scholarly journals Synthesis of controllers for electromechanical tracking systems

2018 ◽  
Vol 28 (4) ◽  
pp. 47-51
Author(s):  
Yu. P. Korniushin ◽  
A. V. Mazin
Keyword(s):  
Moment Problem ◽  
Controller Design ◽  
Tracking Error ◽  
Control Object ◽  
Main Element ◽  
Current Phase ◽  
Time Interval ◽  
Initial State ◽  
Final State ◽  
Servo Systems

The article presents an algorithm for the synthesis of regulators for servomechanisms, in which the control object is linear. The algorithm is focused on electromechanical systems, the main element of which is a controlled electric drive. The synthesis algorithm is based on the use of the L–Markov moment problem. The advantage of the proposed algorithm with respect to the others is that the regulators synthesized on its basis ensure the minimization of energy for the objects control. The essence of the proposed algorithm of synthesis is that the control, according to the provisions underlying the L – moment problem, can be determined not only through the system of moment functions, but also through the moments. The latter depends both on the time interval during which the object is transferred from the initial state to the final state, but also on the state of the object itself. To solve the problem of controller design for servo systems, the following is proposed: 1. Explicitly express management through moments. 2. Perform «unfreezing» the initial and final states of the object. When «unfreezing» the initial state of the object is considered to be the current phase, and the final state is determined by the monitored signal. The time interval during which the object is transferred from one state to another is set arbitrarily in the general case. However, the longer this interval, the smaller the control rate, but, respectively, the greater the tracking error.

Fixed Structure Feedforward Controller Design Exploiting Iterative Trials: Application to a Wafer Stage and a Desktop Printer

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Stan H. van der Meulen ◽  
Rob L. Tousain ◽  
Okko H. Bosgra
Keyword(s):  
High Precision ◽  
Controller Design ◽  
Tracking Error ◽  
Quadratic Function ◽  
Detailed Knowledge ◽  
Convex Optimization Problem ◽  
Servo Systems ◽  
Feedforward Controller ◽  
Fixed Structure ◽  
Parameter Values

In this paper, the feedforward controller design problem for high-precision electromechanical servo systems that execute finite time tasks is addressed. The presented procedure combines the selection of the fixed structure of the feedforward controller and the optimization of the controller parameters by iterative trials. A linear parametrization of the feedforward controller in a two-degree-of-freedom control architecture is chosen, which results in a feedforward controller that is applicable to a class of motion profiles as well as in a convex optimization problem, with the objective function being a quadratic function of the tracking error. Optimization by iterative trials avoids the need for detailed knowledge of the plant, achieves the controller parameter values that are optimal with respect to the actual plant, and allows for the adaptation to possible variations that occur in the plant dynamics. Experimental results on a high-precision wafer stage and a desktop printer illustrate the procedure.

Enhanced data-driven optimal iterative learning control for nonlinear non-affine discrete-time systems with iterative sliding-mode surface

2020 ◽  
Vol 42 (11) ◽  
pp. 1923-1934 ◽  
Author(s):  
Rongrong Wang ◽  
Yangchun Wei ◽  
Ronghu Chi
Keyword(s):  
Discrete Time ◽  
Data Model ◽  
Iterative Learning Control ◽  
Controller Design ◽  
Sliding Mode ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Data Driven ◽  
Time Interval

In this work, an enhanced data-driven optimal iterative learning control (eDDOILC) is proposed for nonlinear nonaffine systems where a new iterative sliding mode surface (ISMS) is designed to replace the traditional tracking error in the controller design and analysis. It is the first time to extend the sliding mode surface to the iteration domain for systems operate repetitively over a finite time interval. By virtual of the new designed ISMS, the control design becomes more flexible where both the time and the iteration dynamics can be taken into account. Before proceeding to the controller design, an iterative dynamic linear data model is built between two consecutive iterations to formulate the linear input-output data relationship of the repetitive nonlinear nonaffine discrete-time system. The linear data model is virtual and does not have any physical meanings, which is very different to the traditional mechanism mathematical model. In the sequel, the eDDOILC is proposed by designing an objective function with respect to the proposed two-dimensional ISMS. Rigorous proof is provided to show the convergence of the proposed eDDOILC method. Furthermore, the results have been extended to a multiple-input multiple-output (MIMO) nonaffine nonlinear discrete-time repetitive system. In general, the proposed eDDOILC is data-driven where no explicit model information is included. It is illustrated that the presented eDDOILC is effective when applied to the nonlinear nonaffine uncertain systems.

scholarly journals RBFNN-Based Nonuniform Trajectory Tracking Adaptive Iterative Learning Control for Uncertain Nonlinear System with Continuous Nonlinearly Input

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chunli Zhang ◽  
Xu Tian ◽  
Lei Yan
Keyword(s):  
Nonlinear System ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Convergent Series ◽  
Iterative Learning ◽  
Time Interval ◽  
Uncertain Nonlinear System ◽  
Initial State ◽  
Adaptive Iterative Learning Control

This paper proposes an adaptive iterative learning control (AILC) method for uncertain nonlinear system with continuous nonlinearly input to solve different target tracking problem. The method uses the radial basis function neural network (RBFNN) to approximate every uncertain term in systems. A time-varying boundary layer, a typical convergent series are introduced to deal with initial state error and unknown bounds of errors, respectively. The conclusion is that the tracking error can converge to a very small area with the number of iterations increasing. All closed-loop signals are bounded on finite-time interval 0 , T . Finally, the simulation result of mass-spring mechanical system shows the correctness of the theory and validity of the method.

scholarly journals Dual subtractions

2021 ◽  
Vol 2021 (6) ◽  
Author(s):  
Renato Maria Prisco ◽  
Francesco Tramontano
Keyword(s):  
Field Theory ◽  
Quantum Field ◽  
Matrix Elements ◽  
Leading Order ◽  
Initial State ◽  
Final State ◽  
Dual Representation ◽  
Subtraction Scheme ◽  
Theoretical Predictions ◽  
Perturbative Quantum

Abstract We propose a novel local subtraction scheme for the computation of Next-to-Leading Order contributions to theoretical predictions for scattering processes in perturbative Quantum Field Theory. With respect to well known schemes proposed since many years that build upon the analysis of the real radiation matrix elements, our construction starts from the loop diagrams and exploits their dual representation. Our scheme implements exact phase space factorization, handles final state as well as initial state singularities and is suitable for both massless and massive particles.

Iterative learning control algorithm for sensor fault nonlinear systems

2020 ◽  
pp. 1-8
Author(s):  
Zimian Lan
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Open Loop ◽  
Iterative Learning ◽  
Sensor Fault ◽  
Initial State ◽  
Control Gain

In this paper, we propose a new iterative learning control algorithm for sensor faults in nonlinear systems. The algorithm does not depend on the initial value of the system and is combined with the open-loop D-type iterative learning law. We design a period that shortens as the number of iterations increases. During this period, the controller corrects the state deviation, so that the system tracking error converges to the boundary unrelated to the initial state error, which is determined only by the system’s uncertainty and interference. Furthermore, based on the λ norm theory, the appropriate control gain is selected to suppress the tracking error caused by the sensor fault, and the uniform convergence of the control algorithm and the boundedness of the error are proved. The simulation results of the speed control of the injection molding machine system verify the effectiveness of the algorithm.

scholarly journals Development and testing of a community flood resilience measurement tool

2016 ◽  
Author(s):  
Adriana Keating ◽  
Karen Campbell ◽  
Michael Szoenyi ◽  
Colin McQuistan ◽  
David Nash ◽  
...  
Keyword(s):  
Sustainable Livelihoods ◽  
Development Work ◽  
Measurement Tool ◽  
Disaster Resilience ◽  
Initial State ◽  
Final State ◽  
Sustainable Livelihoods Framework ◽  
Flood Characteristics ◽  
Over Time ◽  
Livelihoods Framework

Abstract. Given the increased attention on resilience-strengthening in international humanitarian and development work, there is a growing need to invest in its measurement and the overall accountability of "resilience strengthening" initiatives. We present a framework and tool for measuring community level resilience to flooding, built around the five capitals (5Cs) of the Sustainable Livelihoods Framework. At the time of writing the tool is being tested in 75 communities across 10 countries. Currently 88 potential sources of resilience are measured at the baseline (initial state) and endline (final state) approximately two years later. If a flood occurs in the community during the study period, resilience outcome measures are recorded. By comparing pre-flood characteristics to post flood outcomes, we aim to empirically verify sources of resilience, something which has never been done in this field. There is an urgent need for the continued development of theoretically anchored, empirically verified and practically applicable disaster resilience measurement frameworks and tools so that the field may: a) deepen understanding of the key components of "disaster resilience" in order to better target resilience enhancing initiatives, and b) enhance our ability to benchmark and measure disaster resilience over time, and compare how resilience changes as a result of different capacities, actions and hazards.

UML-BASED MODELING AND ANALYSIS OF SECURITY THREATS

2010 ◽  
Vol 20 (06) ◽  
pp. 875-897 ◽  
Author(s):  
JUN KONG ◽  
DIANXIANG XU ◽  
XIAOQIN ZENG
Keyword(s):  
Formal Method ◽  
Graph Transformation ◽  
State Transitions ◽  
Security Threats ◽  
Sequence Diagrams ◽  
Security Threat ◽  
Initial State ◽  
Final State ◽  
Modeling And Analysis ◽  
Software Application

Poor design has been a major source of software security problems. Rigorous and designer-friendly methodologies for modeling and analyzing secure software are highly desirable. A formal method for software development, however, often suffers from a gap between the rigidity of the method and the informal nature of system requirements. To narrow this gap, this paper presents a UML-based framework for modeling and analyzing security threats (i.e. potential security attacks) rigorously and visually. We model the intended functions of a software application with UML statechart diagrams and the security threats with sequence diagrams, respectively. Statechart diagrams are automatically converted into a graph transformation system, which has a well-established theoretical foundation. Method invocations in a sequence diagram of a security threat are interpreted as a sequence of paired graph transformations. Therefore, the analysis of a security threat is conducted through simulating the state transitions from an initial state to a final state triggered by method invocations. In our approach, designers directly work with UML diagrams to visually model system behaviors and security threats while threats can still be rigorously analyzed based on graph transformation.

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