scholarly journals Methods of Pre-Identification of TITO Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 6954
Author(s):  
Milan Saga ◽  
Karel Perutka ◽  
Ivan Kuric ◽  
Ivan Zajačko ◽  
Vladimír Bulej ◽  
...  
Keyword(s):  
Decentralized Control ◽  
Control Method ◽  
Problem Formulation ◽  
Background Information ◽  
Recursive Identification ◽  
Point Change ◽  
Systems Identification ◽  
Self Tuning ◽  
Tito Systems ◽  
Quadratic Tracking

The content of this article is the presentation of methods used to identify systems before actual control, namely decentralized control of systems with Two Inputs, Two Outputs (TITO) and with two interactions. First, theoretical assumptions and reasons for using these methods are given. Subsequently, two methods for systems identification are described. At the end of this article, these specific methods are presented as the pre-identification of the chosen example. The Introduction part of the paper deals with the description of decentralized control, adaptive control, decentralized control in robotics and problem formulation (fixing the identification time at the existing decentralized self-tuning controller at the beginning of control and at the beginning of any set-point change) with the goal of a new method of identification. The Materials and methods section describes the used decentralized control method, recursive identification using approximation polynomials and least-squares with directional forgetting, recursive instrumental variable, self-tuning controller and suboptimal quadratic tracking controller, so all methods described in the section are those ones that already exist. Another section, named Assumptions, newly formulates the necessary background information, such as decentralized controllability and the system model, for the new identification method formulated in Pre-identification section. This section is followed by a section showing the results obtained by simulations and in real-time on a Coupled Drives model in the laboratory.

scholarly journals Modeling and control of a new robotic deburring system

Robotica ◽  
2005 ◽  
Vol 24 (2) ◽  
pp. 229-237 ◽  
Author(s):  
Jae H. Chung ◽  
Changhoon Kim
Keyword(s):  
Comparative Study ◽  
Decentralized Control ◽  
Control Method ◽  
Robotic Manipulator ◽  
Coordination Control ◽  
Pneumatic Actuators ◽  
Modeling And Control ◽  
Control Approach ◽  
Simulation Results ◽  
And Control

This paper discusses the modeling and control of a robotic manipulator with a new deburring tool, which integrates two pneumatic actuators to take advantage of a double cutting action. A coordination control method is developed by decomposing the robotic deburring system into two subsystems; the arm and the deburring tool. A decentralized control approach is pursued, in which suitable controllers were designed for the two subsystems in the coordination scheme. In simulation, three different tool configurations are considered: rigid, single pneumatic and integrated pneumatic tools. A comparative study is performed to investigate the deburring performance of the deburring arm with the different tools. Simulation results show that the developed robotic deburring system significantly improves the accuracy of the deburring operation.

scholarly journals Neural Network Self-Tuning Control for a Piezoelectric Actuator

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3342 ◽  
Author(s):  
Wenjun Li ◽  
Chen Zhang ◽  
Wei Gao ◽  
Miaolei Zhou
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Input Voltage ◽  
Rate Dependency ◽  
Output Displacement ◽  
Control Approach ◽  
Adaptive Parameter ◽  
Hysteresis Nonlinearity ◽  
Self Tuning ◽  
Control Methodology

Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.

Performance Enhancement of Pneumatic Vibration Isolators Using Self-Tuning PID Feedback and Time-Delay-Control (TDC)-Based Feedforward Scheme

2018 ◽  
Author(s):  
Jin-Wei Liang ◽  
Hung-Yi Chen ◽  
Lyu-Cyuan Zeng
Keyword(s):  
Mathematical Model ◽  
Performance Enhancement ◽  
Control Method ◽  
Hybrid Control ◽  
Model Performance ◽  
Experimental Investigations ◽  
Isolation System ◽  
Control Scheme ◽  
Self Tuning ◽  
Hybrid Control Scheme

A hybrid control scheme that combines a self-tuning PID-feedback loop and TDC-based feedforward scheme is proposed in this study to cope with an active pneumatic vibration isolator. In order to establish an effective TDC feedforward control a reliable mathematical model of the pneumatic isolator is required and developed firstly. Numerical and experimental investigations on the validity of the mathematical model are performed. It is found that although slight discrepancy exists between predicted and observed behaviors of the system, the overall model performance is acceptable. The resultant model is then applied in the design of the TDC feedforward scheme. A neuro-based adaptive PID control is integrated with the TDC feedforward algorithm to form the hybrid control. Numerical and experimental isolation tests are carried out to examine the suppression performances of the proposed hybrid control scheme. The results show that the proposed hybrid control method outperforms solely TDC feedforward while the latter outperforms the passive isolation system. Moreover, the proposed hybrid control scheme can suppress the vibration near the system’s resonance.

Decentralized Control Method of Multi-UAVs Arriving at the Same Time under the Influence of Wind

2013 ◽  
Vol 336-338 ◽  
pp. 595-598
Author(s):  
Xia Chen ◽  
Chong Zhang
Keyword(s):  
Control Problem ◽  
Decentralized Control ◽  
Control Method ◽  
Starting Point ◽  
Wind Simulation ◽  
Simulation Results ◽  
Multi Uav

Decentralized control method of multi-uav arriving at the same time is a typical control problem. This paper considers the above problem and presents a decentralized control method. Considering the dynamics and uncertainty of the battlefront, and the feature of the uav, according to the different starting point, flying orientation, and distance, established the algorithm model of the velocity and direction of uav under the influence of wind, presenting a decentralize control method of multi-uav arriving at the same time under the influence of wind. Simulation results are presented to demonstrate the effectiveness, robustness, flexibility of the approach.

Sign in / Sign up

Export Citation Format

Share Document