Non-linear minimum variance estimation for discrete-time multi-channel systems

A nonlinear operator approach to estimation in discrete-time systems is described. It involves inferential estimation of a signal which enters a communications channel involving both nonlinearities and transport delays. The measurements are assumed to be corrupted by a colored noise signal which is correlated with the signal to be estimated. The system model may also include a communications channel involving either static or dynamic nonlinearities. The signal channel is represented in a very general nonlinear operator form. The algorithm is relatively simple to derive and to implement

Restricted structure predictive control for linear and nonlinear systems

An optimal predictive control algorithm is introduced for the control of linear and nonlinear discrete-time multivariable systems. The controller is specified in a 'restricted structure' form involving a set of given linear transfer-functions and a set of gains that minimise a Generalised Predictive Control (GPC) cost-index. The set of functions can be chosen as proportional, integral and derivative terms, however, a wide range of controller structures is possible. This is referred to as Restricted-Structure GPC control. The multi-step predictive control cost-function is novel, since it includes weightings on the ‘low-order’ controller gains and the rate of change of gains. This considerably improves the numerical computations ensuring critical inverse computations cannot lead to a singular matrix. It also provides the option of adding soft or hard constraints on the controller gains which provides additional flexibility for control design. The ability to include a plant model that can include a general nonlinear operator is also new for restricted structure control solutions. The low-order controller provides a potential improvement in robustness, since it is often less sensitive to plant uncertainties. The simple controller structure also enables relatively unskilled staff to retune the system using familiar tuning terms, and provides a potentially simpler QP problem for the constrained case

Editorial: literature survey

The Journal is always looking for ways to provide a better service for its readers and one of the new innovations that we shall be evaluating over the next year is to introduce a literature survey feature. This idea stems from the publishers John Wiley & Sons Limited, who have found this to be a very successful way of keeping readers informed of new developments in a subject on other journals

Observer based restricted structure generalized predictive control for quasi-LPV nonlinear systems

An observer based Restricted Structure Generalized Predictive Control (RS-GPC) algorithm is proposed. The novel feature is to assume the state-observer within the feedback loop is of reduced order. The aim is to inherit the natural robustness of low-order controllers and to provide a solution that may be easily simplified for real-time implementation. The nonlinear discrete-time, multivariable plant model is represented by a state-space system that may be in Linear Parameter Varying or State-Dependent forms. The controller gains are computed to minimize the type of cost-function that is found in traditional model predictive control but with some additional terms that enable gain magnitudes and the rate of change of control gains to be minimized. The cost-function also includes dynamically weighted tracking-error and control signal costing terms. The optimal controller includes a reduced order observer and a time-varying control gain matrix within the loop and background processing for the gain computations. Hard constraints may be imposed on the gain and rate of change of gain and on the control and output signals. Copyright (C) 2020 The Authors

LPV-MPC Path Planning for Autonomous Vehicles in Road Junction Scenarios

The control of an autonomous host vehicle at a crossroads intersection, in the presence of uncoordinated target vehicles, and without any crossing priority regulation is considered. The problem was spilt into two sub-problems, namely the priority and the path-planning problems. These problems are solved using a hierarchical controller. The lower control level is a linear controller to control the vehicle's speed and heading, to follow the reference signals provided by a middle-level algorithm. This middle-level controller is a Model Predictive Control (MPC) computed by modelling a unicycle model that is in a Linear Parameter-Varying (LPV) state-space model form. Different features are introduced for improving the prediction capability of the LPV-MPC. Prediction data computed by the MPC are used by the higher-level state-machine supervisor algorithm to determine when the host vehicle can safely cross the junction. The hierarchical controller was tested in simulation using a set of stressing scenarios. Reported results show the effectiveness of the proposed LPV-MPC in managing complex traffic scenarios with efficient compute