Single-track hard disk drive (HDD) seek performance is measured by settle time, ts. In this paper, we show the effective use of feedforward dynamic inversion, coupled with reference trajectory yd generation, to achieve high performance ts. Models of HDD dynamics are typically nonminimum phase (NMP), and it is well known that the exact tracking solution for NMP systems requires noncausal preactuation to maintain bounded internal signals. In the specific HDD operating modes of interest, anticipation of a seek command is unrealistic, and thus preactuation adds to the overall computation of settle time. Unlike many dynamic inversion tracking applications, this negative effect of preactuation leads to interesting trade-offs between preactuation delay, yd tracking accuracy, and achievable settle performance. We investigate multiple single-input single-output (SISO) inversion architectures, and we show that the feedforward closedloop inverse (FFCLI) achieves superior settle performance to the feedforward plant inverse (FFPI) in our application because FFCLI does not excite the closed-loop dynamics. Using the FFCLI architecture, we further investigate numerous NMP inversion algorithms, including both exact inversion schemes with initial condition preloading and stable approximate NMP inverse techniques. We conclude that the settle performance of the zeroorder Taylor series stable NMP approximation matches the best performance of the exact inversion techniques in our application, and does so without the high frequency excitation required by the Zero Magnitude Error Tracking Controller (ZMETC), or the excessive preactuation required by the Zero Phase Error Tracking Controller (ZPETC). Minimum energy optimal trajectory generation methods show that the system order n is a limiting factor in settle performance. This confirms that the zero-order series method, which is capable of producing settle times in less than n samples, is on par with optimal approaches yet much simpler to implement. Multiple NMP inversion algorithms are experimentally validated on a Servo Track Writer (STW), which reinforces the general trends observed in ideal simulations.

We compare two common model inversion architectures, plant inverse (PI) and closed-loop inverse (CLI), by evaluating their ability to achieve settle time performance improvements. The plant models of interest are discretetime, single-input single-output (SISO), linear time-invariant (LTI), nonminimum phase (NMP), and uncertain. We use a simple algebraic analysis to show that PI and CLI yield the same desired to actual output dynamics if the plant is minimum phase. Using a stable inverse approximation when the plant is certain but NMP, the same algebraic analysis shows that CLI achieves superior settle time performance relative to PI when the settle boundaries are tight. Simulation and experimental data are used to derive conclusions when the plant is NMP and uncertain. We show that CLI has superior performance over PI for our plant dynamics of interest when low frequency parametric uncertainty is present. For higher frequency unstructured uncertainty, the distinction between the two inversion architectures is negligible.

In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the di#erent iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively compensate for and, hence, remove this repetitive error. In the thesis

An algorithm for Iterative Learning Control is developed based on an optimization principle which has been used previously to derive gradient type algorithms. The new algorithm has numerous benefits which include realization in terms of Riccati feedback and feedforward components. This realization also has the advantage of implicitly ensuring automatic step size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm is expressed as a very general norm optimization problem in a Hilbert space setting and hence, in principle, can be used for both continuous and discrete time systems. A basic relationship with almost singular optimal control is outlined. The theoretical results are illustrated by simulation studies which highlight the dependence of the speed of convergence on parameters chosen to represent the norm of the signals appearing in the optimization problem. Contents 1 Introduction 1 2 Norm Optimal Iterative Learning Cont...

This paper is concerned with iterative learning control of Hamiltonian systems, which is applicable to electromechanical systems. A novel iterative learning control scheme is proposed based the self-adjoint structure of the variational of those systems. This method does not require either the physical parameters of the target system nor the time derivatives of output signals. A concrete and effective learning algorithm for mechanical systems is also derived. Furthermore, experiments of a robot manipulator demonstrates the effectiveness of the proposed method.

An algorithm for Iterative Learning Control is proposed based on an optimization principle used by other authors to derive gradient type algorithms. The new algorithm is a descent algorithm and has potential benefits which include realization in terms of Riccati feedback and feedforward components. This realization also has the advantage of implicitly ensuring automatic step size selection and hence guaranteeing convergence without the need for empirical choice of parameters. The algorithm achieves a geometric rate of convergence for invertible plants. One important feature of the proposed algorithm is the dependence of the speed of convergence on weight parameters appearing in the norms of the signals chosen for the optimization problem. Keywords: Iterative learning control, 2D systems, optimal control, singular optimal control, reference-input tracking, descent methods. Contents 1 Introduction 1 2 Norm Optimal Iterative Learning Control 2 2.1 Problem Formulation : : : : : : : : : ...

Learning Control of Complex Skills by Lara Sidonie Crawford Doctor of Philosophy in Biophysics University of California at Berkeley Professor S. Shankar Sastry, Chair This dissertation presents a hierarchical controller which can learn to perform complex motor skills. Humans routinely coordinate many degrees of freedom smoothly and effortlessly to achieve complex goals. Moreover, we are good at learning new patterns of coordination to produce new skills. Robots and artificial systems, on the other hand, typically have difficulty with the kinds of behaviors that come most naturally to us. Skills such as running, skiing, playing basketball, or diving involve complex nonlinear dynamics, many degrees of freedom, and behavioral goals that can be difficult to specify mathematically; goals such as "ski down the mountain without falling down" or "shoot a layup" must be translated from linguistic requirements into dynamic system constraints. The focus in this dissertation will be on the skill...

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.

A simple iterative learning controller (ILC) is proposed for the tracking control of uncertain discrete-time nonlinear systems performing the repetitive tasks. The tracking error of the current learning iteration is utilized in the ILC updating law. It is proven that, under relaxed conditions, the final tracking error is bounded in the presence of uncertainty, disturbance and the initialization error. Furthermore, the tracking error bound and the ILC convergence rate can be tuned by the learning gain of the current iteration tracking error in the ILC updating law. The effectiveness of the proposed ILC scheme is illustrated by a simulation. 1 Introduction Learning is a bridge between the knowledge and the experience. Iterative Learning Control (ILC) utilizes the system repetitions as the experience to compensate the lack of knowledge which gives satisfactory performance iteratively. For a good survey, see [1, 2]. As ILC is essentially a memory-based method, for implementation of ILC ...

We present an iterative learning controller that applies to nonholonomic mobile robots as well as to other systems which can be put in chained form. The learning algorithm exploites the fact that chained-form systems are linear under piecewiseconstant inputs. The proposed control scheme requires the execution of a small number of experiments in order to drive the system to the desired state in finite time, with nice convergence and robustness properties with respect to modeling inaccuracies as well as disturbances. To avoid the necessity of exactly re-initializing the system at each iteration, the basic method is modified so as to obtain a cyclic controller, by which the system is cyclically steered among an arbitrary sequence of states. As a case study, a car-like mobile robot is considered. Both simulation and experimental results are reported in order to show the performance of the method. August 5, 1997 # Corresponding Author 1 Introduction Many robotic systems for advanced appl...