Computational approaches to motor control.

This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Optical tweezing beam control using liquid crystal adaptive optical elements

Liquid crystal (LC) adaptive optical elements are described, which provide an alternative to existing micropositioning technologies in optical tweezing. A full description of this work is given in [1]. An adaptive LC prism supplies tip/tilt to the phase profile of the trapping beam, giving rise to an available steering radius within the x-y plane of 10 μm. Additionally, a modally addressed adaptive LC lens provides defocus, offering a z-focal range for the trapping site of 100 μm. The result is full three-dimensional positional control of trapped particle(s) using a simple and wholly electronic control system. Compared to competing technologies, these devices provide a lower degree of controllability, but have the advantage of simplicity, cost and light efficiency. Furthermore, due to their birefringence, LC elements offer the opportunity of the creation of dual optical traps with controllable depth and separation.

Auto ID based control systems - An overview

This paper will provide a rationale for developing control systems based on the availability of automated identification (Auto ID) information provision. Much of the Auto-ID research has to date focussed on developing the essential infrastructure for dynamically extracting, networking and storing product data. These developments will help to revolutionise the accuracy, quality and timeliness of data acquired by Business Information Systems and should lead to major cost savings and performance improvements as a result. This paper introduces an additional phase of Auto ID research and development in which the nature of control system decisions is reconsidered in the light of the availability of ubiquitous, unique, item-level information. The paper will: (i) Indicate why the availability of ubiquitous, unique, item-level data can enable enhanced and fundamentally different control approaches and highlight potential benefits from control systems incorporating this Auto ID data (ii) Demonstrate what is required to develop control systems based around the availability of Auto ID data. (iii) Outline the research challenges in determining how such systems will be developed.

Vehicle trajectory linearisation to enable efficient optimisation of the constant speed racing line

A driver model is presented capable of optimising the trajectory of a simple dynamic nonlinear vehicle, at constant forward speed, so that progression along a predefined track is maximised as a function of time. In doing so, the model is able to continually operate a vehicle at its lateral-handling limit, maximising vehicle performance. The technique used forms a part of the solution to the motor racing objective of minimising lap time. A new approach of formulating the minimum lap time problem is motivated by the need for a more computationally efficient and robust tool-set for understanding on-the-limit driving behaviour. This has been achieved through set point-dependent linearisation of the vehicle model and coupling the vehicle-track system using an intrinsic coordinate description. Through this, the geometric vehicle trajectory had been linearised relative to the track reference, leading to new path optimisation algorithm which can be formed as a computationally efficient convex quadratic programming problem. © 2012 Copyright Taylor and Francis Group, LLC.

A neuro-inspired method for data rate limited feedback control

The nervous system implements a networked control system in which the plants take the form of limbs, the controller is the brain, and neurons form the communication channels. Unlike standard networked control architectures, there is no periodic sampling, and the fundamental units of communication contain little numerical information. This paper describes a novel communication channel, modeled after spiking neurons, in which the transmitter integrates an input signal and sends out a spike when the integral reaches a threshold value. The reciever then filters the sequence of spikes to approximately reconstruct the input signal. It is shown that for appropriate choices of channel parameters, stable feedback control over these spiking channels is possible. Furthermore, good tracking performance can be achieved. The data rate of the channel increases linearly with the size of the inputs. Thus, when placed in a feedback loop, small loop gains imply a low data rate. ©2010 IEEE.

Generalized vector control for brushless doubly fed machines with nested-loop rotor

This paper presents a generalized vector control system for a generic brushless doubly fed (induction) machine (BDFM) with nested-loop type rotor. The generic BDFM consists of p1/p2 pole-pair stator windings and a nested-loop rotor with N number of loops per nest. The vector control system is derived based on the basic BDFM equation in the synchronous mode accompanied with an appropriate synchronization approach to the grid. An analysis is performed for the vector control system using the generic BDFM vector model. The analysis proves the efficacy of the proposed approach in BDFM electromagnetic torque and rotor flux control. In fact, in the proposed vector control system, the BDFM torque can be controlled very effectively promising a high-performance BDFM shaft speed control system. A closed-loop shaft speed control system is composed based on the presented vector control system whose performance is examined both in simulations and experiments. The results confirm the high performance of the proposed approach in BDFM shaft speed control as well as a very close agreement between the simulations and experiments. Tests are performed on a 180-frame prototype BDFM. © 2012 IEEE.

Assessing boost-assist options for turbocharged engines using 1-D engine simulation and model predictive control

Delivering acceptable low end torque and good transient response is a significant challenge for all turbocharged engines. As downsized gasoline engines and Diesel engines make up a larger and larger proportion of the light-duty engines entering the market, the issue takes on greater significance. Several schemes have been proposed to improve torque response in highly boosted engines, including the use of electrical assist turbochargers and compressed air assist. In this paper we examine these methods with respect to their effectiveness in improving transient response and their relative performance along with some of the practical considerations for real world application. Results shown in this paper are from 1-D simulations using the Ricardo WAVE software package. The simulation model is based on a production light-duty Diesel engine modified to allow the introduction of compressed air at various points in the air-path as well as direct torque application to the turbocharger shaft (such as might be available from an electrical assist turbocharger). Whilst the 1-D simulation software provides a suitable environment for investigating the various boost assistance options, the overall air path performance also depends upon the control system. The introduction of boost assistance complicates the control in two significant ways: the system may run into constraints (such as compressor surge) that are not encountered in normal operation and the assistance introduces an additional control input. Production engine controllers are usually based on gain-scheduled PID control and extensive calibration. For this study, the non-linear nature of the engine together with the multiple configurations considered and the slower than real-time execution of 1-D models makes such an approach time consuming. Moreover, an ad-hoc approach would leave some doubt as to the fairness of comparisons between the different boost-assist options. Model Predictive Control has been shown to offer a convenient approach to controlling the 1-D simulations in a close to optimal manner for a typical Diesel VGT-EGR air path configuration. We show that the same technique can be applied to all the considered assistance methods with only modest calibration effort required. Copyright © 2012 SAE International.

Design of diesel smoke feedback control using a combination of PI control algorithm and performance optimization

A novel smoke sensor was used to realize smoke feedback control on a diesel engine. The controller design based on a combination of PI control algorithm and the engine performance optimization is described. Experimental results demonstrate how this control system behave to meet both of the speed and smoke requirements during engine transients.