifurcation Diagram ◽  
Dynamic Behavior ◽  
Nonlinear Model ◽  
Nonlinear Dynamic ◽  
Optimization Design ◽  
Stable Operation ◽  
Control Parameters ◽  
Nonlinear Dynamic Behavior ◽  
Predictive Controller ◽  
Grid Connected Inverter

A photovoltaic grid-connected inverter is a strongly nonlinear system. A model predictive control method can improve control accuracy and dynamic performance. Methods to accurately model and optimize control parameters are key to ensuring the stable operation of a photovoltaic grid-connected inverter. Based on the nonlinear characteristics of photovoltaic arrays and switching devices, we established a nonlinear model of photovoltaic grid-connected inverters using the state space method and solved its model predictive controller. Then, using the phase diagram, folded diagram, and bifurcation diagram methods, we studied the nonlinear dynamic behavior under the influence of control parameters on both fast and slow scales. Finally, we investigated the methods of parameter selection based on the characteristics of nonlinear dynamic behavior. Our research shows that the predictive controller parameters are closely related to the bifurcation and chaos behaviors of the grid-connected photovoltaic inverter. The three-dimensional bifurcation diagram can be used to observe the periodic motion region of the control parameters. After selecting the optimization target, the bifurcation diagram can be used to guide the selection of control parameters for inverter design. The research results can be used to guide the modeling, stability analysis, and optimization design of photovoltaic grid-connected inverters.

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