J.A. De Dona

Positive invariant sets for fault tolerant multisensor control schemes

This article deals with fault tolerant multisensor control schemes for systems with linear dynamics. Positive invariance is a common analysis and control design tool for systems affected by bounded constraints and disturbances. This article revisits the construction of ε-approximations of minimal robust positive invariant sets for linear systems upon contractive set-iterations. The cases of switching between different sets of disturbances and the inclusion of a predefined region of the state space are treated in detail. All these results are used in multisensor control schemes which have to deal with specific problems originated by the switching between different estimators and by the presence of faults in some of the sensors. The construction of positive invariant sets for different operating regimes provides, in this context, effective fault detection information. Within the same framework, global stability of the switching strategies can be assured if the invariant sets topology allows the exclusive selection of estimates obtained from healthy sensors.

Enlarged terminal sets guaranteeing stability of receding horizon control

Abstract The purpose of this paper is to relax the terminal conditions typically used to ensure stability in model predictive control, thereby enlarging the domain of attraction for a given prediction horizon. Using some recent results, we present novel conditions that employ, as the terminal cost, the finite-horizon cost resulting from a nonlinear controller u =−sat( Kx ) and, as the terminal constraint set, the set in which this controller is optimal for the finite-horizon constrained optimal control problem. It is shown that this solution provides a considerably larger terminal constraint set than is usually employed in stability proofs for model predictive control.

Fault Tolerant Control Allowing Sensor Healthy-to-Faulty and Faulty-to-Healthy Transitions

In this paper, we present a new sensor fault tolerant control scheme for linear, discrete-time systems. The scheme consists of a bank of estimators, each associated with a sensor or group of sensors, a mechanism for the detection and identification of sensor transitions from healthy-to-faulty and faulty-to-healthy operation, an estimate reconfiguration module, and an estimate-based feedback controller with reference tracking. The detection and identification approach is based on the separation of “healthy” and “faulty” sets, where appropriately selected residual variables remain under healthy or faulty operation, from “after-fault” and “after-recovery” sets, towards which the residual variables “jump” when abrupt sensor faults or recoveries occur in one or more groups of sensors. This “set-separation” approach provides pre-checkable fault tolerance guarantees whenever certain conditions, given in terms of systems known data such as plant and estimator dynamics and bounds on reference and disturbance signals, are satisfied.

Elucidation of the state-space regions wherein model predictive control and anti-windup strategies achieve identical control policies

This paper explores the situations in which two well-known (and so far regarded as non-related) techniques for dealing with saturating actuators are equivalent. Model predictive control, on the one hand, is based on a receding horizon quadratic optimal control problem which is solved subject to input constraints. On the other hand, anti-windup methods are ad-hoc procedures which achieve input saturation in an instantaneous fashion. Both methods are known to perform well in practice and each has its strong advocates. In this paper we characterise regions of the state-space wherein both techniques provide identical solutions.

Brief Combining switching, over-saturation and scaling to optimise control performance in the presence of model uncertainty and input saturation

Saturating actuators are present in all real control systems. Their effect on system performance clearly depends on the range of control action required relative to the saturation bounds. Much of the prior work on this topic has centred on how to switch linear controllers so as to avoid saturation occurring. This, however, has meant that the full input authority has not been exploited in the control law. Recently, two alternative methods have been proposed for switching linear controllers so as to force the input into saturation. They achieve this goal by scaling the controls or by allowing over-saturation in the switching scheme. In this paper the two methods are combined into a more general scheme. It is also shown that the combined scheme is capable of achieving superior performance. A robust version of the algorithm is also described which is applicable to a class of uncertain systems.

A Flatness-Based Iterative Method for Reference Trajectory Generation in Constrained NMPC

This paper proposes a novel methodology that combines the differential flatness formalism for trajectory generation of nonlinear systems, and the use of a model predictive control (MPC) strategy for constraint handling. The methodology consists of a trajectory generator that generates a reference trajectory parameterised by splines, and with the property that it satisfies performance objectives. The reference trajectory is generated iteratively in accordance with information received from the MPC formulation. This interplay with MPC guarantees that the trajectory generator receives feedback from present and future constraints for real-time trajectory generation.

Fault-tolerant control of systems with convex polytopic linear parameter varying model uncertainty using virtual-sensor-based controller reconfiguration

Abstract In this paper, a novel robust sensor fault tolerant control strategy for systems with linear parameter varying (LPV) uncertainty model description is proposed. The strategy combines a robust fault detection and identification (FDI) unit based on an invariant-set approach with controller reconfiguration based on the use of a virtual sensor. The robust FDI unit employs a bank of observers that can detect faulty and healthy situations based on the separation of relevant sets, whose computation takes into account system disturbances and model uncertainty. The closed-loop system is reconfigured by means of a virtual sensor which is adapted to the fault situation detected by the FDI unit. The FDI and virtual sensor modules are designed using polytopic LPV techniques and bilinear matrix inequalities. The resulting robust fault tolerant control scheme is guaranteed to preserve boundedness of the closed-loop system trajectories under a wide range of sensor fault scenarios. The performance of the proposed scheme is illustrated by a simulation example.

Diagnosis and actuator fault tolerant control in vehicle active suspension

This paper presents an application of a diagnosis and a fault tolerant control method for an active suspension system. This method detects and identifies an actuator fault and reconfigures the controller in order to maintain the ride comfort and vehicle performance in the presence of road disturbances. The proposed scheme consists of a diagnosis module and a controller reconfiguration module. The diagnosis module is based on the Unknown Input Observer approach to detect and identify actuator faults. A bank of observers is designed to generate residual signals such that each of them matches the system in a defined actuator fault mode. The controller reconfiguration module consists of a bank of controllers, each corresponding to a fault mode of the system. The module then changes the control law after the detection and the identification of an actuator fault. Simulation results show the effectiveness of the scheme applied to the active suspension system, which keeps its properties under the occurrence of severe faults.

Multistep Detector for Linear ISI-Channels Incorporating Degrees of Belief in Past Estimates

This paper formulates the channel equalization problem in the framework of constrained maximum-likelihood estimation. This allows us to highlight key issues including the need to summarize past data and to apply a finite alphabet constraint over a sliding optimization window. The approach adopted here leads to embellishments of the usual (nonadaptive) decision-feedback equalizer and its multistep extensions. It includes a provision for degrees of belief in past estimates, which addresses the problem of error propagation.

Multi-sensor switching strategy for automotive longitudinal control

In this paper we propose and explore a novel multi-sensor switching strategy for automotive longitudinal control. The proposed strategy selects, at each instant of time, the sensor (belonging to a collection of sensors) that provides the best closed loop performance, as measured by a control-performance criterion. It is assumed that each sensor has an associated feedback controller that has been designed such that the sensor-controller pair stabilises the closed loop system under normal operation conditions. Stability of the switching system under normal (fault-free) operation conditions is established in the main result of this paper. In addition, a simulation example illustrates that the proposed switching system is able to maintain performance levels close to the desirable ones, and to preserve stability, even under the occurrence of severe faults in some of the sensors