Fault-tolerant Predictive Control Research Articles

Dynamic output feedback robust MPC hybrid fault-tolerant control for discrete uncertain systems: a switching control strategy

To guarantee the control performance of the discrete system under unmeasurable state and partial actuator failure, a robust dynamic output feedback predictive fault-tolerant control method derived from switching strategy is developed. A new iterative error switching model is established, which both ensures tracking performance and lays the foundation for the development of the next switching controller. Based on this model, output feedback switching fault-tolerant controller is developed. Compared with conventional fault-tolerant control methods, the designed controller can be used under the case of unmeasurable states, and it is more fault tolerant and less conservative. According to the Lyapunov stability theory, the robust stability analysis is carried out under normal and fault conditions, respectively. Then, depending on the findings of the stability analysis, sufficient conditions to guarantee the stability of the closed-loop normal and faulty system are derived. Finally, the suggested method's efficacy is confirmed using the injection moulding process as an example.

Cloud-based time-varying formation active fault-tolerant predictive control of networked multi-agent systems with actuator and sensor faults

This paper addresses the time-varying formation control problem of second-order networked multi-agent systems consisting of a virtual leader and multiple followers, where random communication constraints in the forward and feedback channels as well as actuator and sensor faults are considered. A state observer is designed to estimate the system state and the potential faults, based on which a time-varying formation active fault-tolerant predictive control scheme is proposed. Then, a necessary and sufficient condition is derived to guarantee the stability of the closed-loop system and to achieve the time-varying formation, which is independent of random communication constraints as well as actuator and sensor faults. Finally, simulation results of three contrastive cases are presented to verify the effectiveness of the proposed control scheme.

Robust predictive fault-tolerant control under actuator random failures

A robust predictive fault-tolerant switching control method is presented for a class of industrial processes that are prone to random actuator failures. The primary contribution is the combination of stochastic control theory with robust predictive control to represent the occurrence of system failure in the form of probability, thus significantly enhancing the conventional fault-tolerant control approach. First, a state-space model of the system is established, which is then transformed into an enhanced form that includes state deviations and output tracking errors. This augmented model enables the formulation of fault-tolerant switching control laws with fault probabilities, ensuring system convergence and tracking capabilities. Subsequently, linear matrix inequality constraints are given to find the switching controller gains under recovery and fault probabilities. Under certain probabilities, failure triggers fault-tolerant control, while recovery prompts a shift back to conventional control. Final, we validate the feasibility of our approach with a case study involving pressure regulation.

Predictive fault-tolerant control for nonlinear batch processes with small time delays via iterative learning control: A Lyapunov–Razumikhin approach

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov–Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov–Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

Constrained model predictive fault-tolerant control for nonlinear batch processes with time delay by integrating a LRF method and a switching strategy

To guarantee the control performance of batch processes under time delays and actuator failures, a novel iterative learning model-based predictive fault-tolerant control approach is developed. First, a two-dimensional switched model was developed by linearizing the system at multiple operating points so as to describe the actual process with and without actuator faults. Second, a switching controller with greater robustness and diminished conservativeness was developed based on this switched model. Compared to conventional fault-tolerant control methods, the switching controller has stronger fault-tolerant ability, less conservativeness, and higher energy efficiency. Third, by integrating the Lyapunov-Razumikhin function (LRF) method, sufficient conditions for system stability could be provided based on the robust positive invariant and terminal constraint sets. Solving the stability conditions could then allow us to obtain the control law gains under faulty or normal cases. Compared to the existing Lyapunov-Krasovskii function method (LKF) method, the developed stability conditions were simpler, less complex, and less conservative. Finally, the effectiveness of the proposed method is tested on a continuous stirred reactor (CSTR).

Robust predictive fault-tolerant switching control for discrete linear systems with actuator random failures

Under the condition that state variables are unavailable, a robust predictive fault-tolerant switching control method is proposed for discrete linear systems with actuator random failures. The randomness of failures is described in terms of conditional probabilities. Then, a transfer probability matrix is established to predict the probability of state change. A model incorporating the system state deviation and tracking error is further developed to enhance the regulation of the controller. By using related theories, sufficient conditions for the stability of the system are obtained. Furthermore, average dwell times for the system are obtained according to exponential stability analysis. These times and the matrix form conditions for the output feedback fault-tolerant switching controller to switch between conventional control and fault-tolerant control. Based on the previous work, the research on disturbances and set-point changes has been extended. The feasibility of the method is verified by a case study of the pressure-holding phase.

Harmonic closed-loop model predictive fault-tolerant control of a dual three-phase permanent magnet synchronous motor

Harmonic closed-loop model predictive fault-tolerant control of a dual three-phase permanent magnet synchronous motor

Stochastic robust predictive fault-tolerant control for discrete systems with actuator faults

Abstract A stochastic robust predictive fault-tolerant control approach is proposed for a class of industrial processes with uncertainties, external disturbances and the random failure of actuators. First, a process model of the above system is established in terms of state space. It is further converted into a new augmented form containing the state deviation and output tracking error. By means of the augmented model, the designed control law is given. Then the related theorem and corollary in the form of linear matrix inequality constraints are provided for solving the gain of the control law under different probabilities of failure. Under a certain probability, the occurrence of a failure is regarded as a switching condition. Conventional control is adopted when the failure does not occur, while fault-tolerant control is performed when the failure occurs. Finally, a pressure holding phase case is provided for verifying the feasibility of the given approach.

Iterative learning hybrid robust predictive fault-tolerant control for nonlinear batch processes with partial actuator faults

For nonlinear batch processes with uncertainties, disturbances and partial actuator faults, an iterative learning robust predictive fault-tolerant control approach is developed. Based on the conclusion of robustness analysis, the stability conditions combining the robust positive invariant set with the terminal constraint set can guarantee that the system is stable under faults. Furthermore, the real-time optimal gains of control law solved online can significantly reduce the learning cycles of the controller, which in turn can meet the demand for rapid production of batch processes. The injection process is employed as an example to ultimately confirm the viability and efficacy of the developed approach.

Fault-Tolerant Predictive Control for Five-Leg Dual-Mover Permanent-Magnet Motor Drives

In this paper, a fault-tolerant predictive control (FTPC) is proposed for the post-fault operation of five-leg dual-mover primary permanent-magnet linear motor (PPMLM) drives with open circuit fault (OCF). In the post-fault operation, one phase winding and one voltage-source-inverter (VSI) leg are lost. As a result, there are four VSI legs and five healthy phase windings from two movers. Especially, one VSI leg is shared by two movers. The objects of FTPC are to reduce the computation burden and improve the performances. Firstly, A minimum copper loss principle is designed to distribute the reference current for two movers. Secondly, two global optimal mover voltage vectors (MVVs) for two movers are respectively determined by geometrical location. Thirdly, the two global optimal MVVs are respectively modified as two local optimal MVVs, in which the influence of the common leg is considered. Finally, two synthesized voltage vectors are obtained to improve the steady-state performances. Meanwhile, 60% of the computation burden can be reduced by using FTPC. The effectiveness of the proposed FTPC have been verified by experimental results.

Adaptive diagnosis and non-fragile predictive control for HFV engine with non-Gaussian uncertain output

This study presents an adaptive fault estimation and model predictive fault-tolerant control (FTC) for the hypersonic vehicle engine with non-Gaussian uncertain output jet plume. The non-Gaussian probability density function (PDF) describing the plume velocity in a stochastic system is approximated by Type II fuzzy radial basis functions. In the fault observer, the novel prey adaptive estimation method is designed for the valve faults with different amplitudes. Ultimately, this fuzzy-prey fusion adaptive observer shields perturbation and accurately estimates complex faults including incipient and intermittent faults. A predictive tolerant controller compensates for all the faults effects and non-fragile compensation factors of FTC eliminate the perturbation interference, thence the output PDF matches with the expected value. Finally, the more refined internal structure of plume output distribution replaces the local and rough valve position variables, helping this study realizes the hypersonic vehicle engine integrated refine control. Simulation result verifies the effectiveness of the proposed approaches and the superiority compared with the existing method.

Fault-Tolerant Tracking Control Optimization of Constrained LPV Systems Based on Embedded Preview Regulation and Reference Governance

This article presents some new improvements to the relevant constrained predictive fault-tolerant tracking control (FTTC) methods through embedding optimal preview regulation and reference governance. The main novelty of such a strategy is that some valuable information of finite future references can be adequately scheduled to optimize the robust tracking performance and significantly enlarge the fault-tolerant admissible region. In order to better describe the wide applicability of the proposed strategy, the robust FTTC problem for a class of LPV systems with state/input constraints is considered. Overall, the involved key designs consist of three parts. First, an unconstrained FTTC component is constructed by combining tracking error feedback, reference input regulation, and fault signal compensation. It is used to guarantee the robust tracking stability of closed-loop systems when the constraints are not activated. Second, a tube-based predictive FTTC policy with an embedded optimal preview regulator is designed to achieve the robust constraint satisfaction and transient response improvement. Third, an embedded reference governor is additionally integrated to significantly enlarge the size of the fault-tolerant admissible region. This design further reinforces the feasibility of constrained FTTC optimization. The effectiveness of these results is finally validated by a case study of a single transistor dc/dc Forward converter.

A continuous-time fault-tolerant predictive control approach for wind turbines

A continuous-time fault-tolerant predictive control approach for wind turbines

A continuous-time fault-tolerant predictive control approach for wind turbines

A continuous-time fault-tolerant predictive control approach for wind turbines

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.

Intelligent Discrete Sliding Mode Predictive Fault-Tolerant Control Method for Multi-Delay Quad-Rotor UAV System Based on DIECOA

This paper introduces a novel intelligent sliding mode predictive fault-tolerant control method based on the Dynamic Information Exchange Coyote Optimization Algorithm (DIECOA), which is applied to a quad-rotor UAV system with multi-delay and sensor fault. First, the system nonlinearity and sensor fault are dealt with by means of interpolation transformation and system state expansion, and an equivalent system is obtained. Second, the quasi-integral sliding mode surface is used to construct the prediction model so that the initial state of the system is located on the sliding mode surface, and the global robustness is guaranteed. Third, this paper introduces an improved fault and disturbance compensation term, which effectively weakens the adverse effect of time delays and enhances the FTC performance of the system. Fourth, the Dynamic Information Exchange (DIE) strategy is designed to further improve the coyote individual replacement mechanism and speeds up the solution and convergence speed of the method in this paper. Finally, the simulation is carried out on the fault-tolerant simulation platform of the quad-rotor Unmanned Aerial Vehicle (UAV), and the results show the rationality of the method.

Networked Active Fault-Tolerant Predictive Control for Systems With Random Communication Constraints and Actuator/Sensor Faults

This brief presents an active fault-tolerant predictive control method for the output tracking of networked control systems (NCSs) subject to random communication constraints as well as actuator and sensor faults. The system state and actuator/sensor faults are simultaneously estimated via an observer and then used to perform a fault-tolerant predictive controller. Thus, both faults and the communication constraints in the backward and forward channels are actively compensated. A closed-loop stability condition is obtained, which also guarantees a zero steady-state tracking error. Moreover, the separation principle for designing the observer-based controller holds. Finally, the numerical simulation for a mass-spring-damper system is conducted to illustrate the effectiveness of the proposed method.

Observer-Based Fault-Tolerant Predictive Control for LPV Systems with Sensor Faults: An Active Car Suspension Application

In this paper, an observer-based robust fault-tolerant predictive control (ORFTPC) strategy is proposed for Linear Parameter-Varying (LPV) systems subject to input constraints and sensor failures. The main objective of this work is to establish a real observer based on a virtual observer to be used to estimate both states and sensor failures of the system. The proposed virtual observer is employed to improve the observation precision and reduce the impacts of the sensor faults and the external disturbances in the LPV systems. In addition, a real observer is proposed to overcome the virtual observer margins and to ensure that all states and sensor faults of the system are properly estimated, without the need for any fault isolation modules. The proposed solution demonstrates that, using both observers, a robust fault-tolerant predictive control is established via the Lyapunov function. Moreover, sufficient stability conditions are derived using the Lyapunov approach for the convergence of the proposed robust controller. Furthermore, the proposed approach simultaneously computes the gains of the real observer and the controller from a linear matrix inequality (LMI), which is deduced from the estimation errors. Finally, the performance of the proposed approach is investigated by a simulation example of a quarter-vehicle model, and the simulation results under a sensor fault illustrate the robustness and performance of the proposed method.

Explicit Model Predictive Based Fault Tolerant Control for Turboshaft Engine System

In order to tackle the faults of turboshaft engine-rotor systems and the real time implementation difficulty of traditional model predictive control methods, an explicit model predictive (EMPC) fault-tolerant control algorithm is designed based on an active fault-tolerant control scheme that implicitly detects the faults and adjusts the control law online. The proposed real time control algorithm can achieve good control command tracking performance and, at the same time, guarantee limit protection of turboshaft engine-rotor system. Firstly, to start with the algorithm, a dynamic system model library and a fault monitoring mechanism are established, in which the dynamic system model library contains sets of piecewise affine models (PWA) of normal engine mode and modes with faults. Secondly, the EMPC fault-tolerant controller is derived and designed for each sub-model in the modes of dynamic model library. Through the transformation and derivation of the traditional model predictive controller of turboshaft engine-rotor system, the explicit solution of its fault-tolerant controller is obtained. With the explicit solution which is in the form of state feedback control for each partition, controller gains can be designed off-line. Finally, the online engine control process is completed by searching the corresponding controller gain based on engine state, which improves the real-time performance of the control system. The effectiveness of the method for engine systems with modeled or un-modeled compressor faults and the robustness of the algorithm are verified by simulations.

Current model predictive fault-tolerant control for grid-connected photovoltaic system

<abstract><p>This paper investigates the performance of the current model predictive control (CMPC) for controlling a two-stage transformerless grid-connected photovoltaic (PV) system under grid fault conditions. A maximum power point tracking (MPPT) controller was used to extract the maximum power of the PV panel. To stabilize the DC link and generate the reference current values, a proportional-integral (PI) controller was used. The CMPC strategy was implemented to control the output current of the inverter that connects the PV system to the utility grid. The system and control strategy were simulated via a MATLAB/Simulink environment. The performance of the proposed control strategy was investigated under fault conditions between the three-phase two-level inverter and the grid. Moreover, to validate the capability of the CMPC, comparative case studies were conducted between CMPC, PI, and sliding mode control (SMC) under grid fault. Case studies' results showed that under grid fault, CMPC did not introduce any overshoot or undershoot in the PV output DC current and power. However, PI and SMC produced undershoots of almost 15 kW for the output power and 45 A for the output current. Under the fault conditions, the active output power and three-phase current recovery time of the inverter was 50 ms using CMPC, compared to PI and SMC with recovery times of 80 ms and 60 ms, respectively. Moreover, a voltage dip of 75 V at the DC link voltage was recorded with CMPC under faulty conditions, while the voltage dips for PI and SMC were around 180 V.</p></abstract>