Fault-tolerant Control Framework Research Articles

Data-based deep reinforcement learning and active FTC for unmanned surface vehicles

In this study, we investigate the active fault tolerant control(AFTC) of unmanned surface vehicles (USV) under actuator faults and environmental disturbances using deep reinforcement learning(DRL). We realize the perception of the environment and continuous control through end-to-end learning based on neural networks(NN). The contents of AFTC include 1) actuator fault estimation(FE) based on long short-term memory(LSTM) and 2) a fault tolerant control(FTC) framework based on a deep deterministic policy gradient to enable the USV to learn autonomous decision making through training. We introduce mixed noise based on Gaussian noise and Ornstein–Uhlenbeck noise into the training, which increases the exploration space and improves the generalization ability of the USV. An experiential replay method based on quantum computing is designed to distinguish the importance of samples through preparation and depreciation operations, enabling a guided learning process. Finally, we verify the universality and effectiveness of the AFTC framework by tracking straight and sinusoidal trajectories.

Semi-Global Bipartite Fault-Tolerant Containment Control for Heterogeneous Multiagent Systems With Antagonistic Communication Networks and Input Saturation.

Semi-global bipartite fault-tolerant containment control framework on antagonistic communication networks is proposed in this article for heterogeneous multiagent systems (MASs) under the influence of input saturation and actuator faults. An observer is constructed to estimate the leaders' states on signed digraph, where the communication networks are antagonistic. A fully distributed virtual control approach is developed to acquire the containment trajectory. Based on the observer, a semi-global containment control method is developed to compensate for the detrimental impacts of both input saturation and actuator faults. Besides, the dynamics and state-space dimensions of the agents can be different. The proposed framework overcomes two drawbacks of the conventional containment control: 1) the containment trajectory is obtained under general antagonistic communication networks, which is more general in engineering applications and 2) both actuator faults and input saturation are solved for heterogeneous agents, which relaxes the limitation of homogeneous dynamics. Finally, a simulation example is conducted to test and verify the feasibility of the proposed method framework.

A Novel, Finite-Time, Active Fault-Tolerant Control Framework for Autonomous Surface Vehicle with Guaranteed Performance

In this paper, a finite-time, active fault-tolerant control (AFTC) scheme is proposed for a class of autonomous surface vehicles (ASVs) with component faults. The designed AFTC framework is based on an integrated design of fault detection (FD), fault estimation (FE), and controller reconfiguration. First, a nominal controller based on the Barrier Lyapunov function is presented, which guarantees that the tracking error converges to the predefined performance constraints within a settling time. Then, a performance-based monitoring function with low complexity is designed to supervise the tracking behaviors and detect the fault. Different from existing results where the fault is bounded by a known scalar, the FE in this study is implemented by a finite-time estimator without requiring any prioir information of fault. Furthermore, under the proposed finite-time AFTC scheme, both the transient and steady-state performance of the ASV can be guaranteed regardless of the occurrence of faults. Finally, a simulation example on CyberShip II is given to confirm the effectiveness of the proposed AFTC method.

Reconfigurable tolerant control of nonlinear Euler–Lagrange systems under actuator fault: A reinforcement learning-based fixed-time approach

This paper presents a novel fixed-time adaptive Fault Tolerant Control (FTC) framework for MIMO nonlinear Euler-Lagrange systems using sliding mode-based strategy, reinforcement learning (RL) and fixed-time disturbance observer. The primary objective is to enhance system reliability in the presence of actuator faults, uncertainties and disturbances. The proposed RL algorithm incorporates an actor-critic neural network (NN), where the actor NN estimates the uncertainty and the critic NN evaluates the performance cost function. Additionally, a fixed-time adaptive observer is designed to estimate the lumped term of faults and disturbances. To achieve high precision trajectory tracking within a fixed-time interval, a nonsingular fast terminal sliding mode scheme is designed. This scheme ensures fixed-time convergence of the tracking error and facilitates disturbance attenuation and fault mitigation, which are key features of the proposed fixed-time secure control strategy. Furthermore, the closed-loop system's fixed-time stability is analyzed using Lyapunov theory. Experimental results demonstrate the effectiveness of the proposed FTC framework in mitigating the adverse effects of faults, uncertainties and disturbances, thereby enhancing system performance and reliability.

Robust Adaptive Fuzzy Fault Tolerant Control of Robot Manipulators With Unknown Parameters

This paper resolves the safety tracking problem of the robot manipulator with the actuator faults, process faults, uncertain dynamics, and external disturbances. A novel robust adaptive fuzzy fault tolerant control (FTC) framework is presented. In this approach, a constraint mechanism of the zero overshoot error is proposed to keep the filter variable within an adjustable range, and the compact set can be available. An adaptive fuzzy logic system (FLS) is, then, employed to approximate the time-varying, nonlinear, and unknown robot dynamics. Furthermore, a robust adaptive term is designed to compensate the actuator faults and approximation errors, and ensure the convergence and stability of the whole robot control system. The uniform ultimate boundedness of the closed-loop dynamics for the robot manipulator is proved by the Lyapunov function. Afterwards, the simulation results demonstrate that the proposed robust adaptive fuzzy FTC (RAFFTC) approach of the robot manipulator has good control performance. Finally, the effectiveness of the proposed RAFFTC approach is verified by experiments.

Cable failure tolerant control and planning in a planar reconfigurable cable driven parallel robot.

The addition of geometric reconfigurability in a cable driven parallel robot (CDPR) introduces kinematic redundancies which can be exploited for manipulating structural and mechanical properties of the robot through redundancy resolution. In the event of a cable failure, a reconfigurable CDPR (rCDPR) can also realign its geometric arrangement to overcome the effects of cable failure and recover the original expected trajectory and complete the trajectory tracking task. In this paper we discuss a fault tolerant control (FTC) framework that relies on an Interactive Multiple Model (IMM) adaptive estimation filter for simultaneous fault detection and diagnosis (FDD) and task recovery. The redundancy resolution scheme for the kinematically redundant CDPR takes into account singularity avoidance, manipulability and wrench quality maximization during trajectory tracking. We further introduce a trajectory tracking methodology that enables the automatic task recovery algorithm to consistently return to the point of failure. This is particularly useful for applications where the planned trajectory is of greater importance than the goal positions, such as painting, welding or 3D printing applications. The proposed control framework is validated in simulation on a planar rCDPR with elastic cables and parameter uncertainties to introduce modeled and unmodeled dynamics in the system as it tracks a complete trajectory despite the occurrence of multiple cable failures. As cables fail one by one, the robot topology changes from an over-constrained to a fully constrained and then an under-constrained CDPR. The framework is applied with a constant-velocity kinematic feedforward controller which has the advantage of generating steady-state inputs despite dynamic oscillations during cable failures, as well as a Linear Quadratic Regulator (LQR) feedback controller to locally dampen these oscillations.

A Blockchain-Based Data-Driven Fault-Tolerant Control System for Smart Factories in Industry 4.0

Modern technologies and data-driven approaches have enabled fault-tolerant controllers in Industry 4.0 smart factories to detect, identify, and mitigate anomalies in real-time with a high level of accuracy. However, this has also presented new challenges and requirements for cybersecurity, data analytics, and computational complexity for Data-Driven Fault-Tolerant Controllers (DD-FTC) in smart factories. To address these issues, a Blockchain-Based Data-Driven Fault-Tolerant Control (BB-DD-FTC) framework for smart factories is proposed in this paper. Blockchain ensures the integrity of data logs via its immutable ledger and decentralized architecture. Moreover, the blockchain smart contract functionality, embedded with a Data-Driven Intrusion Detection System (DD-IDS), and reconfiguration conditions, realizes DD-FTC and undertakes the mitigation response in case of cyber-attacks. The DD-IDS mechanism utilizes the principal component analysis technique and observer models, trained via neural networks, to detect an attack and identify the compromised component. The Tennessee Eastman (TE) industrial benchmark process is considered a case study to investigate the performance of the proposed framework. Two kinds of integrity attacks are applied to the sensors of the TE process with simulation results demonstrating the effectiveness of the method in mitigating the adversarial effect of the applied attacks on the overall system performance. As feedback delays can negatively impact performance, a detailed delay analysis is performed using network calculus. The security advantages and limitations of the proposed method are finally highlighted in the performed security analysis. The results are encouraging for the wider adoption of the control-over-the-blockchain concept.

A Fault-Tolerant and Reconfigurable Control Framework: Modeling, Design, and Synthesis

Manufacturing systems (MS) have become increasingly complex due to constraints induced by a changing environment, such as flexibility, availability, competition, and key performance indicators. This change has led to a need for flexible systems capable of adapting to production changes while meeting productivity and quality criteria and reducing the risk of failures. This paper provides a methodology for designing reconfigurable and fault-tolerant control for implementation in a Programmable Logic Controller (PLC). The main contribution of this methodology is based on a safe control synthesis founded on timed properties. If a sensor fault is detected, the controller switches from normal behavior to a degraded one, where timed information replaces the information lost from the faulty sensor. Switching between normal and degraded behaviors is ensured through reconfiguration rules. The primary objective of this method is to implement the obtained control into a PLC. In order to achieve this goal, a method is proposed to translate the controllers of the two behaving modes and the reconfiguration rules into different Grafcets. This approach relies on the modular architecture of manufacturing systems to avoid the combinatorial explosion that occurs in several approaches.

Continuous Fixed-Time Fault-Tolerant Formation Control for Heterogeneous Multiagent Systems Under Fixed and Switching Topologies

This article proposes a novel continuous fixed-time fault-tolerant formation control framework for heterogeneous multiagent systems (HMASs) involving unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and model uncertainties under fixed and switching topologies. Firstly, to estimate the information of actuator faults and uncertainties for each follower, a fixed-time observer is constructed. Then, an integral fixed-time sliding mode surface is designed such that the merits of high robustness, exact transient response and fixed-time convergence can be achieved. Subsequently, based on the integration of disturbance observer, high-order sliding mode and backstepping technologies, distributed and decentralized continuous fixed-time formation tracking control schemes under actuator faults are developed of the HMASs in the X-Y axis and Z axis, respectively. The proposed algorithm can not only handle time-varying formation in a timely manner, but also provide continuous control action under fixed and switching topologies. It is theoretically proved that the designed protocol can realize the expected fixed-time formation missions by employing the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the designed fixed-time control algorithm.

Neural network-based sensor fault estimation and active fault-tolerant control for uncertain nonlinear systems

In this work, we developed a novel active fault-tolerant control (FTC) design scheme for a class of nonlinear dynamic systems subjected simultaneously to modelling imperfections, parametric uncertainties and sensor faults. Modelling imperfections and parametric uncertainties are dealt with using an adaptive radial basis function neural network (RBFNN) that estimates the uncertain part of the system dynamics. For sensor fault estimation (FE), a nonlinear observer based on the estimated dynamics is designed. A scheme to estimate sensor faults in real-time using the nonlinear observer and an additional RBFNN is developed. The convergence properties of the RBFNN, used in the fault FE part, are improved by using a sliding surface function. For FTC design, a sliding surface is designed that incorporates the real-time sensor FE. The resulting sliding mode control (SMC) technique-based FTC law uses the estimated dynamics and real-time sensor FE. A double power-reaching law is adopted to design the switching part of the control law to improve the convergence and mitigate the chattering associated with the SMC. The FTC works well in the presence and absence of sensor faults without the requirement for controller reconfiguration. The stability of the proposed active FTC law is proved using the Lyapunov method. The developed scheme is implemented on a nonlinear simulation of an unmanned aerial vehicle (UAV). The results show good performance of the proposed unified FE and the FTC framework.

A cascaded nonlinear fault-tolerant control for fixed-wing aircraft with wing asymmetric damage

This paper investigates the flight dynamics of the aircraft with wing asymmetric damage and the fault-tolerant control problem to improve the stability and flight quality of damaged aircraft. A high-fidelity wing asymmetric damaged aircraft nonlinear model is developed, as well as the impact of wing asymmetric damage on the physical and aerodynamic properties of the aircraft is also analyzed. The trim strategies for damaged aircraft are investigated to achieve a rapid estimation of trim states after damage occurs. This paper presents a robust cascaded nonlinear fault-tolerant control framework that integrates the incremental nonlinear dynamic inversion control with improved piecewise-constant-based nonlinear L1 adaptive control for the stability control to enhance the stability and tracking performance of the damaged aircraft. Theoretical analysis proves that the presented fault-control structure is robust to disturbances and can decouple rapidity and robustness while guaranteeing steady-state and transient performance. Finally, the hardware-in-the-loop flight control experiment platform is developed to validate the cascaded nonlinear fault-tolerant controller. In the experiment, the proposed controller is verified under wing asymmetric damage and compared with existing methods. Experimental results show that the proposed fault-tolerant control is able to overcome wing asymmetric damage and significantly improve the tracking performance of the damaged aircraft even with 27.2% of the severe damage to the left-wing.

Cooperative Fault-Tolerant Control for a Mobile Dual Flexible Manipulator With Output Constraints

This article discusses cooperative control of a mobile dual flexible manipulator system with asymmetric time-varying output constraints and actuator failures. The shift function and a barrier Lyapunov function (BLF) are used to guarantee output constraints when the initial states of the system violate the prescribed constraints. Moreover, an adaptive fault-tolerant control scheme is developed to deal with actuator failures, while suppressing system’s vibration and achieving cooperative operation. Finally, theoretic analysis proves the uniform bounded stability of the system and numerical simulation verifies the effectiveness of the proposed control method. Note to Practitioners—The purpose of this article is to develop a cooperative dual flexible manipulator system with performance limitations and actuator failures. The system can realize the task of stably grasping and moving a rigid object. The existing research on the grasping task of flexible manipulators only focuses on coordinated operation, which limits the application of the dual flexible manipulator system in practical engineering. To further study the problem, this article considers the output constraints and actuator failures of the dual flexible manipulator system in the actual process and proposes a cooperative fault-tolerant control framework. The control framework uses shift function and BLF to deal with output constraints and adopts adaptive technology to deal with actuator failures. In addition, the cooperative operation task of grasping object with dual flexible manipulators is realized. Simulation shows that this control strategy is feasible.

Takagi–Sugeno Fuzzy Realization of Stability Performance-Based Fault-Tolerant Control for Nonlinear Systems

This article is dedicated to studying realization issues of the stability performance-based nonlinear fault-tolerant control framework via Takagi–Sugeno (T–S)fuzzy models. To this end, the nonlinear fault-tolerant control strategy with an online fault detection system monitoring the system stability performance degradation induced by faults is first introduced by means of the stable image and kernel representations. On this basis, the T–S fuzzy models are applied to approximate the nonlinear system, and a design approach of the fuzzy observer-based controller is proposed for the system stabilization via the iterative linear matrix inequality method. With the controller gains, the fuzzy-model-based nominal stable image representation of the system is formulated, which leads to the generation of the input and output error signals. Then, with the reference signal and system input and output error signal data, a data-driven algorithm is given to online estimate the evaluation function defined in terms of system uncertainties and faults. By virtue of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2}$</tex-math></inline-formula> input–output stability of the controller stable kernel representation, a threshold calculation method is presented and, thus, the stability performance-based fault detection system based on fuzzy models is realized. Furthermore, for fault-tolerant purpose, the fault-tolerant controller design is discussed, which aims to retain the system stability. Two examples are provided in the end to illustrate the proposed results.

Framework for fault-tolerant speed tracking control of gasoline engines using the first principle-based engine model

ABSTRACT Optimal engine torque management, a fundamental objective, depends predominantly on engine speed tracking performance. It ensures to attain desired speed profile in the presence of uncertainties, disturbances and malfunctions. On the other hand, certain requirements such as emissions control, fuel efficiency and drivability are degraded in case of poor speed tracking. Furthermore, constraints on engine speed tracking performance are even more stringent for hybrid power-train architecture as crankshaft speed and engine torque are the basic variables for coordinated control. Speed tracking is also considered essential for gear shift control of the automatic transmission. In this research work, a framework for fault-tolerant speed tracking of the gasoline engine is proposed using the First Principle-based Engine Model (FPEM). A high-fidelity direct relationship between fuel injection input and engine speed is derived by the transformation of FPEM. Fault is induced in the fuel injection subsystem to generate the torque imbalance. Using the proposed framework, a second-order sliding mode-based control technique is applied to track desired speed profile by mitigating the faults in the fuel injection subsystem. Reference data acquired from the engine test rig is used to demonstrate the offline validity and fault tolerance capabilities of the proposed framework in MATLAB/Simulink.

A Distributed Event-Triggered Fixed-Time Fault-Tolerant Secondary Control Framework of Islanded AC Microgrid Against Faults and Communication Constraints

In AC microgrids (MGs), the required high control gain for dealing with control faults would reduce the robustness of the secondary control to communication constraints. To tackle with this problem, the subarea physical infrastructure of AC MGs and the two-layer secondary control framework are designed. For the physical infrastructure, the whole AC MG is divided into several areas, where each area is composed of a distributed generator (DG) and several geographical-close loads. Furthermore, by utilizing the advanced metering infrastructure (AMI), loads transmit the local load change information to the DG in the same area. Based on this physical infrastructure, a distributed event-triggered fixed-time fault-tolerant (ET-FTFT) secondary control framework is proposed in this paper to improve the resilience of the secondary control to sensor and actuator faults/attacks, and communication constraints, simultaneously. The adopted two-layer control framework is to decouple the communication constraints and control faults. The event-based strategy is adopted in the upper layer to reduce the communication burden and Zeno behavior can be evaded. Real-time simulations based on the NI-PXI real-time simulator validate the advantages of the distributed ET-FTFT secondary control framework, which are the better robustness to control faults, and the fixed-time convergence to improve the power quality.

Fault-Tolerant Cooperative Control for Multiple Vehicle Systems Based on Topology Reconfiguration.

In this article, the fault-tolerant synchronization and time-varying tracking control problem is investigated for nonlinear multivehicle systems (MVSs) in the presence of partial loss-of-control-effectiveness (LoCE) faults. Based on the graph theory, a two-level fault-tolerant cooperative control framework is proposed, namely, the low-level distributed nominal control scheme and the high-level topology reconfiguration protocols. The low-level scheme is developed to guarantee system performances in the fault-free scenario. With the low-level scheme, the high-level topology reconfiguration protocols, each of which corresponds to one partial LoCE fault scenario, are then proposed to mitigate the fault impact by adjusting the underlying topology. Accordingly, without modifying the structure or the design parameter of the low-level control scheme, the proposed framework can guarantee the synchronization and tracking errors of the MVS asymptotically convergent to zero in both fault-free and fault scenarios. Finally, the effectiveness of the proposed control method is verified via a simulation study of three degree-of-freedom helicopters.

Uncertainty estimator-based dual layer adaptive fault-tolerant control for wind turbines

Under the detrimental effects of sensor and actuator faults, the blade pitch system is found to be the least reliable subsystem. Therefore, to apprehend required level of reliability and efficiency, an efficient Fault Tolerant Control framework (FTC) seems crucial. This paper presents an Active FTC (AFTC) strategy to control the pitch angle of a wind turbine in the presence of actuator and sensor faults, uncertainties and exogenous disturbances. First, a lumped term consisting of model uncertainty and disturbance is estimated by a novel dual layer adaptive uncertainty estimator. Next, to achieve high accuracy in supplying the required power, a continuous adaptive time delay control is designed based on the estimated uncertainties. In the proposed controller, chattering caused by discontinuous control term is eliminated, and faults are accommodated. Stability of the closed-loop system is demonstrated by the Lyapunov theory. Furthermore, in order to verify the validity of the proposed strategy, the controller is implemented in FAST-MATLAB/Simulink for five different load cases generated using TurbSim. Results confirm the effectiveness and superiority of the proposed structure in the presence of sensor and actuator faults (bias, gain, performance degradation and actuator stuck) compared to Nonlinear PI (N-PI) control and Feedback Linearized Control (FLC) schemes.

Active Fault-Tolerant Control Framework for Linear Parameter-Varying Systems Affected by Sensor Faults

In this paper, we propose a novel fault tolerant multi-sensor switching control framework for linear parameter-varying systems considering multiplicative and additive sensor faults simultaneously. We show that the closed-loop stability is preserved under sensor fault situation if established guaranteed fault detection conditions based on invariant sets are fulfilled. At each time instant, the switching control strategy selects the sensor group that provides the optimal closed-loop performance, which is characterized by a quadratic performance objective function. Furthermore, faults on each channel of all group of sensors can be also pre-guaranteed to be isolated under the fulfillment of established fault isolation conditions, which ensures a more precise determination of the location of fault occurrence. At the end, a practical vehicle model is provided to illustrate the effectiveness of the proposed method.

Quadcopter Trajectory Tracking in the Presence of 4 Faulty Actuators: A Nonlinear MHE and MPC Approach

This letter proposes an Active Fault Tolerant Control (AFTC) framework for a quadcopter Unmanned Aerial Vehicle (UAV). The proposed framework is capable of achieving trajectory tracking, estimating states, as well as estimating and compensating for all four actuators multiplicative faults. The framework combines two optimization-based nonlinear estimation and control techniques; Nonlinear Moving Horizon Estimation (NMHE) and Nonlinear Model Predictive Control (NMPC). We formulate the NMHE algorithm such that it simultaneously estimates the states and the 4 actuators faults. These estimates are then provided to NMPC to achieve fault accommodation. To validate our proposed framework, we investigate two fault scenarios, where 4 actuators experience an abrupt and time-varying partial Loss Of Effectiveness (LOE) in the presence of noisy measurements. Finally, simulation results show satisfactory performance and prove that the proposed framework is numerically tractable and real-time applicable.

Robust Time-Delayed PID Flight Control for Automatic Landing Guidance under Actuator Loss-Of-Control

A robust fault-tolerant flight control scheme using a Time-Delayed PID (TD-PID) control law for automatic landing guidance of a fixed-wing aircraft under successive actuator faults is presented in this paper. The desired landing trajectory is translated into attitude reference commands, and a PID control law in discrete-time form within a Time-Delay Control (TDC) framework is synthesized into a TD-PID control for aircraft attitude command tracking control system. The robustness property of TDC towards parametric uncertainties in the system is exploited in this work in developing a fault-tolerant flight control framework to passively to mitigate the effects of partial loss-of-control during the landing phase of the aircraft.