Kenneth R. Uren

An Energy Perspective on Modelling, Supervision, and Control of Large-Scale Industrial Systems: Survey and Framework

Abstract Energy is a universal concept that can be used across physical domains to describe complex large-scale industrial systems. This brief survey and framework gives a perspective on energy as a unifying domain for system modelling, supervision, and control. Traditionally, modelling and control problems have been approached by adopting a signal-processing paradigm. However, this approach becomes problematic when considering non-linear systems. A behavioural viewpoint, which incorporates energy as basis for modelling and control, is considered a viable solution. Since energy is seen as a unifying concept, its relationship to Euler-Lagrange equations, state space representation, and Lyapunov functions is discussed. The connection between control and process supervision using passivity theory coupled with a system energy balance is also established. To show that complex industrial systems comprising multiple energy domains can be modelled by means of a single electric circuit, its application to a large-scale thermo-hydraulic system is presented. Next, a simple non-linear transmission impedance electric circuit is used to illustrate how energy can be used to not only describe a system, but also serve as basis for system optimisation. An energy-based framework is proposed whereby energy is used as a unifying domain to work in, to analyse, and to optimise large-scale industrial systems.

Adaptive Neural network control of a helicopter system with optimal observer and actor-critic design

Abstract This paper proposes a methodology for developing an adaptive neural network controller for a simulated helicopter system. Since an indirect adaptive neural network framework is chosen, the controller comprises three interconnected neural networks called the observer, actor and critic. The actor and critic networks rely on the observer network responsible for state estimation. The main contribution of this paper is the development of an observer that has fast convergence capabilities in order to be used in a completely on-line stability control scheme. This improved convergence is obtained by uniquely modifying the observer network structure and update law. The observer parameters are also optimised by means of a genetic algorithm (GA) for improved performance. The developed observer is firstly evaluated on a first principle linear model and then on actual test flight data of an attack helicopter. The results indicate excellent performance in terms of the state estimation capability of the observer. Lyapunov’s direct method is used to derive update laws for both the critic and actor networks and the control parameters of these networks are also optimised by means of a multi-objective GA. Actual data from a wind-tunnel test set-up were used for controller evaluation purposes.

State estimation for a hexapod robot

This paper introduces a state estimation methodology for a hexapod robot that makes use of proprioceptive sensors and a kinematic model of the robot. The methodology focuses on providing reliable full pose state estimation for a commercially-available hexapod robot platform with the use of only commonly-available sensors. The presented methodology provides the derivation of the kinematic model and implements an Extended Kalman Filter (EKF) state estimation framework similar to that recently validated on a quadruped. The EKF fuses the kinematic model with on-board IMU measurements to estimate the pose of the robot. The methodology was tested with experiments using a physical hexapod robot and validated with independent ground truth measurements.

Series-Parallel Approach to On-line Observer based Neural Control of a Helicopter System

National Research Foundation of South Africa (Grant specific unique reference number (UID: 80020)

Interdependent multi-objective sizing and control optimisation of a renewable energy hydrogen system

Abstract This paper presents a sizing and control optimisation architecture for the design and evaluation of a small-scale stand-alone hybrid PV-wind-battery system for the production of hydrogen (H 2 ) using proton exchange membrane (PEM) technology. Three objectives are considered simultaneously namely cost, efficiency and reliability. For this task an optimisation approach is developed combining a single objective genetic algorithm (GA) with a multi-objective GA (MOGA) to optimise nine system sizing variables and six power management system control set-point variables. The nine sizing and six control variables are combined to form a solution vector. The optimisation algorithm searches the search space, with user defined boundaries, for non-dominated solution vectors. The result is a set of solution vectors which are useful in the selection of components for the design and evaluation of these systems. The optimisation approach developed sufficiently searches the bounded search space and provides results in the form of a set of non-dominated solution vectors. These results are useful in understanding how the different components of such a non-linear complex system affect each other as well as the three objectives considered in this study.

Flight controller and low-cost test environment for a simulated helicopter

Abstract In this paper, optimal linear control techniques are utilised to control a radio-controlled helicopter (30 size) in the AeroSIMRC simulation environment. A grey-box time-domain system identification method is used to estimate a linear state space model that operates in hover mode. Identifying the unknown parameters in the model is highly dependent on the initial values and the input data. The model is divided into sub-systems to make estimation possible. The identified state space model shows a good measure of fit compared to the simulators flight data. A linear quadratic controller forms the inner-loop, and an optimised PID outer-loop generates attitude commands from a given inertial position trajectory. An observer estimates the unmeasured states such as blade flapping. The controller is developed in Simulink® with a plug-in written for the AeroSIMRC flight simulator. The plug-in enables Simulink® to control the simulator through a User Datagram Protocol interface for the purpose of model and controller validation. The proposed methodology facilitates a low cost test environment for new flight control algorithms. The simulation results show that the controller keeps the helicopter stable in the presence of considerable environmental disturbances and plant uncertainties.