Model-based Fault-tolerant Control Research Articles

Brain-Inspired Deep Meta-Reinforcement Learning for Active Coordinated Fault-Tolerant Load Frequency Control of Multi-Area Grids

This paper proposes an active coordinated fault tolerance load frequency control (AFCT-LFC) method, which effectively prevents sudden frequency changes caused by unit actuator failures or unplanned decommissioning in a multi-area interconnected grid subject to the performance-based frequency regulation market mechanism. It can also reduce regulation mileage payments and achieve multi-objective active fault-tolerant control. In addition, this paper proposes a brain-Inspired deep meta-deterministic policy gradient algorithm (BIMA-DMDPG), which adopts multi-agent centralized training, equates the controller of each area as an agent capable of independent decision making, and implements distributed training by dividing the environment into multiple environments. In addition, meta-reinforcement learning is employed to realize multi-task collaborative learning. The optimal policy is actively selected under different fault conditions to achieve active fault-tolerant control. The superior performance of the method is verified in a four-area LFC model of the China Southern Grid (CSG), in which it is tested alongside a selection of existing algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —AFCT-LFC is based on advanced artificial intelligence algorithm, which can effectively identify any fault in multi-area grids and make rapid response to achieve active fault-tolerant control. Compared with the existing model-based fault-tolerant control methods, the BIMA-DMDPG algorithm proposed in this paper does not need to rely on accurate mathematical models, and can be applied to practice through simple training, which is very suitable for practical applications. Therefore, AFCT-LFC is an advanced adaptive active fault-tolerant control method that can be truly applied in practice because of its fast-decision-making ability and performance.

Model-Based Fault Diagnosis and Fault Tolerant Control for Safety-Critical Chemical Reactors: A Case Study of an Exothermic Continuous Stirred-Tank Reactor.

In safety-critical chemical reactors with potential hazards, reaction kinetics and heat transfer parameters are usually known, and a mathematical model is available. It is then meaningful to base fault detection and isolation algorithms on the first-principles model as opposed to statistics, so that physically meaningful residual signals are generated from material and/or energy balances not closing, leading to reliable fault diagnosis. Additionally, to maintain the safety of the entire system, it is necessary to take appropriate control action based on the mathematical model and the identified faults, to minimize their impact and thus ensure safe operation. In the present work, these ideas will be formulated and illustrated through a continuous stirred-tank reactor case study involving the liquid-phase oxidation of alkylpyridine with hydrogen peroxide. The proposed fault tolerant control strategy monitors the DSM (distance of the system state from the boundary of the dynamic safe set) and the estimate of the fault size, and when they cross a certain limit as a result of an abnormal event, the manipulated input is switched. Simulation results show the effectiveness of the proposed fault tolerant control strategy in dealing with cooling system failure.

A Novel Data-Based Fault-Tolerant Control Method for Multicontroller Linear Systems via Distributed Policy Iteration

This article presents a novel data-based fault-tolerant control method for multicontroller linear systems via distributed policy iteration. The traditional fault-tolerant control methods based on policy iteration may cause a huge-computational burden under the situation of high-dimension control laws. In order to solve this problem, a novel distributed policy iteration method is presented, where only one iterative control law is updated in each iteration, to realize the fault-tolerant control of multicontroller linear systems. The main contributions can be highlighted as follows: 1) a novel data-based distributed policy iteration method is presented to reduce the computational burden; 2) the fault-tolerant control method is presented via designing fault compensators; and 3) the developed data-based method only requires the partial system information. First, the model-based fault-tolerant control via distributed policy iteration and fault compensation is provided. Based on the model-based method, a data-based fault-tolerant control method is presented. Finally, numerical experiments are given to show the performance of the presented method.

Adaptive second-order sliding model-based fault-tolerant control of a lower-limb exoskeleton subject to tracking the desired trajectories augmented by CPG algorithm

Adaptive second-order sliding model-based fault-tolerant control of a lower-limb exoskeleton subject to tracking the desired trajectories augmented by CPG algorithm

Off-policy reinforcement learning-based novel model-free minmax fault-tolerant tracking control for industrial processes

For industrial processes with external disturbance and actuator failure, off-policy reinforcement learning-based novel model-free minmax fault-tolerant control is proposed in this paper to solve H∞ fault-tolerant tracking control problem. An augmented model equivalent to the original system is constructed, and the state of the new augmented model is composed of state increment and tracking error of the original system. The original H∞ fault-tolerant tracking problem was transformed into the linear quadratic zero-sum game problem by establishing performance index function, and the Game Algebraic Riccati Equation (GARE) was established. Then Q function was introduced and the Off-policy reinforcement learning algorithm was designed. Different from the traditional model-based fault-tolerant control method, the proposed algorithm does not need the knowledge of system dynamics, and it can learn from the measured data of the system trajectory to solve the GARE. In addition, it is proved that the probing noise added to satisfy the persistent excitation condition does not cause bias. A simulation example of injection molding process is used to verify the effectiveness of the proposed algorithm.

Model-based sensor fault detection, isolation and tolerant control for a mine hoist

Encoder is essential for speed control and very valuable in condition monitoring for a mine hoist and the failure in sensor measurements can lead to serious accidents. In this paper, a novel encoder fault detection, isolation and tolerant control strategy based on finite time observers and constrained fault-tolerant controller is proposed for a mine hoist. The hybrid nonlinear observers which can converge to the origin in finite time are employed to detect and isolate faulty sensors, a residuals evaluation unit is then used to provide reconstructed signals. The constrained fault tolerant controller is presented to guarantee steady and safe running of the mine hoist when sensor fails. This approach is feasible and practical because it does not require a complicated update process, and the fault tolerance controller also makes the hoist run more reliably. Compared with traditional ways, the proposed method has superior performance and can be more effective, which are verified by experimental results.

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Model-based fault-tolerant control (FTC) often consists of two distinct steps: fault detection & isolation (FDI), and fault accommodation. In this work we investigate posing fault-tolerant control as a single Bayesian inference problem. Previous work showed that precision learning allows for stochastic FTC without an explicit fault detection step. While this leads to implicit fault recovery, information on sensor faults is not provided, which may be essential for triggering other impact-mitigation actions. In this paper, we introduce a precision-learning based Bayesian FTC approach and a novel beta residual for fault detection. Simulation results are presented, supporting the use of beta residual against competing approaches.

Model-based fault tolerant control and fault isolation through bipartite graph approach

Model-based fault tolerant control and fault isolation through bipartite graph approach

Model-based fault tolerant control and fault isolation through bipartite graph approach

Model-based fault tolerant control and fault isolation through bipartite graph approach

Dual-sliding mode approach for separated fault detection and tolerant control for functional safety of longitudinal autonomous driving

This paper describes model-based separated fault detection and fault tolerant control of longitudinal autonomous driving using dual-sliding mode observer for functional safety. Internal and environment sensors such as camera or radar are required to measure the acceleration information of the subject vehicle and the relative distance and velocity information between the preceding and subject vehicles in longitudinal autonomous driving. In order to detect the independent fault of each sensor, a dual-sliding mode observer (SMO) is used for fault reconstruction under the assumption that V2V (Vehicle to Vehicle) communication for vehicle driving state is available. The each SMO reconstructs the expected fault in sensor based on discontinuous injection term used for converging output error to zero. Based on the reconstructed fault by each SMO, faults are detected using threshold approach. When the fault is detected, the reconstructed fault is used for fault tolerant control by subtracting to faulty data. The proposed fault detection (FD) and fault tolerant control (FTC) algorithms were evaluated using actual driving data and a three-dimensional (3D) vehicle model with a linear quadratic regulator for following control. The evaluation results are presented and analyzed with regard to fault reconstruction, detection, and tolerant control in four cases wherein two types of faults were applied.

Enhanced Tire Blowout Modeling Using Vertical Load Redistribution and Self-Alignment Torque

Abstract Tire blowout impacts vehicle stability and creates challenges to vehicle control. To accurately evaluate the impact of tire blowout in different driving scenarios, a new tire blowout model is developed in this work with explicit considerations of the tire vertical load redistribution and self-alignment torque (SAT). A two-stage vertical force redistribution model is employed for the vertical load variations instead of the one-stage model in the literature. Moreover, the SAT is formulated, and the impact on the steering system is investigated. The developed tire blowout model is validated through both simulation and experiment using a scaled test vehicle. The results indicate that the proposed tire blowout model can evaluate the tire blowout impacts on vehicle dynamics more accurately, which will benefit the evaluation of tire blowout impact and the future development on model-based fault tolerant control.

Multiple model-based fault-tolerant control system for distributed drive electric vehicle

Generally, the distributed drive electric vehicle (DDEV) works under different fault status, and this may lead to unstable operating performance. However, the conventional fault-tolerant controller is only suitable for one special fault status, and thus, the stability of DDEV is hard to be guaranteed efficiently. To deal with this problem, a novel multiple model-based fault-tolerant control system (MMFTCS) is proposed in this paper. The MMFTCS integrates an operating status recognizer and a fault-tolerant controller set. The operating status recognizer is designed to distinguish current operating status, where the matching degree between current operating status and typical operating mode set is detected using fuzzy logic. The fault-tolerant controller set is designed to achieve optimal fault-tolerant control, and each fault-tolerant controller is designed for the corresponding typical operating mode by the model predictive controller. The output of the MMFTCS is computed by the weighted signal of each fault-tolerant controller to realize smooth switching. The simulation is carried on the MATLAB environment, and the results show that the MMFTCS has excellent fault-tolerant performance under various operating status.

A Model-based Active Fault Tolerant Control Scheme for a Remotely Operated Vehicle

An active failure and fault tolerant control scheme for an underwater remotely operated vehicle is proposed in this work. The proposed solution is based on a fault tolerant allocation, which takes into account an estimate of the faults on the vehicle’s thrusters for an accurate redistribution of the control effort. The diagnosis is based on a sliding mode observer, which generates three residuals and allows to evaluate the health condition of each actuator of the vehicle. The scheme can be adapted to any control law for the vehicle dynamics, and has been tested in simulation using a backstepping controller for the dynamics and with real world parameters, demonstrating the effectiveness of the proposed active fault tolerant control scheme.

Model-based fault-tolerant control with robustness to unanticipated faults

Fault-tolerant control is important for the autonomous operation of complex processes. When model predictive control is used, fault detection and diagnosis is often based on the available process model used by the controller. Unanticipated faults can however cause misdiagnosis of faults, and consequently incorrect compensation actions. A fault-tolerant model predictive controller is presented in this article and tested on a grinding mill circuit simulator. The fault diagnosis algorithm quickly and accurately detects anticipated faults based on the generalized likelihood ratio test. Unanticipated faults are isolated when the process data do not sufficiently match the most probable anticipated fault data. The scheme is applicable to nonlinear multiple-input multiple-output systems.

Model-based single fault disambiguation and fault tolerant control for a hydraulic drive using receding horizon

Model-based fault detection and isolation (FDI) needs an analytical model from which residuals can be obtained from a definite cause and effect relationships. The residuals are considered as the fault indicators, which are utilized for root cause analysis via fault signature and parameter estimation. After the disambiguation of fault, the job remains to take a decision in the supervision platform whether the fault can be accommodated (or tolerated) with the existing instrumentation. This applicability of fault tolerant control (FTC) depends upon actuator sizing through system inversion and control law design. In this work, a proven methodology for FDI using bond graph modeling has been applied to an oil hydraulic test bench for isolating a faulty component. The test bench was designed with a provision to inject a fault. After isolation of the faulty component, estimation of faulty parameters, inversion of systems, actuator sizing, and FTC have been addressed. In this paper, a novel fault tolerant control (FTC) using system inversion with due consideration of actuator constraints is reported. In addition, the receding horizon control as FTC has been corroborated through experimentation in a hydraulic drive, where two-step PI control is used to comply with actuator constraints.

Model-based diagnosis and fault tolerant control for ensuring torque functional safety of pedal-by-wire systems

This paper presents a model based approach for defining automotive functional safety requirements and provides a solution to ensure functional safety through model-based diagnosis and fault tolerant control. This model-based approach is consistent with ISO 26262 – functional safety standard. In particular, this paper presents the necessary steps for defining and implementing functional safety requirements, including item and function definition, Hazard Analysis and Risk Assessment, as well as the design of a model-based diagnostic and fault tolerant control (FTC) system that can lead to a systematic solution to automotive functional safety problems. The methodology proposed in this paper is applied to the problem of torque functional safety of pedal-by-wire systems.

A polytopic LPV approach to active fault tolerant control system design for three-phase induction motors

ABSTRACTThis paper presents a new active approach in order to design a model-based fault-tolerant control (FTC) system for three-phase induction motors (IMs) subjected to mechanical faults caused by stator and rotor failures. In this approach, a nominal controller achieves control objectives in fault-free case. The main part of this approach is related to design a fault/state observer for simultaneous estimation of additive faults which model the mechanical faults and axial fluxes in any operating conditions. For trade-off between robustness and performance in the presence of uncertainties and faults, this fault/state observer design problem is reduced to a well-known mixed optimisation problem by using polytopic linear parameter-varying approach. The FTC system consists of a nominal controller together with an observer which provides auxiliary inputs using the estimation of additive faults in order to compensate their undesirable effects on IM performance. The simulation results are shown to illustrate the effectiveness of the proposed approach to compensate the mechanical faults in IM.

Model-based Fault-Tolerant Control of Uncertain Particulate Processes: Integrating Fault Detection, Estimation and Accommodation

This work presents a methodology for the integrated identification, estimation and accommodation of control actuator faults in particulate processes with discretely-sampled measurements and plant-model mismatch. Initially, a stabilizing state feedback controller is designed on the basis of a reduced-order model of the infinite-dimensional system, and the closed-loop stability region is characterized in terms of the model uncertainty, the fault magnitude, the sampling period and the control design parameters. When state measurements are unavailable, the reduced-order inter-sample model predictor generates state estimates which are updated at each sampling time. A moving-horizon optimization problem is then formulated and solved for on-line actuator fault detection, isolation and estimation using past state and input data. The resulting estimates are used to locate the operating point with respect to the closed-loop stability region, which in turn is used to carry out the fault accommodation logic via updating the pot-fault control model and/or adjusting the controller design parameters. The developed methodology is illustrated using a non-isothermal continuous crystallizer example.

Fault-tolerant Control of a Master Generation Unit in an Islanded Microgrid

Two model-based fault-tolerant control design strategies are presented for a Diesel Engine Generator (DEG) working as a master generation unit in an islanded microgrid consisting of a hybrid wind-diesel-photovoltaic power system with a Battery Storage System (BSS). A Model Predictive Control (MPC) scheme and a Model Reference Adaptive Control (MRAC) scheme have been selected for precise and stable voltage and frequency regulation in the DEG. A Fault Detection and Diagnosis (FDD) module is added to the MPC structure, in order to reconfigure the control strategy when actuator faults in the DEG are present. MRAC is used in combination with a PID controller tuned by a Genetic Algorithm (GA). Improved performance over a baseline controller, IEEE type 1 Automatic Voltage Regulator (AVR), is achieved in a developed realistic simulation environment based on Matlab/Simulink.

Model-based Fault-tolerant Control to Guarantee the Performance of a Hybrid Wind-Diesel Power System in a Microgrid Configuration

This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its fault-tolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller.