Input/output linearization using dynamic recurrent neural networks

This paper brings together two areas of research that have received considerable attention during the last years, namely feedback linearization and neural networks. A proposition that guarantees the Input/Output (I/O) linearization of nonlinear control affine systems with Dynamic Recurrent Neural Networks (DRNNs) is formulated and proved. The proposition and the linearization procedure are illustrated with the simulation of a single link manipulator.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

Robust initialisation of Gaussian radial basis function networks using partitioned k-means clustering

Radial basis function networks can be trained quickly using linear optimisation once centres and other associated parameters have been initialised. The authors propose a small adjustment to a well accepted initialisation algorithm which improves the network accuracy over a range of problems. The algorithm is described and results are presented.

Stability of solutions of linear difference equations with periodic coefficients

This paper represents the last technical contribution of Professor Patrick Parks before his untimely death in February 1995. The remaining authors of the paper, which was subsequently completed, wish to dedicate the article to Patrick. A frequency criterion for the stability of solutions of linear difference equations with periodic coefficients is established. The stability criterion is based on a consideration of the behaviour of a frequency hodograph with respect to the origin of coordinates in the complex plane. The formulation of this criterion does not depend on the order of the difference equation.

Neural networks for linear control: an analysis

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

Dynamic recurrent neural network for system identification and control

A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller.

System identification using partitioned least squares

A novel partitioned least squares (PLS) algorithm is presented, in which estimates from several simple system models are combined by means of a Bayesian methodology of pooling partial knowledge. The method has the added advantage that, when the simple models are of a similar structure, it lends itself directly to parallel processing procedures, thereby speeding up the entire parameter estimation process by several factors.

Force distribution in manipulation by a robot hand with equality and inequality constraints

The problem of the appropriate distribution of forces among the fingers of a four-fingered robot hand is addressed. The finger-object interactions are modelled as point frictional contacts, hence the system is indeterminate and an optimal solution is required for controlling forces acting on an object. A fast and efficient method for computing the grasping and manipulation forces is presented, where computation has been based on using the true model of the nonlinear frictional cone of contact. Results are compared with previously employed methods of linearizing the cone constraints and minimizing the internal forces.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.

Predictive algorithm for the roll control autopilot of a jet fighter aircraft

Predictive controllers are often only applicable for open-loop stable systems. In this paper two such controllers are designed to operate on open-loop critically stable systems, each of which is used to find the control inputs for the roll control autopilot of a jet fighter aircraft. It is shown how it is quite possible for good predictive control to be achieved on open-loop critically stable systems.

Cybernetics – the modern science of systems

This article has been written in memory of Norbert Wiener and is dedicated to him. Takes a look at how cybernetics provides an extremely useful framework for the control and operation of real-world systems. With the true advent of computers and simple communications, many more processes can and will be viewed from a systems standpoint. Examples are given of how cybernetics can be applied to industrial processes and how it is seen as an important, integral part of future systems science.

A survey of multi-fingered robot hands: issues and grasping achievements

Recently a substantial amount of research has been done in the field of dextrous manipulation and hand manoeuvres. The main concern has been how to control robot hands so that they can execute manipulation tasks with the same dexterity and intuition as human hands. This paper surveys multi-fingered robot hand research and development topics which include robot hand design, object force distribution and control, grip transform, grasp stability and its synthesis, grasp stiffness and compliance motion and robot arm-hand coordination. Three main topics are presented in this article. The first is an introduction to the subject. The second concentrates on examples of mechanical manipulators used in research and the methods employed to control them. The third presents work which has been done on the field of object manipulation.

Dextrous hands: issues relating to a four-finger articulated hand

Manipulation of an object by a multi-fingered robot hand requires task planning which involves computation of joint space vectors and fingertip forces. To implement a task as fast as possible, computations have to be carried out in minimum time. The state of the art in manipulation by multi-fingered robot hand designs has shown the possible use of remotely driven finger joints. Such remotely driven hands require computation of tendon displacement for evaluating joint space vectors before signals are sent to actuators. Alternatively, a direct drive hand is a mechanical hand in which the shafts of articulated joints are directly coupled to the rotors of motors with high output torques. This article has been divided into two main sections. The first section presents a brief view of manipulation using a direct drive approach. Meanwhile, the other section presents ongoing research which is being carried out to design a four-finger articulated hand in the Department of Cybernetics at the University of Reading.

Expert system aid for design of overload line distribution networks

The use of expert system techniques in power distribution system design is examined. The selection and siting of equipment on overhead line networks is chosen for investigation as the use of equipment such as auto-reclosers, etc., represents a substantial investment and has a significant effect on the reliability of the system. Through past experience with both equipment and network operations, most decisions in selection and siting of this equipment are made intuitively, following certain general guidelines or rules of thumb. This heuristic nature of the problem lends itself to solution using an expert system approach. A prototype has been developed and is currently under evaluation in the industry. Results so far have demonstrated both the feasibility and benefits of the expert system as a design aid.