Some new results on iterative learning control of noninteger order

Iterativno upravljanje putem učenja (ILC) predstavlja jedno od važnih oblasti u teoriji upravljanja i ono je moćan koncept upravljanja koji na iterativan način poboljšava ponašanje procesa koji su po prirodi ponovljivi. ILC je pogodno za upravljanje šire klase mehatroničkih sistema i posebno su pogodni za upravljanje na primer kretanja robotskih sistema koji imaju važnu ulogu u tehničkim sistemima koji uključuju sisteme upravljanja, primenu u vojnoj industriji itd. Ovaj se rad bavi problemom ILC upravljanja za nelinearne sisteme necelog reda sa vremenskim kašnjenjem. Posebno, ovde se proučavaju sistemi necelog reda sa nepoznatim ograničenim vremenskim kašnjenjem u prostoru stanja kao i slučaj vremenski promenljivog kašnjenja. Pri tome, dovoljni uslovi za konvergenciju u vremenskom domenu predloženog PDα ILC upravljanja za datu klasu necelog reda sistema sa kašnjenjem su prezentovani i dati u vremenskom domenu.Takođe, robusno PDα ILC upravljanje u direktnoj-povratnoj sprezi za dati sistem sa kašnjenjem je razmatrano.Posebno, razmatra se sistem necelog reda sa nepoznatim ali ograničenim konstantnim vremenskim kašnjenjem. Dovoljni uslovi za konvergenciju u vremenskom domenu predloženog PDα ILC upravljanja su dati odgovarajućom teoremom sa pratećim dokazom. Konačno, simulacioni primer pokazuje izvodljivost i efikasnost predloženog pristupa.Iterative learning control (ILC) is one of the recent topics in control theories and it is a powerful control concept that iteratively improves the behavior of processes repetitive in their nature. ILC is suitable for controlling a wider class of mechatronic systems - it is especially suitable for the motion control of robotic systems that attract and hold an important position in technical systems involving control applications, military industry, etc. This paper addresses the problem of iterative learning control (ILC) for fractional nonlinear time delay systems. Particularly, we study fractional order time delay systems in the state space form with unknown bounded constant time delay as well as time-varying delay. Sufficient conditions for the convergence of a proposed PDα type of a learning control algorithm for a class of fractional state space time delay systems are presented in the time domain. Also, a feedback-feed forward PDα type robust iterative learning control (ILC) of the given fractional order uncertain time delay system is considered. We consider fractional order time delay systems in the state space form with uncertain bounded constant time delay in particular. Sufficient conditions for the convergence in the time domain of the proposed PDα ILC are given by the corresponding theorem together with its proof. Finally, a simulation example shows the feasibility and effectiveness of the proposed approach

Some new results on iterative learning control of noninteger order

Iterativno upravljanje putem učenja (ILC) predstavlja jedno od važnih oblasti u teoriji upravljanja i ono je moćan koncept upravljanja koji na iterativan način poboljšava ponašanje procesa koji su po prirodi ponovljivi. ILC je pogodno za upravljanje šire klase mehatroničkih sistema i posebno su pogodni za upravljanje na primer kretanja robotskih sistema koji imaju važnu ulogu u tehničkim sistemima koji uključuju sisteme upravljanja, primenu u vojnoj industriji itd. Ovaj se rad bavi problemom ILC upravljanja za nelinearne sisteme necelog reda sa vremenskim kašnjenjem. Posebno, ovde se proučavaju sistemi necelog reda sa nepoznatim ograničenim vremenskim kašnjenjem u prostoru stanja kao i slučaj vremenski promenljivog kašnjenja. Pri tome, dovoljni uslovi za konvergenciju u vremenskom domenu predloženog PDα ILC upravljanja za datu klasu necelog reda sistema sa kašnjenjem su prezentovani i dati u vremenskom domenu.Takođe, robusno PDα ILC upravljanje u direktnoj-povratnoj sprezi za dati sistem sa kašnjenjem je razmatrano.Posebno, razmatra se sistem necelog reda sa nepoznatim ali ograničenim konstantnim vremenskim kašnjenjem. Dovoljni uslovi za konvergenciju u vremenskom domenu predloženog PDα ILC upravljanja su dati odgovarajućom teoremom sa pratećim dokazom. Konačno, simulacioni primer pokazuje izvodljivost i efikasnost predloženog pristupa.Iterative learning control (ILC) is one of the recent topics in control theories and it is a powerful control concept that iteratively improves the behavior of processes repetitive in their nature. ILC is suitable for controlling a wider class of mechatronic systems - it is especially suitable for the motion control of robotic systems that attract and hold an important position in technical systems involving control applications, military industry, etc. This paper addresses the problem of iterative learning control (ILC) for fractional nonlinear time delay systems. Particularly, we study fractional order time delay systems in the state space form with unknown bounded constant time delay as well as time-varying delay. Sufficient conditions for the convergence of a proposed PDα type of a learning control algorithm for a class of fractional state space time delay systems are presented in the time domain. Also, a feedback-feed forward PDα type robust iterative learning control (ILC) of the given fractional order uncertain time delay system is considered. We consider fractional order time delay systems in the state space form with uncertain bounded constant time delay in particular. Sufficient conditions for the convergence in the time domain of the proposed PDα ILC are given by the corresponding theorem together with its proof. Finally, a simulation example shows the feasibility and effectiveness of the proposed approach

Adaptive iterative learning control of robotic system based on particle swarm optimization

In this paper, an adaptive iterative learning control algorithm for robotic manipulators is proposed. A simplified robot manipulator model with 3 degrees of freedom is used as control object for verification purposes. The mathematical model is obtained via Rodriguez approach for modeling differential equations of motion for multi-body systems. The model itself is a simple open-chain kinematic structure. The proposed control system design consists of two layers of controllers. In the inner loop, feedback linearization is applied to deal with the model nonlinearities. Post feedback linearization advanced iterative learning control (ILC) algorithm of sign-D (signum-Derivative) type is introduced as feed-forward compensation with classical PD (Proportional-Derivative) controller in feedback closed loop. A particle swarm optimization (PSO) algorithm is used to optimize ILC gain parameters while gains for PD controller are set by trial and error. Suitable cost function based on position error is chosen for PSO algorithm in order to ensure convergence. Numerical simulation is carried out in two cases – case with constant learning gains and case with PSO optimized learning gains. It is observed that the proposed control law converges to some steady-state error value in both cases

Simultaneous Iterative Learning and Feedback Control Design

Iterative learning controllers aim to produce high precision tracking in operations where the same tracking maneuver is repeated over and over again. Model-based iterative learning control laws are designed from the system Markov parameters which could be inaccurate. Chapter 2 examines several important learning control laws and develops an understanding of how and when inaccuracy in knowledge of the Markov parameters results in instability of the learning process. While an iterative learning controller can compensate for unknown repeating errors and disturbances, it is not suited to handle non-repeating, stochastic errors and disturbances, which can be more effectively handled by a feedback controller. Chapter 3 explores feedback and iterative learning combination controllers, showing how a one-time step behind disturbance estimator and one-repetition behind disturbance estimator can be incorporated together in such a combination. Since learning control applications are finite-time by their very nature, frequency response based design techniques are not best suited for designing the feedback controller in this context. A finite-time feedback controller design approach is more appropriate given the overall aim of zero tracking error for the entire trajectory, even for shorter trajectories where the system response is still in its transient phase and has not yet reached steady state. Chapter 4 presents a combination of finite-time feedback and learning control as a natural solution for such a control objective, showing how a finite-time feedback controller and an iterative learning controller can be simultaneously synthesized during the learning process. Finally, Chapter 5 examines different configurations where a combination of a feedback controller and an iterative learning controller can be implemented. Numerical results are used to illustrate the feedback and iterative controller designs developed in this thesis

Safe-guarded multi-agent control for mechatronic systems: implementation framework and design patterns

This thesis addresses two issues: (i) developing an implementation framework for Multi-Agent Control Systems (MACS); and (ii) developing a pattern-based safe-guarded MACS design method.\ud \ud The Multi-Agent Controller Implementation Framework (MACIF), developed by Van Breemen (2001), is selected as the starting point because of its capability to produce MACS for solving complex control problems with two useful features:\ud • MACS is hierarchically structured in terms of a coordinated group of elementary and/or composite controller-agents;\ud • MACS has an open architecture such that controller-agents can be added, modified or removed without redesigning and/or reprogramming the remaining part of the MACS

Hybrid intelligent machine systems : design, modeling and control

To further improve performances of machine systems, mechatronics offers some opportunities. Traditionally, mechatronics deals with how to integrate mechanics and electronics without a systematic approach. This thesis generalizes the concept of mechatronics into a new concept called hybrid intelligent machine system. A hybrid intelligent machine system is a system where two or more elements combine to play at least one of the roles such as sensor, actuator, or control mechanism, and contribute to the system behaviour. The common feature with the hybrid intelligent machine system is thus the presence of two or more entities responsible for the system behaviour with each having its different strength complementary to the others. The hybrid intelligent machine system is further viewed from the system’s structure, behaviour, function, and principle, which has led to the distinction of (1) the hybrid actuation system, (2) the hybrid motion system (mechanism), and (3) the hybrid control system. This thesis describes a comprehensive study on three hybrid intelligent machine systems. In the case of the hybrid actuation system, the study has developed a control method for the “true” hybrid actuation configuration in which the constant velocity motor is not “mimicked” by the servomotor which is treated in literature. In the case of the hybrid motion system, the study has resulted in a novel mechanism structure based on the compliant mechanism which allows the micro- and macro-motions to be integrated within a common framework. It should be noted that the existing designs in literature all take a serial structure for micro- and macro-motions. In the case of hybrid control system, a novel family of control laws is developed, which is primarily based on the iterative learning of the previous driving torque (as a feedforward part) and various feedback control laws. This new family of control laws is rooted in the computer-torque-control (CTC) law with an off-line learned torque in replacement of an analytically formulated torque in the forward part of the CTC law. This thesis also presents the verification of these novel developments by both simulation and experiments. Simulation studies are presented for the hybrid actuation system and the hybrid motion system while experimental studies are carried out for the hybrid control system

Smart Exercise Adaptive Control of a Three Degree of Freedom Upper-limb Manipulator Robot

An adaptive velocity field controller for robotic manipulators is proposed in this thesis. The control objective is to cause the user to exercise in a manner that optimizes a criterion related to the user’s mechanical power. The control structure allows for passive user-manipulator physical interaction while the adaptive algorithm identifies the user’s biomechanical characteristics as a linear Hill based force-velocity curve defined at each pose of a repetitive exercise motion i.e. a Hill surface. The study of such a surface allows for the characterization of maximal effort exercise tasks and subsequently the control of exercises that is unique to each user. This allows for the intelligent characterization of a user’s abilities such that repetitive exercises defined by velocity fields can be safely performed. Such a study involving a 3DOF manipulator operating in full 3D has not been conducted in literature to the best of author’s knowledge. The proposed control structure is verified through experimentation on a unimanual setup of the BURT rehabilitation manipulator system involving a single user. The manipulator system includes friction, actuator/sensor noise, and unmodelled dynamics