Fault-tolerant Model Predictive Control Research Articles

Fault Tolerant Model Predictive Control for the Biopower 5 CHP Plant

Fault Tolerant Model Predictive Control for the Biopower 5 CHP Plant

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.

Decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems

This paper presents a novel approach to address the decentralized fault tolerant model predictive control of discrete-time interconnected nonlinear systems. The overall system is composed of a number of discrete-time interconnected nonlinear subsystems at the presence of multiple faults occurring at unknown time-instants. In order to deal with the unknown interconnection effects and changes in model dynamics due to multiple faults, both passive and active fault tolerant control design are considered. In the Active fault tolerant case an online approximation algorithm is applied to estimate the unknown interconnection effects and changes in model dynamics due to multiple faults. Besides, the decentralized control strategy is implemented for each subsystem with the model predictive control algorithm subject to some constraints. It is showed that the proposed method guarantees input-to-state stability characterization for both local subsystems and the global system under some predetermined assumptions. The simulation results are exploited to illustrate the applicability of the proposed method.

Integrated nonlinear model predictive fault tolerant control and multiple model based fault detection and diagnosis

In this paper, a new fault-tolerant control approach is presented for a class of nonlinear systems, which preserves system stability despite a time delay in fault detection. The faults are assumed to occur in the actuators and are modeled for the general form of affine nonlinear systems. A fault detection and diagnosis (FDD) block is designed based on the multiple model method. The bank of extended Kalman filters (EKF) is used to detect predefined actuator faults and to estimate the unknown parameters of actuator position. The estimated parameters are then used to correct the model of the faulty system and to reconfigure the controller. The reconfigurable controller is designed based on the stabilizing nonlinear model predictive control (NMPC) scheme. On the other hand, in the duration between fault occurrence and fault detection, because of the mismatch between the process and the model, the system states may go off the attraction region. The proposed method is based on designing multiple local controllers for individual predefined faults. Depending on the value of a system variable at the moment of fault detection, one of these controllers will operate. This leads to a stability region of a set of auxiliary equilibrium points (AEPs), which is larger than the attraction region. Moreover, a framework for preserving system stability is presented. Finally, a practical chemical process example is presented to illustrate the effectiveness of this method.

Data-Based, Fault-Tolerant Model Predictive Control of a Complex Industrial Dearomatization Process

The main focus of this paper is on the development of an active data-based fault-tolerant model predictive controller (FTMPC) for an industrial dearomatization process. Three different fault-tolerant control (FTC) strategies are presented; these comprise data-based fault detection and diagnosis methods and fault accommodation- and controller reconfiguration-based FTC methods. These three strategies are tested with the simulated industrial dearomatization process. According to the validation and performance testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyzer, flow, and temperature measurements as well as the controller actuators. The reliability of the model predictive controller is increased and the profitability is enhanced owing to the lower off-spec production.

Reference Management for Fault-Tolerant Model Predictive Control

This work presents a reference management technique for fault-tolerant model predictive control. The reference value to be tracked is first filtered through a set of statically admissible commands. This set expresses the airframe's physical ability to achieve a new equilibrium point, and it is constructed by considering the limitations of the inputs and states of the system under control. The set has a polyhedral form defined by linear inequalities. State and control vectors are obtained at steady state to guarantee the feasibility of the constrained numerical problem faced by the model predictive control regulator. The proposed method has been evaluated using a linear model of a fighter aircraft, and it demonstrated adequate performance with a computational burden compatible with real-time applications.

Fault-Tolerant Model Predictive Control with Flight-Test Results

This work presents a novel method of fault-tolerant model predictive control where the response of a reference closed-loop model is followed even in the presence of an actuator fault. This architecture is capable of redistributing the control efforts among healthy actuators in a stable manner, respecting their limitations. Also, a constrained guidance system that works in conjunction with the inner-loop fault-tolerant controller is proposed. The guidance law considers the calculated limitations of the inner-loop control system as input constraints in order to smooth the transition between two consecutive navigation legs defined by waypoints. A trajectory-tracking system composed of the constrained guidance and the fault-tolerant model predictive controller is demonstrated through numerical simulations and experimental results on an experimental midsize transport aircraft, showing adequate performance.

Closed-loop Subspace Predictive Control for Fault Tolerant MPC Design

Subspace predictive control (SPC) is recently seen in the literature for joint system identification and control design. This combination enables automatically tuning the parameters in conventional model predictive control (MPC); and therefore provides a solution to the problem of fault tolerant MPC design. The existing SPCs either deal with open-loop data or depend on the information of the controller in a closed loop. In this paper we introduce a new closed-loop SPC method, which is independent of any controller information. Both the analytic solution to the unconstrained case and the quadratic programming problem for the constrained case are formulated. A recursive solution for updating the SPC control law is proposed. A fault tolerant MPC scheme is then developed based on the recursive algorithm, whose effectiveness is demonstrated on tolerating a fault in a steer-by-wire actuator.

FAULT TOLERANT MPC WITH AN EMBEDDED FDI SYSTEM

The fault detection and isolation (FDI) in industrial processes has been under an active study during the last decade, but fault tolerant control applications relying to the traditional FDI methods have been scarce. In this paper a fault tolerant model predictive controller (MPC) with an embedded FDI system is developed for controlling a simulated heavy oil fractionator process, the Shell Control Problem (SCP). Two different kinds of FDI systems are used for achieving the fault tolerance in co-operation with MPC: system based on Principal Component Analysis (PCA) and Partial Least Squares (PLS) and a system based on Subspace Model Identification (SMI). The effectiveness of selected methods was successfully tested by introducing bias and drift faults to simulated process measurements. Finally the results are presented and discussed.

FAULT TOLERANT HYBRID MPC APPLIED ON SEWER NETWORKS1

In this paper, fault tolerant hybrid model predictive control (HMPC) for sewer networks is introduced. A hybrid model of the sewer system is presented which allows capturing inherent phenomena such as tank and collector overflows that are not always present but depend on the system state. Constraints due to actuator faults can be expressed in the same modelling framework. The model is used for predictive control to reduce combined sewer overflows (CSO) and pollution even in the presence of actuator faults. A simulation study is presented where fault tolerant control strategies are compared and analyzed for real rain scenarios.

Integrating Model Based Fault Diagnosis with Model Predictive Control

Model predictive control (MPC) schemes are typically developed under the assumption that the sensors and actuators are free from faults. Attempts to develop fault-tolerant MPC schemes have mainly focused on dealing with hard faults, such as sensor or actuator failures, process leaks, etc. However, soft faults such as biases or drifts in sensors or actuators are more frequently encountered in the process industry. Occurrences of such faults can lead to degradation in the closed loop performance of the MPC controller. Since MPC controllers are typically used to control key operations in a chemical plant, this can have an impact on safety and productivity of the entire plant. The conventional approach to dealing with such soft faults in MPC formulations is through the introduction of additional artificial states to the model. The main limitation of this approach is that number of artificial extra states introduced cannot exceed the number of measurements. This implies that it is necessary to have a priori knowledge of which subset of faults are most likely to occur. In this paper, an active on-line fault-tolerant model predictive control (FTMPC) scheme is proposed by integrating state space formulation of MPC with the fault detection and identification (FDI) method based on generalized likelihood ratios. The fact that both these schemes use a Kalman filter as their basis facilitates tight integration of these two components. The main difference between the conventional MPC formulation and FTMPC formulation is that the bias corrections to the model are made as and when necessary and at qualified locations identified by the FDI component. The FTMPC eliminates offset between the true values and set points of controlled variables in the presence of a variety of faults while conventional MPC does not. Also, the true values of state variables, manipulated inputs, and measured variables are maintained within their imposed bounds in FTMPC, while in conventional MPC, these may be violated when soft faults occur. These advantages of the proposed scheme are demonstrated using simulation studies on a CSTR process and experimental studies conducted on the temperature control of a coupled two tank heater system.

Hot isostatic pressing furnaces

A robust fault-tolerant model predictive control (MPC) scheme for the precise control of temperature in hot isostatic pressing (HIP) furnaces was designed based on a nonlinear state-space representation of the dynamics of the temperature in an HIP furnace. The model was developed and validated using the data collected from open-loop experiments. The MPC-based controller was then implemented and tested on a six-zone HIP furnace at Bodycote IMT, Inc. The robust fault-tolerant MPC was demonstrated to outperform the existing PID-based control system in the following aspects: 1) achievement of a heating rate of at least twice that achieved by the existing PID-based system; and 2) automatic detection of loss of power to a furnace zone and controller reconfiguration to achieve the desired process objectives, which with the current PID system would be impossible and would require operator intervention.