An adaptive power system stabiliser designed in the frequency domainHogg, B.W.; Wang, H.F.
doi: 10.1177/014233129401600402pmid: N/A
This paper presents a new adaptive Power System Stabiliser (PSS) designed in the frequency domain, which has been developed and implemented in the laboratory using a multi-transputer system. This stabiliser tracks the variations of operating conditions and system configurations of power systems by estimating the oscillations occurring in the systems, and adjusts its parameters on-line to supply required damping to suppress the oscillations. Nonlinear simulations of an unstable power system and experimental results on a laboratory model power system show that this adaptive PSS possesses good robustness to changes of the operating conditions and system configurations, and can considerably improve the system stability.
A high-performance multi-arm environment: path planning and practical implementationDodds, G.I.; Irwin, G.W.; Zalzala, A.M.S.
doi: 10.1177/014233129401600403pmid: N/A
This paper reports on the development of a working multi-arm robotic system at the Queen's University of Belfast (QUB). The practical implementation of the system involves motion coordination of two multi-joint RTX robots with six degrees-of-freedom, interfaced through transputers to a host SPARC-IPC workstation. A path-planning scheme is introduced to provide accurate and coordinated collision-free motion. In addition, taking into account the need for high productivity in industrial environments, minimum-time movements are imposed by increasing the manipulators'performance to a maximum, thus providing a high-performance workcell. A real-time case study and its implementation is included to show the validity and efficiency of the system.
Accurate multi-step-ahead prediction of non-linear systems using the MLP neural network with spread encodingGomm, J.B.; Lisboa, P.J.G.; Williams, D.; Evans, J.T.
doi: 10.1177/014233129401600404pmid: N/A
This paper focuses on the use of the standard multi-layer perceptron (MLP) neural network to provide accurate multi-step-ahead predictions of non-linear dynamical systems. A spread encoding method of representing continuous variables in a form suitable for presentation to an MLP is investigated. With this technique each numerical value is spread over the activity of several nodes at the inputs and outputs of the network. The main purpose of using spread encoding in this application is to form representations with sufficient accuracy to allow a neural network, trained using conventional feed-forward algorithms, to be used recursively. In this mode the network is required to predict the time evolution of the process output multiple time steps into the future, thus acting as a process model which has potential for improving control strategies that rely on a model of the plant and enhancing the performance of neural networks when used as simulation tools. The spread encoding form of data representation is compared to the conventional scaling method in an application of the MLP to modelling the response of a non-linear process. Results demonstrate that significant improvements in the neural network model prediction accuracy can be achieved using the spread encoding technique. The ability of the network model to capture the process dynamics is further illustrated by examining the localised frequency response of the network, in a novel application of spectral analysis techniques. The paper also includes introductory material on using neural networks for multi-step and single-step prediction.
Nonlinear control of an autonomous tracked vehiclePing Lu, ; Lin, Kuo-Chi
doi: 10.1177/014233129401600405pmid: N/A
The problem of motion control of an autonomous tracked vehicle is considered in this paper. While some well-known nonlinear control techniques are shown to be inapplicable to the problem, a recently developed nonlinear control approach is successfully applied. The performance of the controller in tracking desired trajectories is evaluated. The robustness of the controller is demonstrated by considering modelling uncertainties due to side slip of the vehicle.