A CENTER MANIFOLD THEORY-BASED APPROACH TO THE STABILITY ANALYSIS OF STATE FEEDBACK TAKAGI-SUGENO-KANG FUZZY CONTROL SYSTEMS

The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system

Simulation method of impact load for vehicle drivetrain on durability test rig

Fatigue and durability tests are important to develop or to optimize the vehicle drivetrain system. Using the vehicle drivetrain road load simulation test rig to reproduce the longitudinal driving load of the vehicle on the real road and the vertical impact load caused when the vehicle is on a bumpy pavement. In order to improve the control accuracy and convergence speed, an iterative learning control (ILC) method is presented. After 10 times of learning, the control error of iterative learning control method is 4.8 %, it is better than the 7.1 % error achieved by proportional-integral-derivative (PID) control. The simulation results demonstrate that the ILC can improve the convergence rate and increase the tracking accuracy than the PID control method

Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions

Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona

Hydraulic Press Commissioning Cost Reductions via Machine Learning Solutions

Abstract publicado en EUROSIM 2019 Abstract Volume. ARGESIM Report 58, ISBN: 978-3-901608-92-6 (ebook), DOI: 10.11128/arep.58In industrial processes, PI controllers remain as the dominant control technique due to their applicability and performance reliability. However, there could be applications where the PI controller is not enough to fulfill certain specifications, such as in the force control loop of hydraulic presses, in which specific pressure profiles need to be ensured in order not to damage theworkpiece. An Iterative Learning Control scheme is presented as a Machine Learning control alternative to the PI controller, in order to track the pressure profiles required for any operational case. Iterative Learning Control is based on the notion that a system that realizes the same process repeatedly, e.g. hydraulic presses, can improve its performance by learning from previous iterations. The improvements are revealed in high-fidelity simulations of a hydraulic press model, in which the tracking performance of the PI controller is considerably improved in terms of overshoot and the settling time of pressure signal.UPV/EHU, Grupo de Investigación de Inteligencia Computaciona

Model-Based Control Design of an EHA Position Control Based on Multicriteria Optimization

For the control of dynamic systems such as an Electro-Hydraulic Actuator (EHA), there is a need to optimize the control based on simulations, since a prototype or a physical system is usually not available during system design. In consequence, no system identification can be performed. Therefore, it is unclear how well a simulation model of an EHA can be used for multicriteria optimization of the position control due to the uncertain model quality. To evaluate the suitability for control optimization, the EHA is modeled and parameterized as a grey-box model using existing parameters independent of test bench experiments. A method for multi-objective optimization of a controller is used to optimize the position control of the EHA. Finally, the step responses are compared with the test bench. The evaluation of the step responses for different loads and control parameters shows similar behavior between the simulation model and the physical system on the test bench, although the essential phenomena could not be reproduced. This means that the model quality achieved by modeling is suitable as an indication for the optimization of the control by simulation without a physical system

Implementation of Iterative Learning Control on a Pneumatic Actuator.

Masters Degree. University of KwaZulu-Natal, Durban.Pneumatic systems play a pivotal role in many industrial applications, such as in petrochemical industries, steel manufacturing, car manufacturing and food industries. Besides industrial applications, pneumatic systems have also been used in many robotic systems. Nevertheless, a pneumatic system contains different nonlinear and uncertain behaviour due to gas compression, gas leakage, attenuation of the air in pipes and frictional forces in mechanical parts, which increase the system’s dynamic orders. Therefore, modelling a pneumatic system tends to be complicated and challenges the design of the controller for such a system. As a result, employing an effective control mechanism to precisely control a pneumatic system for achieving the required performance is essential. A desirable controller for a pneumatic system should be capable of learning the dynamics of the system and adjusting the control signal accordingly. In this study, a learning control scheme to overcome the highlighted nonlinearity problems is suggested. Many industrial processes are repetitive, and it is reasonable to make use of previously acquired data to improve a controller’s convergence and robustness. An Iterative Learning Control (ILC) algorithm uses information from previous repetitions to learn about the system’s dynamics. The ILC algorithm characteristics are beneficial in real-time control given its short time requirements for responding to input changes. Cylinder-piston actuators are the most common pneumatic systems, which translate the air pressure force into a linear mechanical motion. In industrial automation and robotics, linear pneumatic actuators have a wide range of applications, from load positioning to pneumatic muscles in robots. Therefore, the aim of this research is to study the performance of ILC techniques in position control of the rod in a pneumatic position-cylinder system. Based on theoretical analysis, the design of an ILC is discussed, showing that the controller can satisfactorily overcome nonlinearities and uncertainties in the system without needing any prior knowledge of the system’s model. The controller has been designed in such a way to even work on non-iterative processes. The performance of the ILC-controlled system is compared with a well-tuned PID controller, showing a faster and more accurate response

Iterative learning control in the commissioning of industrial presses

182 p.This thesis presents solutions to the control problems that exist nowadays in industrial presses, followed by a discussion of the most appropriate control schemes that may be used for their solution. Iterative Learning Control is subsequently analyzed, as the most promising control scheme for machine presses, due to its capability to improve the performance of a system that operates repeatedly.A novel Iterative Learning Control design is presented, which makes use of the dynamic characteristics of the system to improve the current controller performance and stability. This, results in an adaptation of the presented Iterative Learning Control design to two use cases: the single-input-single-output force control of mechanical presses and the multiple-input-multiple-output position control of hydraulic presses. While existing Iterative Learning Control approaches are also described and applied to the previously mentioned use cases, the presented novel approach has been shown to outperform the existing algorithms in terms of control performance.The proposed Iterative Learning control algorithms are validated in an experimental hydraulic test rig, in which the performance, robustness and stability of the algorithm have been demonstrated