Weighting Matrix Design for Robust Monotonic Convergence in Norm Optimal Iterative Learning Control

In this paper we examine the robustness of norm optimal ILC with quadratic cost criterion for discrete-time, linear time-invariant, single-input single-output systems. A bounded multiplicative uncertainty model is used to describe the uncertain system and a sufficient condition for robust monotonic convergence is developed. We find that, for sufficiently large uncertainty, the performance weighting can not be selected arbitrarily large, and thus overall performance is limited. To maximize available performance, a time-frequency design methodology is presented to shape the weighting matrix based on the initial tracking error. The design is applied to a nanopositioning system and simulation results are presented

Learning control strategies for high-rate materials testing machines

Hydraulic high strain rate materials testing machines are required to track a user-defined velocity profile during tensile or compression tests in the face of sudden large impact forces. Due to delays and limited bandwidth of the actuation system, causal feedback/feedforward controllers fail to compensate for these disturbances. This paper presents more suitable non-causal learning control strategies, which anticipate the impact and take corrective action in advance. Two control strategies are discussed. The first comprises an iterative algorithm, which calculates a command signal correction by passing the velocity error observed in the previous test through an inverse model linearized around the target velocity. In the second approach, a detailed nonlinear inverse model is used to obtain a command signal from demand motion and force data. It is concluded that the first method is superior if two or more iterations can be performed. </jats:p

Coupling Movement Primitives: Interaction With the Environment and Bimanual Tasks

The framework of dynamic movement primitives (DMPs) contains many favorable properties for the execution of robotic trajectories, such as indirect dependence on time, response to perturbations, and the ability to easily modulate the given trajectories, but the framework in its original form remains constrained to the kinematic aspect of the movement. In this paper, we bridge the gap to dynamic behavior by extending the framework with force/torque feedback. We propose and evaluate a modulation approach that allows interaction with objects and the environment. Through the proposed coupling of originally independent robotic trajectories, the approach also enables the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion and, thus, modifies the trajectory in accordance to the desired positions or external forces. A stability analysis and results of simulated and real-world experiments using two KUKA LWR arms for bimanual tasks and interaction with the environment are presented. By expanding on the framework of DMPs, we keep all the favorable properties, which is demonstrated with temporal modulation and in a two-agent obstacle avoidance task

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

Next generation automotive embedded systems-on-chip and their applications

It is a well known fact in the automotive industry that critical and costly delays in the development cycle of powertrain1 controllers are unavoidable due to the complex nature of the systems-on-chip used in them. The primary goal of this portfolio is to show the development of new methodologies for the fast and efficient implementation of next generation powertrain applications and the associated automotive qualified systems-on-chip. A general guideline for rapid automotive applications development, promoting the integration of state-of-the-art tools and techniques necessary, is presented. The methods developed in this portfolio demonstrate a new and better approach to co-design of automotive systems that also raises the level of design abstraction.An integrated business plan for the development of a camless engine controller platform is presented. The plan provides details for the marketing plan, management and financial data.A comprehensive real-time system level development methodology for the implementation of an electromagnetic actuator based camless internal combustion engine is developed. The proposed development platform enables developers to complete complex software and hardware development before moving to silicon, significantly shortening the development cycle and improving confidence in the design.A novel high performance internal combustion engine knock processing strategy using the next generation automotive system-on-chip, particularly highlighting the capabilities of the first-of-its-kind single-instruction-multiple-data micro-architecture is presented. A patent application has been filed for the methodology and the details of the invention are also presented.Enhancements required for the performance optimisation of several resource properties such as memory accesses, energy consumption and execution time of embedded powertrain applications running on the developed system-on-chip and its next generation of devices is proposed. The approach used allows the replacement of various software segments by hardware units to speed up processing.1 Powertrain: A name applied to the group of components used to transmit engine power to the driving wheels. It can consist of engine, clutch, transmission, universal joints, drive shaft, differential gear, and axle shafts

Experimental Comparison of some Classical Iterative Learning Control Algorithms

This paper gives an overview of classical Iterative Learning Control algorithms. The presented algorithms are also evaluated on a commercial industrial robot from ABB. The presentation covers implicit to explicit model based algorithms. The result from the evaluation of the algorithms is that performance can be achieved by having more system knowledge

Experimental comparison of some classical iterative learning control algorithms

Abstract—This letter gives an overview of classical iterative learning control algorithms. The presented algorithms are also evaluated on a commercial industrial robot from ABB. The presentation covers implicit to explicit model-based algorithms. The result from the evaluation of the algorithms is that performance can be achieved by having more system knowledge. Index Terms—Design, experiment, industrial robot, iterative learning control. I