Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA

Accuracy Enhancement for High Precision Gantry Stage

Ph.DDOCTOR OF PHILOSOPH

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

The Iteration Domain Reference Governor, a Constraint Management Scheme for Batch Processes

In this work, a novel combination of Reference Governors (RG) and Iterative Learning Control (ILC) to address the issue of simultaneous learning and constraint management in systems that perform a task repeatedly is proposed. The proposed control strategy leverages the measured output from the previous iterations to improve tracking, while guaranteeing constraint satisfaction during the learning process. To achieve this, the plant is modeled by a linear system with uncertainties. An RG solution based on a robust Maximal Admissible Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. The proposed method is applied to the Scalar Reference Governor (SRG), the Vector Reference Governor (VRG) and the Command Governor (CG). An update law on the MAS is proposed to further improve performance

Joint CARE-ELAN, CARE-HHH-APD, and EUROTEV-WP3 Workshop on Electron Cloud Clearing

This report contains the Proceedings of the joint CARE-HHH-APD, CARE-ELAN, and EUROTEV-WP3 Mini-Workshop on 'Electron Cloud Clearing - Electron Cloud and Technical Consequences', "ECL2", held at CERN in Geneva, Switzerland, 1-2 March 2007). The ECL2 workshop explored novel technological remedies against electron-cloud formation in an accelerator beam pipe. A primary motivation for the workshop was the expected harmful electron-cloud effects in the upgraded LHC injectors and in future linear colliders, as well as recent beam observations in operating facilities like ANKA, CESR, KEKB, RHIC, and SPS. The solutions discussed at ECL2 included enamel-based clearing electrodes, slotted vacuum chambers, NEG coating, and grooves. Several of the proposed cures were assessed in terms of their clearing efficiency and the associated beam impedance. The workshop also reviewed new simulation tools like the 3D electron-ion build-up 'Faktor', modeling assumptions, analytical calculations, beam experiments, and laboratory measurements. Several open questions could be identified. The workshop reinforced inter-laboratory collaboration on electron-cloud suppression, and it concluded with a discussion of the next steps to be taken

Iterative learning control for robot manipulators

When a system is performing the same task repeatedly it is, from an engineering perspective, advantageous to use the knowledge from the previous iterations of the same task in order to reduce the error on successive trials. In control systems, the aim is to force the system output to follow a desired trajectory as closely as possible. Specific norms and measures of optimality are used to determine how close the output is to the desired trajectory. Although control theory provides many different possible solutions for such problem, it is not always possible to achieve a desired set of performance requirements. This may be due to the presence of unmodeled dynamics or parametric uncertainties exhibited during the system operation, or due to the lack of suitable design techniques for particular class of systems. Iterative learning control (ILC) is a relatively new addition to these techniques that, for a particular class of problems, can be used to overcome some of the difficulties associated with performance design of control systems

Hybrid Systems, Iterative Learning Control, and Non-minimum Phase

Hybrid systems have steadily grown in popularity over the last few decades because they ease the task of modeling complicated nonlinear systems. Legged locomotion, robotic manipulation, and additive manufacturing are representative examples of systems benefiting from hybrid modeling. They are also prime examples of repetitive processes; gait cycles in walking, product assembly tasks in robotic manipulation, and material deposition in additive manufacturing. Thus, they would also benefit substantially from Iterative Learning Control (ILC), a class of feedforward controllers for repetitive systems that achieve high performance in output reference tracking by learning from the errors of past process cycles. However, the literature is bereft of ILC syntheses from hybrid models. The main thrust of this dissertation is to provide a broadly applicable theory of ILC for deterministic, discrete-time hybrid systems, i.e. piecewise defined (PWD) systems. A type of ILC called Newton ILC (NILC) serves as the foundation for this mission due to its admittance of an unusually broad range of nonlinearities. Preventing the synthesis of NILC from hybrid models is the fact that contemporary hybrid modeling frameworks do not admit closed-form function composition of a single state transition formula capturing the complete hybrid system dynamics. This dissertation offers a new, closed-form PWD modeling framework to solve this problem. However, NILC itself is not without flaw. This dissertation's research reveals that it generally fails to converge when synthesized from models with unstable inverses (i.e. non-minimum phase (NMP) models), a class that includes flexible-link robotic manipulators. Thus, to fulfill the goal of providing the most broadly applicable control theory possible, improvement to NILC must be made to avoid the operation that causes divergence when applied to NMP systems (a particular matrix inversion). Stable inversion---a technique for generating stable state trajectories from unstable systems by decoupling their stable and unstable modes---is identified as a valuable tool in this endeavor. This concept is well-explored for linear time invariant systems, but stable inversion for hybrid systems has not been explored by the prior art. Thus, to focus the research, this dissertation specifically examines piecewise affine (PWA) systems (a subset of PWD systems) for the study of NMP hybrid system control. For PWA systems (and their PWD superset), in addition to a lack of stable inversion, a general, closed-form solution to the conventional inversion problem is also absent from the literature. Having a closed-form conventional inverse model is a prerequisite for stable inversion, but inversion of PWA models is nontrivial because the uniqueness of PWA system inverses is not guaranteed as it is for ordinary affine systems. Therefore, to achieve the first ILC of a hybrid system with an unstable inverse, theory for both conventional inversion and stable inversion must be delivered for PWA systems. In summary, the three main gaps addressed by this dissertation are (1) the lack of compatibility between existing hybrid modeling frameworks and ILC synthesis techniques, (2) the failure of NILC for NMP systems, and (3) the lack of inversion and stable inversion theory for PWA systems. These issues are addressed by (1) developing a closed-form representation for PWD systems, (2) developing a new ILC framework informed by NILC but free of matrix inversion, and (3) deriving conventional and stable model inversion theories for PWA systems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167929/1/ispiegel_1.pd