Model-based Adaptive Approach Research Articles

High-quality sheet metal production using a model-based adaptive approach

Abstract Automatic panel benders of Salvagnini Maschinenbau GmbH enable high-quality and high-efficient production of sheet metal components. To achieve the steadily increasing requirements on precision, a model-based adaptive concept has been developed controlling the complete production process as a digital twin, from CAD data to the final sheet metal part. First, an overview of the underlying simulation models with different levels of detail is given. The models consider the elastoplastic deformation of the metal sheets as well as the elastic machine components and mechatronic models of the powertrain. Secondly, an overview is given of the adaptive production concept allowing the real-time adaption of the machine to changing process parameters like material properties. Finally, all production steps are controlled by a digital twin based on this model-based adaptive strategy. The paper demonstrates the successful transfer of scientific results to an industrial application.

Adaptive Fuzzy Synchronization Design and Parameter Identification for Chaotic Systems

In this paper, a new fuzzy model-based adaptive approach for synchronization of chaotic systems with unknown parameters. Theoretical analysis based on Lyapunov stability theory is provided to verify its feasibility. Takagi-Sugeno (T-S) fuzzy model is employed to express the chaotic systems. Based on this model, an adaptive fuzzy controller and the parameters update law are developed. With the proposed scheme, parameters identification and synchronization of identical or nonidentical chaotic systems can be achieved simultaneously. Numerical simulations further demonstrate the effectiveness of the proposed scheme.

Nonlinear Dynamic Model-Based Adaptive Control of a Solenoid-Valve System

In this paper, a nonlinear model-based adaptive control approach is proposed for a solenoid-valve system. The challenge is that solenoids and butterfly valves have uncertainties in multiple parameters in the nonlinear model; various kinds of physical appearance such as size and stroke, dynamic parameters including inertia, damping, and torque coefficients, and operational parameters especially, pipe diameters and flow velocities. These uncertainties are making the system not only difficult to adjust to the environment, but also further complicated to develop the appropriate control approach for meeting the system objectives. The main contribution of this research is the application of adaptive control theory and Lyapunov-type stability approach to design a controller for a dynamic model of the solenoid-valve system in the presence of those uncertainties. The control objectives such as set-point regulation, parameter compensation, and stability are supposed to be simultaneously accomplished. The error signals are first formulated based on the nonlinear dynamic models and then the control input is developed using the Lyapunov stability-type analysis to obtain the error bounded while overcoming the uncertainties. The parameter groups are updated by adaptation laws using a projection algorithm. Numerical simulation results are shown to demonstrate good performance of the proposed nonlinear model-based adaptive approach and to compare the performance of the same solenoid-valve system with a non-adaptive method as well.

Adaptive fuzzy synchronization of discrete-time chaotic systems

Abstract This paper presents a fuzzy model-based adaptive approach to synchronize two different discrete-time chaotic systems. Takagi–Sugeno (TS) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, an adaptive law is derived to estimate its unknown parameters. Then, a control law is proposed to stabilize the error dynamics. The design procedure is illustrated by two examples to demonstrate the effectiveness of the proposed methodology.

Speed control of differentially driven wheeled mobile robots—model-based adaptive approach

In this paper, the issue concerning model-based adaptive control for a differentially driven wheeled mobile robot is addressed. By choosing state variables directly related to its speeds (linear and angular), the robot's dynamic model becomes simple with good properties. The controller takes account of the robot dynamics and the coupling between the motions of two wheels, and thus achieves better speed tracking than commonly used model-free PID controller does. The effectiveness of the model free controllers (mostly PID) in some situations is also discussed. Simulation studies are done to verify the effectiveness of the proposed approaches. © 2005 Wiley Periodicals, Inc.

T-S 퍼지 모델을 이용한 불확실한 카오스 시스템의 적응동기화

본 논문은 퍼지 모델을 이용한 불확실한 카오스 시스템의 적응 동기화 기법을 제안한다. 카오스 동기화 시스템은 마스터 시스템과 슬레이브 시스템으로 구성되며 각각의 시스템은 Takagi-Sugeno (T-S) 퍼지 모델을 통해 표현된다. 마스터 시스템은 파라미터가 미리 알려지지 않은 불확실한 모델로 가정되므로 불확실한 파라미터를 추정하기 위해 적응 기법을 적용하여 슬레이브 시스템을 설계한다. 동기화 오차 시스템을 안정화하고 불확실한 파라미터를 추정하는 적응 규칙을 이용한 제어기를 설계하며 Lyapunov 이론을 통해 안정도를 해석한다. 제안된 퍼지 적응 동기화 기법의 효과를 확인하기 위해서 Duffing 시스템과 Lorenz 시스템에 대해 모의 실험을 수행한다. This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Doffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

Adaptive synchronization of T–S fuzzy chaotic systems with unknown parameters

Abstract This paper presents a fuzzy model-based adaptive approach for synchronization of chaotic systems which consist of the drive and response systems. Takagi–Sugeno (T–S) fuzzy model is employed to represent the chaotic drive and response systems. Since the parameters of the drive system are assumed unknown, we design the response system that estimates the parameters of the drive system by adaptive strategy. The adaptive law is derived to estimate the unknown parameters and its stability is guaranteed by Lyapunov stability theory. In addition, the controller in the response system contains two parts: one part that can stabilize the synchronization error dynamics and the other part that estimates the unknown parameters. Numerical examples, including Duffing oscillator and Lorenz attractor, are given to demonstrate the validity of the proposed adaptive synchronization approach.

Fault Detection and Isolation in Mechanical Systems Using Adaptive Friction Compensation

The problem of adaptive fault detection in linear dynamical systems is addressed in this paper. We consider a mechanical system described by a model with some deviations due to non linear frictions. Without any compensation, classical methods for instrument fault detection do not perform very well. To solve this problem, a model-based adaptive approach is proposed that is composed of two stages : estimation of the friction parameters and state estimation. The application to an inverted pendulum allows illustrates the method.