Learning for Advanced Motion Control

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced Motion Control, 202

Controlled switching in Kalman filtering and iterative learning controls

“Switching is not an uncommon phenomenon in practical systems and processes, for examples, power switches opening and closing, transmissions lifting from low gear to high gear, and air planes crossing different layers in air. Switching can be a disaster to a system since frequent switching between two asymptotically stable subsystems may result in unstable dynamics. On the contrary, switching can be a benefit to a system since controlled switching is sometimes imposed by the designers to achieve desired performance. This encourages the study of system dynamics and performance when undesired switching occurs or controlled switching is imposed. In this research, the controlled switching is applied to an estimation process and a multivariable Iterative Learning Control (ILC) system, and system stability as well as system performance under switching are investigated. The first article develops a controlled switching strategy for the estimation of a temporal shift in a Laser Tracker (LT). For some reason, the shift cannot be measured at all time. Therefore, a model-based predictor is adopted for estimation when the measurement is not available, and a Kalman Filter (KF) is used to update the estimate when the measurement is available. With the proposed method, the estimation uncertainty is always bounded within two predefined boundaries. The second article develops a controlled switching method for multivariable ILC systems where only partial outputs are measured at a time. Zero tracking error cannot be achieved for such systems using standard ILC due to incomplete knowledge of the outputs. With the developed controlled switching, all the outputs are measured in a sequential order, and, with each currently-measured output, the standard ILC is executed. Conditions under which zero convergent tracking error is accomplished with the proposed method are investigated. The proposed method is finally applied to solving a multi-agent coordination problem”--Abstract, page iv

Decentralized Trajectory Tracking Control for Soft Robots Interacting With the Environment

Despite the classic nature of the problem, trajectory tracking for soft robots, i.e., robots with compliant elements deliberately introduced in their design, still presents several challenges. One of these is to design controllers which can obtain sufficiently high performance while preserving the physical characteristics intrinsic to soft robots. Indeed, classic control schemes using high-gain feedback actions fundamentally alter the natural compliance of soft robots effectively stiffening them, thus de facto defeating their main design purpose. As an alternative approach, we consider here using a low-gain feedback, while exploiting feedforward components. In order to cope with the complexity and uncertainty of the dynamics, we adopt a decentralized, iteratively learned feedforward action, combined with a locally optimal feedback control. The relative authority of the feedback and feedforward control actions adapts with the degree of uncertainty of the learned component. The effectiveness of the method is experimentally verified on several robotic structures and working conditions, including unexpected interactions with the environment, where preservation of softness is critical for safety and robustness

Multi-Robot Transfer Learning: A Dynamical System Perspective

Multi-robot transfer learning allows a robot to use data generated by a second, similar robot to improve its own behavior. The potential advantages are reducing the time of training and the unavoidable risks that exist during the training phase. Transfer learning algorithms aim to find an optimal transfer map between different robots. In this paper, we investigate, through a theoretical study of single-input single-output (SISO) systems, the properties of such optimal transfer maps. We first show that the optimal transfer learning map is, in general, a dynamic system. The main contribution of the paper is to provide an algorithm for determining the properties of this optimal dynamic map including its order and regressors (i.e., the variables it depends on). The proposed algorithm does not require detailed knowledge of the robots' dynamics, but relies on basic system properties easily obtainable through simple experimental tests. We validate the proposed algorithm experimentally through an example of transfer learning between two different quadrotor platforms. Experimental results show that an optimal dynamic map, with correct properties obtained from our proposed algorithm, achieves 60-70% reduction of transfer learning error compared to the cases when the data is directly transferred or transferred using an optimal static map.Comment: 7 pages, 6 figures, accepted at the 2017 IEEE/RSJ International Conference on Intelligent Robots and System

Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation

Queißer J. Multi-modal Skill Memories for Online Learning of Interactive Robot Movement Generation. Bielefeld: Universität Bielefeld; 2018.Modern robotic applications pose complex requirements with respect to the adaptation of actions regarding the variability in a given task. Reinforcement learning can optimize for changing conditions, but relearning from scratch is hardly feasible due to the high number of required rollouts. This work proposes a parameterized skill that generalizes to new actions for changing task parameters. The actions are encoded by a meta-learner that provides parameters for task-specific dynamic motion primitives. Experimental evaluation shows that the utilization of parameterized skills for initialization of the optimization process leads to a more effective incremental task learning. A proposed hybrid optimization method combines a fast coarse optimization on a manifold of policy parameters with a fine-grained parameter search in the unrestricted space of actions. It is shown that the developed algorithm reduces the number of required rollouts for adaptation to new task conditions. Further, this work presents a transfer learning approach for adaptation of learned skills to new situations. Application in illustrative toy scenarios, for a 10-DOF planar arm, a humanoid robot point reaching task and parameterized drumming on a pneumatic robot validate the approach. But parameterized skills that are applied on complex robotic systems pose further challenges: the dynamics of the robot and the interaction with the environment introduce model inaccuracies. In particular, high-level skill acquisition on highly compliant robotic systems such as pneumatically driven or soft actuators is hardly feasible. Since learning of the complete dynamics model is not feasible due to the high complexity, this thesis examines two alternative approaches: First, an improvement of the low-level control based on an equilibrium model of the robot. Utilization of an equilibrium model reduces the learning complexity and this thesis evaluates its applicability for control of pneumatic and industrial light-weight robots. Second, an extension of parameterized skills to generalize for forward signals of action primitives that result in an enhanced control quality of complex robotic systems. This thesis argues for a shift in the complexity of learning the full dynamics of the robot to a lower dimensional task-related learning problem. Due to the generalization in relation to the task variability, online learning for complex robots as well as complex scenarios becomes feasible. An experimental evaluation investigates the generalization capabilities of the proposed online learning system for robot motion generation. Evaluation is performed through simulation of a compliant 2-DOF arm and scalability to a complex robotic system is demonstrated for a pneumatically driven humanoid robot with 8-DOF

Time-frequency analysis of position-dependent dynamics in an Iteratively Controlled Waferstage

Abstract In this report signal processing, applied for the analysis of control signals, is exploited in the investigation of position-dependent dynamics in a motion system, i.e. a wafer stage apparatus with Iterative Learning Control (ILC). Based on the Matching Pursuit [1],[2] algorithm, which is used to decompose signals with respect to a multiple complex dictionary of atoms, a high-resolution signal energy distribution is derived in the time-frequency plane, which does not include cross-terms, like Wigner distribution. A thorough high-resolution time-frequency analysis of servo error signals of the controlled system will provide much insight in the appearance and propagation of the low energetic position-dependent dynamics encountered