Hybrid iterative learning control of a flexible manipulator

This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied

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

Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking

In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties). The goal is to leverage knowledge from the source robot such that the target robot achieves high-accuracy trajectory tracking on arbitrary trajectories from the first attempt with minimal data recollection and training. Most existing approaches for multi-robot knowledge transfer are based on post-analysis of datasets collected from both robots. In this work, we study the feasibility of impromptu transfer of models across robots by learning an error prediction module online. In particular, we analytically derive the form of the mapping to be learned by the online module for exact tracking, propose an approach for characterizing similarity between robots, and use these results to analyze the stability of the overall system. The proposed approach is illustrated in simulation and verified experimentally on two different quadrotors performing impromptu trajectory tracking tasks, where the quadrotors are required to accurately track arbitrary hand-drawn trajectories from the first attempt.Comment: European Control Conference (ECC) 201

Experimental study of a two-DOF five bar closed-loop mechanism

This research is to carry out an experimental study to examine and verify the effectiveness of the control algorithms and strategies developed at the Advanced Engineering Design Laboratory (AEDL). For this purpose, two objectives are set to be achieved in this research. The first objective is to develop a generic experiment environment (test bed) such that different control approaches and algorithms can be implemented on it. The second objective is to conduct an experimental study on the examined control algorithms, as applied to the above test bed. To achieve the first objective, two main test beds, namely, the real-time controllable (RTC) mechanism and the hybrid machine, have been developed based on a two degree of freedom (DOF) closed-loop five-bar linkage. The 2-DOF closed-loop mechanism is employed in this study as it is the simplest of multi-DOF closed-loop mechanisms, and control approaches and conclusions based on a 2-DOF mechanism are generic and can be applied to a closed-loop mechanism with a higher number of degrees of freedom. The RTC mechanism test bed is driven by two servomotors and the hybrid machine is driven by one servomotor and a traditional CV motor. To achieve the second objective, an experimental study on different control algorithms has been conducted. The Proportional Derivative (PD) based control laws, i.e., traditional iii PD control, Nonlinear-PD (NPD) control, Evolutionary PD (EPD) control, non-linear PD learning control (NPD-LC) and Adaptive Evolutionary Switching-PD (AES-PD) are applied to the RTC mechanism; and as applied to the Hybrid Actuation System (HAS), the traditional PD control and the SMC control techniques are examined and compared. In the case of the RTC mechanism, the experiments on the five PD-based control algorithms, i.e., PD control, NPD control, EPD, NPD-LC, and AES-PD, show that the NPD controller has better performance than the PD controller in terms of the reduction in position tracking errors. It is also illustrated by the experiments that iteration learning control (ILC) techniques can be used to improve the trajectory tracking performance. However, AES-PD showed to have a faster convergence rate than the other ILC control laws. Experimental results also show that feedback ILC is more effective than the feedforward ILC and has a faster convergence rate. In addition, the results of the comparative study of the traditional PD and the Computed Torque Control (CTC) technique at both low and high speeds show that at lower speeds, both of these controllers provide similar results. However, with an increase in speed, the position tracking errors using the CTC control approach become larger than that of the traditional PD control. In the case of the hybrid machine, PD control and SMC control are applied to the mechanism. The results show that for the control of the hybrid machine and the range of speed used in this experimental study, PD control can result in satisfactory performance. However, SMC proved to be more effective than PD control

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended appropriately to frame the PID policy as a static state feedback policy. This renders PID tuning possible as the solution of a finite horizon optimal control problem without further a priori knowledge. The framework is applied to the task of balancing an inverted pendulum on a seven degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International Conference on Robotics and Automation (ICRA

Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation

Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation

Iterative learning control for improved tracking of fluid percussion injury device

Traumatic brain injury (TBI) afflicts over 10 million people around the world. Injury to the brain can occur from a variety of physical insults and the degree of disability can greatly vary from person to person. It is likely that the wide range of TBI outcomes may be due to the magnitude, direction, and forces of biomechanical insult acting on the head during such TBI events. Lateral Fluid Percussion (FPI) brain injury is one of the most commonly used and well-characterized experimental models of TBI. A Fluid Percussion Injury (FPI) device in the laboratory is used to replicate the injury but does not execute the desired pressure profile. The controller used is a QCI-S3-IG Silver Sterling from Quick Silver Controls. A limitation innate to the controller was a 3-millisecond sampling of the input signal that proved challenging for developing fast, accurate FPI pulses with periods as fast as 18-milliseconds. Iterative Learning Control is implemented which conditions the input signal to the open loop system offline such that the desired pressure profile is attained