Fault-tolerant Control Approach Research Articles

Adaptive fault-tolerant depth control for a bionic robotic dolphin with stuck oscillating joints

The bionic robotic dolphin often employs a multi-joint dorsoventral mechanism to achieve dolphin-like undulatory propulsion, ensuring excellent maneuverability. However, the motors of the oscillating joints in this mechanism are susceptible to sticking faults, posing a challenge for the robotic dolphin to maintain its intended swimming depth, leading to undesired ascent or descent. To address this issue, we propose an adaptive fault-tolerant depth control method for a bionic robotic dolphin experiencing oscillating joint faults. First, the mechatronic design and dynamic model of a four-joint robotic dolphin are introduced. Thereafter, the swimming behaviors of the robotic dolphin with struck oscillating joints are analyzed. Based on this, an adaptive fault-tolerant depth controller is designed to minimize the reference tracking errors by updating dynamic model parameters in real time. Extension experiments are conducted to validate the effectiveness of the proposed fault-tolerant control method. The results demonstrate that this controller enables the robotic dolphin to successfully achieve and maintain the target depth, even in the presence of stuck oscillating joints. These findings confirm the robustness and practicality of the fault-tolerant depth control approach for the bionic robotic dolphins, thereby ensuring their safety in complex ocean conditions.

Formation tracking control for networked heterogeneous MSV systems with actuator faults: A distributed optimal event-triggered fixed-time sliding mode fault-tolerant control approach

This paper studies the formation tracking problem in networked heterogeneous marine surface vessel systems (NHMSVs) subject to model perturbations, marine environmental disturbances, and actuator faults. A distributed optimal event-triggered fixed-time sliding mode fault-tolerant control (EFSMFC) method is proposed, which can achieve formation tracking within a fixed time that is only related to the control parameters. At the same time, a distance-based Floyd distributed optimization method is used to reduce the high communication cost caused by the redundancy of the network communication topology. In addition, an event-triggered strategy is integrated into the design of the controller to reduce the update frequency of control signals and reduce waste of communication resources. Finally, using Lyapunov stability theory, the global fixed-time convergence of system errors is proved. The simulation results demonstrate the feasibility of the proposed method, exhibiting superior robustness and speed compared to existing control methods.

Event-triggered explorized IRL-based decentralized fault-tolerant guaranteed cost control for interconnected systems against actuator failures

This paper presents a novel data-based decentralized guaranteed cost (DGC) fault tolerant control (FTC) scheme for the large-scale systems subject to actuator faults and mismatched interconnection. First, the FTC issues of interconnected systems are converted into a series of near optimal event-triggered control (ETC) methods for isolated subsystems via constructing a modified performance index function of each subsystem. By means of adaptive dynamic programming (ADP) algorithm, the upper bound of performance index function of large-scale systems can be obtained by solving the Hamilton–Jacobi-Bellman (HJB) equation of each auxiliary subsystem. Second, according to the proposed ADP-based decentralized approach and utilizing the event-based synchronous integral reinforcement learning (IRL) algorithm, a model-free guaranteed cost (GC) FTC approach is developed for interconnected large-scale system which can relax the restriction on the condition that system functions must be known. Further, the ultimate uniformly bounded (UUB) stability of auxiliary subsystems can be proved according to the Lyapunov principle. Finally, the effectiveness of the proposed control method is verified by presenting the simulation results.

Adaptive Fault-Tolerant Control of Mobile Robots with Fractional-Order Exponential Super-Twisting Sliding Mode

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches.

Adaptive critic design for safety-optimal FTC of unknown nonlinear systems with asymmetric constrained-input

Safe fault tolerant control is one of the key technologies to improve the reliability of dynamic complex nonlinear systems with limited inputs, which is hard to solve and definitely a great challenge to tackle. Thus the paper presents a novel safety-optimal FTC (Fault Tolerant Control) approach for a category of completely unknown nonlinear systems incorporating actuator fault and asymmetric constrained-input, which can guarantee the system’s operation within a safe range while showcasing optimal performance. Firstly, a CBF (Control Barrier Function) is incorporated into the cost function to penalize unsafe behaviors, and then we translate the intractable safety-optimal FTC problem into a differential ZSG (Zero-Sum Game) problem by defining the control input and the actuator fault as two opposing sides. Secondly, a neural-network-based identifier is employed to reconstruct system dynamics using system data, and the resolution of handling asymmetric constrained-input with the introduced non-quadratic cost function is achieved through the design of an adaptive critic scheme, aiming to reduce computational expenses accordingly. Finally, through the theoretical stability analysis, it is demonstrated that all signals in the closed-loop system are consistently UUB (Uniformly Ultimately Bounded). Furthermore, the proposed method’s effectiveness is also verified in the simulation experiments conducted on a model of a single-link robotic arm system with actuator failure. The result shows that the algorithm can fulfill the safety-optimal demand of fault tolerant control in fault system with asymmetric constrained-input.

Prescribed Performance Fault-Tolerant Control for Synchronization of Heterogeneous Nonlinear MASs Using Reinforcement Learning.

In this article, a novel approach of prescribed performance synchronization control is developed for heterogeneous nonlinear multiagent systems (MASs) subject to unknown actuator faults. Considering that not all followers are able to access the information of the leader, a distributed auxiliary perception system is proposed to estimate the state information of the leader to guarantee that the estimation errors converge to zero within fixed time. Then, based on the estimated states, a prescribed performance fault-tolerant control (FTC) approach is proposed, which achieves the user-defined performance specifications even in the presence of system faults. Moreover, as accurate system dynamic models are perhaps hard to acquire in practical engineering, a data-based method is proposed by using the reinforcement learning (RL) algorithm to design the fault-tolerant controller, which only needs the off-policy online data and is independent of the model dynamics of followers. The stability and synchronization with the prescribed behavior are guaranteed through the Lyapunov stability theorem. Finally, simulation results are presented to illustrate the effectiveness of the developed controller.

Fault tolerant motion planning for a quadrotor subject to complete rotor failure

Assuring safety of a quadrotor subject to rotor failure has been heavily investigated at the control level in view of fault tolerant control (FTC) approach. Yet, the existing FTCs are often concerned with tracking the reference motion even when that reference may not be safely trackable due to the physical constraints of the quadrotor. This paper tackles the faulty quadrotor safety at the planner level, proposing a fault tolerant motion planner. Starting from the formal backward reachability problem formulation, the proposed motion planner generates the time trajectory of the coupled rotational and translational motions that safely guide the faulty quadrotor. The generated trajectory is theoretically guaranteed to be tracked by the embedded FTC without violating the physical constraints. Further, the trajectory is prescribed as an analytical closed-form expression and thus suitable for real-time emergency maneuvers. The effectiveness of the proposed motion planner is numerically validated in conjunction with the different FTC techniques and compared to the existing planning method. The simulation results clearly signify that the proposed planner can successfully complement the fault tolerance of quadrotor. The supplements including code implementations are available on GitHub repository: https://github.com/HMCL-UNIST/Fault-tolerant-motion-planner.

Adaptive neural event-dependent intermittent fault-tolerant control of reaction–diffusion multi-agent systems

This article employs an event-dependent intermittent fault-tolerant control approach to address the adaptive consensus of reaction–diffusion multi-agent systems under directed topologies in both leaderless and leader-following networks. Initially, an event-dependent intermittent control mechanism is proposed to determine the work/rest time, relying on three partitions of the non-negative real region and the formulated Lyapunov function. Subsequently, under the introduced control mechanism, an adaptive consensus protocol is established, incorporating the updating law for adaptive coupling strength and adaptive weight estimation. Utilizing the neural network estimation theorem for fault estimation, the article presents explicit criteria to ensure the attainment of leaderless or leader-following consensus in reaction–diffusion multi-agent systems. Finally, two numerical simulation experiments are conducted to validate the robustness and effectiveness of the derived theorem.

Robust fault detection and adaptive fixed-time fault-tolerant control for quadrotor UAVs

This note scrutinizes an adaptive fault-tolerant control (FTC) approach tailored for unmanned aerial vehicles (UAVs), addressing the critical need for both fault accommodation and disturbance suppression. Departing from traditional reliance on robust discontinuous control strategies prone to chattering and demanding precise uncertainty bounds, our FTC method ensures fixed-time stability, guaranteeing the convergence of attitude tracking errors to zero. Central to our approach is an adaptive algorithm adept at concurrently estimating unknown actuator faults and upper bounds of lumped uncertainties. Moreover, our adaptive schemes accurately estimate the upper bound of the lumped uncertainty term, encompassing model uncertainties, external disturbances, and unmodeled dynamics, thereby eliminating the need for assuming known bounds on uncertainties. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Notably, our strategy employs an extended Kalman filter (EKF) observer for state estimation and fault detection, facilitating fault detection through an adaptive threshold technique dynamically adjusted based on real-time mean and variance of the residual signal. Through comprehensive simulation and experimental validations, our proposed methodology demonstrates significant advancements in ensuring safety and reliability in UAVs.

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.

A robust [formula omitted] fault-tolerant control approach for time-delay LPV systems with uncertain parameters and unknown disturbances

The article proposes a H∞ fault-tolerant control approach for a series of uncertain linear parameter-varying (LPV) time-delay models to obtain disturbance suppressions. Specifically, by applying the Lyapunov functions, a series of feedback controllers are provided to ensure the robust performance of LPV models with actuator faults. Meanwhile, a convex optimization strategy is developed for resolving optimization problems in the presence of bilinear matrix inequalities (BMIs), where the robustness conditions are improved to guarantee the stability of LPV model under uncertain factors. By resolving a class of linear matrix inequalities (LMIs), the gain matrices for LPV systems can be obtained. Furthermore, the less conservative conditions are developed and supported by strict theoretical derivation. Ultimately, the validity of proposed approach is confirmed by simulation analyses of truck–trailer systems.

Active Fault-Tolerant and Attack-Resilient Control for a Renewable Microgrid Against Power-Loss Faults and Data Integrity Attacks.

The next-generation power grid evolves from the development of fundamental cyber-physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid's real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

Adaptive fractional-order integral fast terminal sliding mode and fault-tolerant control of dual-arm robots

AbstractClosed-loop kinematics of a dual-arm robot (DAR) often induces motion conflict. Control formulation is increasingly difficult in face of actuator failures. This article presents a new approach for fault-tolerant control of DARs based on advanced sliding mode control. A comprehensive fractional-order model is proposed taking nonlinear viscous and viscoelastic friction at the joints into account. Using integral fast terminal sliding mode control and fractional calculus, we develop two robust controllers for robots subject to motor faults, parametric uncertainties, and disturbances. Their merits rest with their strong robustness, speedy finite-time convergence, shortened reaching phase, and flexible selection of derivative orders. To avoid the need for full knowledge of faults, robot parameters, and disturbances, two versions of the proposed approach, namely adaptive integral fractional-order fast terminal sliding mode control, are developed. Here, an adaptation mechanism is equipped for estimating a common representative of individual uncertainties. Simulation and experiment are provided along with an extensive comparison with existing approaches. The results demonstrate the superiority of the proposed control technique. The robot performs well the tasks with better responses (e.g., with settling time reduced by at least 16%).

Path tracking control for autonomous vehicles using hybrid fault-tolerant approach

Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach.

Fixed-Time Control for a Flexible Smart Structure With Actuator Failure: A Broad Learning System Approach.

This article proposes an adaptive fault-tolerant control (AFTC) approach based on a fixed-time sliding mode for suppressing vibrations of an uncertain, stand-alone tall building-like structure (STABLS). The method incorporates adaptive improved radial basis function neural networks (RBFNNs) within the broad learning system (BLS) to estimate model uncertainty and uses an adaptive fixed-time sliding mode approach to mitigate the impact of actuator effectiveness failures. The key contribution of this article is its demonstration of theoretically and practically guaranteed fixed-time performance of the flexible structure against uncertainty and actuator effectiveness failures. Additionally, the method estimates the lower bound of actuator health when it is unknown. Simulation and experimental results confirm the efficacy of the proposed vibration suppression method.

Distributed Fault Tolerant Consensus Control of Nonlinear Multiagent Systems via Adaptive Dynamic Programming.

This article develops a distributed fault-tolerant consensus control (DFTCC) approach for multiagent systems by using adaptive dynamic programming. By establishing a local fault observer, the potential actuator faults of each agent are estimated. Subsequently, the DFTCC problem is transformed into an optimal consensus control problem by designing a novel local value function for each agent which contains the estimated fault, the consensus errors, and the control laws of the local agent and its neighbors. In order to solve the coupled Hamilton-Jacobi-Bellman equation of each agent, a critic-only structure is established to obtain the approximate local optimal consensus control law of each agent. Moreover, by using Lyapunov's direct method, it is proven that the approximate local optimal consensus control law guarantees the uniform ultimate boundedness of the consensus error of all agents, which means that all following agents with potential actuator faults synchronize to the leader. Finally, two simulation examples are provided to validate the effectiveness of the present DFTCC scheme.

Fixed-time nonsingular adaptive attitude control of spacecraft subject to actuator faults

In this article, a fixed-time adaptive fault-tolerant control approach is presented for the attitude tracking of rigid spacecraft subject to inertia uncertainties, external disturbances, and actuator faults. The designed controller is developed as a combination of the fixed-time integral sliding mode control and parametric adaptation technique. The fixed-time integral sliding mode controller has no singularity problem naturally by constructing a novel integral sliding mode surface based on the bi-limit homogeneous method. Moreover, the parametric adaptation technique is incorporated to estimate the total uncertainty indirectly. Benefiting from this development, the designed controller is smooth with no obvious chattering phenomenon and does not require any information on the upper bound of the total uncertainty. The attitude and angular velocity tracking errors under the designed controller can regulate to the minor fields about zero in fixed time. A remarkable feature of the designed controller is that it is not only robust against inertia uncertainties and external disturbances, but also insensitive to multiple types of actuator faults. Finally, simulation results show the effectiveness and merits of the presented control approach.

Boundary event-triggered FTC of uncertain Euler–Bernoulli beam systems with actuator failures

This work presents an event-triggered fault-tolerant boundary control approach for Euler–Bernoulli beam systems with actuator failures, uncertain system parameters, and external disturbances. Firstly, partial differential equations with boundary conditions are employed to model the system, and an adaptive compensation method is introduced to solve the issue of infinite times actuator faults, encompassing complete and partial faults. By using the relative threshold strategy, the event-triggered fault-tolerant boundary controller is developed to dampen vibration and reduce communication load between actuators and controllers. Simultaneously, the uniformly bounded stability and well-posedness of flexible beam systems are proved by Lyapunov's direct approach and semigroup theory, respectively. Lastly, the validity of the presented algorithm is demonstrated through simulation results.

Adaptive backstepping fault-tolerant control for hypersonic aircraft with unknown control direction under actuator and sensor faults

Abstract In this paper, an adaptive backstepping controller of hypersonic flight vehicle (HFV) system is investigated in the presence of unknown actuator and sensor faults, unknown control direction and varying external disturbance, which expands traditional fault-tolerant control (FTC) approaches in HFV field. The control-oriented model is established with reasonable and comprehensive analysis of various fault types. Compared with the previous FTC research on HFV system, the time-varying and fixed fault types are simultaneously designed for actuator and sensors, and the control direction is unknown, which can more accurately simulate the real fault situations in the hypersonic flight environment. To avoid singularity problems caused by the unknown control direction, the Nussbaum gain function technique is adopted in the control algorithm, which is also capable of overcoming the influence of unknown time-varying fault parameters. The boundness and stability of system under the designed control scheme are theoretically verified. Finally, numerical simulation results demonstrate the effectiveness and superiority of the proposed control strategy.

Vibration Suppression of an Input-Constrained Wind Turbine Blade System

During the actual wind power generation process, wind turbines are often affected by side effects such as blade vibrations, input constraints, and actuator faults. This can lead to a reduction in power generation efficiency and even result in unforeseen losses. This study discusses a robust adaptive fault-tolerant boundary control approach to address the issues of input-constrained and actuator-fault problems in wind turbine blade vibration control. By employing projection mapping techniques and hyperbolic tangent functions, a novel robust adaptive controller based on online dynamic updates is constructed to constrain vibrations, compensate for unknown disturbance upper bounds, and ensure the robustness of the coupled system. Additionally, considering the possibility of actuator faults during the control process, a fault-tolerant controller is proposed to effectively suppress elastic vibrations in the wind turbine blade system even in the presence of actuator faults. The effectiveness of the proposed controller is validated through numerical simulations.