Optimal Fault-tolerant Control Research Articles

DFIG in Wind Energy Applications with High Order Sliding Mode Observer-based Fault-Tolerant Control Scheme using Sea Gull Optimization

This paper describes a new method for maximizing power extraction from a wind energy conversion system (WECS) by using a doubly fed induction generator (DFIG) that operates below nominal wind speed. To maximize the collected power of a wind turbine (WTG) exposed to actuator failure, a fault-tolerant high-order sliding mode observer (HOSMO) and Seagull Optimization Algorithm with a model predictive controller (MPC) technique is proposed. Evaluate both the real state and the sensor error simultaneously using a higher-order sliding-mode observer. Active fault tolerant controllers are designed to regulate wind turbine rotor speed and power in the presence of actuator defects and uncertainty. With the growing interest in employing wind turbines (WTGs) as the primary generators of electrical energy, fault tolerance has been seen as essential to improving efficiency and reliability. This research focuses on optimal fault-tolerant pitch control, which is used to modify the pitch angle of wind turbine blades in the event of sensor, actuator, and system failures. A Seagull Optimization Algorithm (SOA) is proposed to tune controller parameters to improve the performance of WT. The proposed method has achieved 92% of power tracking performance when compared to existing method.

Progressive Optimal Fault-Tolerant Control Combining Active and Passive Control Manners

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified, and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. Simulation results verified the feasibility of the proposed algorithm and the validity of the proposed theory.

Adaptive fault-tolerant optimal control for hypersonic vehicles with state constrains based on adaptive dynamic programming

This paper addresses a novel adaptive fault-tolerant control (FTC) design based on adaptive dynamic programming (ADP) technique for hypersonic vehicle (HSV) subject to actuator fault and state constrains. The total control input is constructed by the combination of backstepping-based adaptive FTC and the ADP-based adaptive optimal control to improve the tracking performance and fault-tolerant capacity. By introducing a barrier Lyapunov function (BLF) to deal with the state constraints, a backstepping-based FTC scheme is designed to transform the tracking control problem into an equivalent optimal control problem. Subsequently, an adaptive optimal control strategy is developed by using the ADP technique to provide a supplementary control action. A critic network is constructed to solve the Hamilton–Jacobi–Bellman (HJB) equation online. The convergence properties of the closed-loop system are developed by utilizing the Lyapunov stability theory. Finally, simulation results are carried out to illustrate the effectiveness and efficiency of the proposed adaptive ADP-based FTC strategy.

Two-Level Local Observer-Based Decentralized Optimal Fault Tolerant Tracking Control for Unknown Nonlinear Interconnected Systems

Two-Level Local Observer-Based Decentralized Optimal Fault Tolerant Tracking Control for Unknown Nonlinear Interconnected Systems

Adaptive optimal fault-tolerant vibration control and sensitivity analysis of semi-active suspension based on a self-powered magneto-rheological damper

To reuse the energy dissipated by vehicle suspension, a semi-active suspension with a self-powered magneto-rheological damper is proposed. An electromechanical coupling model of self-powered semi-active suspension is established. The energy conversion efficiency is defined and investigated by changing the electrical parameters. By considering unmodeled dynamics and perturbation values, an adaptive optimal fault-tolerant control algorithm is proposed to ensure the vibration-isolation performance. The robust index of the adaptive optimal fault-tolerant control algorithm is constructed using the Lyapunov equation and evaluated by changing the key parameters. The sensitivity of the key parameters to the damping force is investigated using a grey relation analysis approach. Furthermore, multi-objective optimization between the vibration-isolation capability and energy harvesting is conducted. Via analysis, the proposed suspension can harvest more energy near the second resonance range. Compared to passive control and self-powered mode, the adaptive optimal control algorithm mitigates vibration more significantly in the time and frequency domains, respectively, under stochastic excitation. The robust index is most sensitive to inductance and the diameter of the magnetism cylinder. The length of the damping channel and the diameter of the magnetism cylinder influence the sensitivity of key parameters to the damping force most obviously.

Fault-tolerant model predictive sliding mode control with fixed-time attitude stabilization and vibration suppression of flexible spacecraft

This brief proposes two novel double-loop fault-tolerant controllers to tackle the problems of stabilizing the attitude and nullifying appendages' vibration of flexible spacecraft by integrating the advantages of model predictive control (MPC) and sliding mode control (SMC). The structure of the first method is as follows: First in the external loop, by using terminal sliding mode (TSM), the attitude of the spacecraft is derived to its reference value, and the proper angular velocity of the interior part is generated. Then, by employing the preciseness and constrained attributes of MPC, an optimal fault-tolerant controller is suggested in the internal part. Constrained fault-tolerant MPC tracks the angular velocity to the set value while guaranteeing appendages' passive vibration suppression. In another method, as opposed to the first technique, in the inner loop fault-tolerant finite-time TSM, and in the outer loop MPC are designed. The unified double-layer nonlinear controller schemes guarantee good dynamics and robustness against external torques and actuator faults. The closed-loop system's stability of the controllers is precisely proved using the Lyapunov theorem in the presence of actuator faults and external disturbances and in the absence of the damping matrix to study the advantages of the proposed methods. The performance of the proposed methods has been compared with each other. Moreover, the simulation results substantiate that the proposed method outperforms faster fixed-time sliding model control significantly.

Event-triggered fault-tolerant control for input-constrained nonlinear systems with mismatched disturbances via adaptive dynamic programming

In this paper, the issue of event-triggered optimal fault-tolerant control is investigated for input-constrained nonlinear systems with mismatched disturbances. To eliminate the effect of abrupt faults and ensure the optimal performance of general nonlinear dynamics, an adaptive dynamic programming (ADP) algorithm is employed to develop a sliding mode fault-tolerant control strategy. When the system trajectories converge to the sliding-mode surface, the equivalent sliding mode dynamics is transformed into a reformulated auxiliary system with a modified cost function. Then, a single critic neural network (NN) is adopted to solve the modified Hamilton–Jacobi–Bellman (HJB) equation. In order to overcome the difficulty that arises from the persistence of excitation (PE) condition, the experience replay technique is utilized to update the critic weights. In this study, a novel control method is proposed, which can effectively eliminate the effects of abrupt faults while achieving optimal control with the minimum cost under a single network architecture. Furthermore, the closed-loop nonlinear system is proved to be uniformly ultimate boundedness based on Lyapunov stability theory. Finally, three examples are presented to verify the validity of the control strategy.

Adaptive Neural Inverse Optimal Tracking Control for Uncertain Nonlinear System With Actuator Faults

Most optimal control method aim at finding a control law to minimize the corresponding cost function, and the key problem is to solve the Hamilton–Jacobi–Bellman equation (HJBE). However, the solution of HJBE is hard and even unavailable to obtain for nonlinear system and, thus, the optimal control method can not be developed. To address this problem, an adaptive neural inverse optimal control method is proposed for nonlinear systems with external disturbances and actuator failures in this article. The cost function in this article is defined based on the controller and, therefore, once the controller is obtained, the corresponding cost function is minimized. Accordingly, the directly solution of HJBE is avoided. In more detail, the auxiliary system is constructed to develop the auxiliary controller, based on which the actual optimal fault-tolerant controller can be further obtained. The uncertainties in system dynamics are compensated by neural networks, and the unknown actuator failures are handled by adaptive control gain. It is proved that nonlinear system is input-to-state stabilizable (ISS) and the proposed controller can minimize the cost function that puts penalty on tracking error and system input. Two simulation examples are provided to demonstrate the effectiveness of the proposed control method.

Neural network–based optimal fault compensation control of the nonlinear multi-agent system and its application to UAVs formation flight

This article investigates the optimal consensus problem for unmanned aerial vehicle formation systems with actuator faults based on nonlinear multi-agent systems. Initially, for fault-free multi-agent system, the distributed optimal controllers are constructed based on the adaptive dynamic programming technique. A critic neural network is applied to approximate the solution of the nonlinear Hamilton–Jacobi–Bellman equations, in which the weight updating laws are built to guarantee the weight vectors of the critic neural network convergence. Second, the fault compensators and corresponding tuning laws are proposed to compensate for actuator faults. Through a combination of optimal controllers and fault compensators, the distributed optimal fault-tolerant controllers are obtained. Then, according to Lyapunov extension theorem, some stability criteria for ensuring the stability of the aircraft and the normal flight of the unmanned aerial vehicle formation are established in the event of an actuator failure. Finally, an example of an unmanned aerial vehicle formation system is introduced to verify the efficiency and reliability of the designed optimal fault-tolerant control scheme.

Fault-Tolerant Control Strategy for Sub-Modules Open-Circuit Fault of Modular Multilevel Converter

Modular multilevel converter (MMC) is a key device of high-voltage-direct circuit (HVDC) transmission system, the sub-module detection technology of which will directly influence the damage severity, and even the reliability of the whole system. In this paper, the open-circuit fault characteristics of an insulated gate bipolar transistor (IGBT) in a sub-module are analyzed, and a fault-tolerant optimal control strategy is proposed for the redundant hot-reserved MMC based on nearest-level modulation (NLM). A fault sub-module diagnosis and location strategy based on the deviation distance of the capacitor voltages is presented. After the faulty sub-module is removed, due to the asymmetric operation of the MMC, odd-order circulating currents are introduced in the faulty phase, in which the fundamental-frequency circulating current is the major component; the fundamental-frequency voltage related to the redundancy rate is injected into the faulty phase, which effectively suppresses the fundamental-frequency circulating current and harmonics in the faulty phase. The proposed method combines fault detection and fault ride-through steps, so it has the features of high reliability and high compatibility. Based on the Matlab/Simulink simulation model, the effectiveness of the proposed strategy is verified.

Fixed-Time Optimal Fault-Tolerant Formation Control With Prescribed Performance for Fixed-Wing UAVs Under Dual Faults

Fixed-Time Optimal Fault-Tolerant Formation Control With Prescribed Performance for Fixed-Wing UAVs Under Dual Faults

Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming

<abstract><p>The fault-tolerant control problem of a hydraulic servo actuator in the presence of actuator faults is studied utilizing adaptive dynamic programming. This task is challenging because of unknown system dynamics, uncertain disturbances or unmeasurable system states of such highly nonlinear systems in real applications. The aim is to achieve asymptotic tracking and actuator faults compensation by minimizing some predefined performance index. The discrete-time algebraic Riccati equation is iteratively solved by the adaptive dynamic programming approach. For practical reasons, adaptive dynamic programming techniques and fault compensation are integrated to iteratively compute an approximated optimal fault-tolerant control using real-time input/output data without any a priori knowledge of the system dynamics and unmeasurable states. As a result, a fault-tolerant control of hydraulic servo actuator is then designed based on adaptive dynamic programming via output feedback. Also, the convergence analysis of a data-driven fault-tolerant control is theoretically shown as well. Finally, intensive simulation results are given to prove the validity and merits of the developed data-driven fault-tolerant control strategy.</p></abstract>

Reinforcement Learning Applications in Unmanned Vehicle Control: A Comprehensive Overview

This paper briefly reviews the dynamics and the control architectures of unmanned vehicles; reinforcement learning (RL) in optimal control theory; and RL-based applications in unmanned vehicles. Nonlinearities and uncertainties in the dynamics of unmanned vehicles (e.g. aerial, underwater, and tailsitter vehicles) pose critical challenges to their control systems. Solving Hamilton–Jacobi–Bellman (HJB) equations to find optimal controllers becomes difficult in the presence of nonlinearities, uncertainties, and actuator faults. Therefore, RL-based approaches are widely used in unmanned vehicle systems to solve the HJB equations. To this end, they learn the optimal solutions by using online data measured along the system trajectories. This approach is very practical in partially or completely model-free optimal control design and optimal fault-tolerant control design for unmanned vehicle systems.

Fault-Tolerant Optimal Spacecraft Attitude Maneuver: An Incremental Model Approach

This paper addresses the fault-tolerant optimal attitude control problem for a rigid spacecraft with external disturbance, model uncertainties, and actuator faults. Firstly, the incremental nonlinear control technology is used to simplify the attitude control system into an incremental nominal model with a synthetic uncertainty/fault term that is estimated by a nonsingular terminal sliding mode disturbance observer. Secondly, considering a quadratic performance index, the original fault-tolerant optimal control problem is transformed into a guaranteed performance optimal problem of the nominal model. Benefiting from the simplicity of the incremental model, such a modified optimal control problem is solved online by the modified policy iteration adaptive dynamic programming without any initial stable policy assumption. Finally, the attitude angles of the closed-loop system are proved theoretically to be uniformly ultimately bounded at the desired state by using the Lyapunov methods. Simulation results are given to verify the effectiveness of the proposed fault-tolerant optimal attitude control strategy.

Reinforcement learning-based optimal fault-tolerant control for offshore platforms

This article investigates a novel reinforcement learning-based fault-tolerant control approach for steel-jacket offshore platforms. In the first step, the dynamic of the steal-jacket offshore platform with an active mass damper is considered, and the equivalent linear time-invariant model is obtained with the actuator fault. In fault-free conditions, an optimal controller is designed to keep the system stable under external wave force. Subsequently, in faulty conditions, the actuator fault is estimated by the fault observer. Next, by inserting the actuator fault estimation into the cost function, the fault-tolerant control problem transforms into the optimal control problem. The online policy iteration is used to minimize the new cost function. Finally, the final control law, which is a mixture of the nominal and the modified control law, stabilizes the offshore platform and improves its performance in the presence of the actuator fault without needing the complete knowledge of the offshore platform. The simulation results show the effectiveness of the proposed method.

Fault-tolerant optimal pitch control of wind turbines using dynamic weighted parallel firefly algorithm

With steadily increasing interest in utilizing wind turbine (WT) systems as primary electrical energy generators, fault-tolerance has been considered decisive to enhance their efficiency and reliability. In this work, an optimal fault-tolerant pitch control (FTPC) strategy is addressed to adjust the pitch angle of WT blades in the presence of sensor, actuator, and system faults. The proposed scheme incorporates a fractional-calculus based extended memory (EM) of pitch angles along with a fractional-order proportional–integral–derivative (FOPID) controller to enhance the performance of the WT. A dynamic weighted parallel firefly algorithm (DWPFA) is also proposed to tune the controller parameters. The efficiency of the proposed algorithm is evaluated on the test functions adopted from 2017 IEEE congress on evolutionary computation (CEC2017). The merits of the proposed fault-tolerant approach are tested on a 4.8-MW WT benchmark model and compared to conventional PI and optimal FOPID approaches. Corresponding comparative simulation results validate the effectiveness and fault-tolerant capability of the proposed control paradigm, where it is observed that the proposed control scheme tends to be more consistent in the power generated at a given wind speed.

Fault-Tolerant Optimal Control for Discrete-Time Nonlinear System Subjected to Input Saturation: A Dynamic Event-Triggered Approach.

This paper investigates the dynamic event-triggered fault-tolerant optimal control strategy for a class of output feedback nonlinear discrete-time systems subject to actuator faults and input saturations. To save the communication resources between the sensor and the controller, the so-called dynamic event-triggered mechanism is adopted to schedule the measurement signal. A neural network-based observer is first designed to provide both the system states and fault information. Then, with consideration of the actuator saturation phenomenon, the adaptive dynamic programming (ADP) algorithm is designed based on the estimates provided by the observer. To reduce the computational burden, the optimal control strategy is implemented via the single network adaptive critic architecture. The sufficient conditions are provided to guarantee the boundedness of the overall closed-loop systems. Finally, the numerical simulations on a two-link flexible manipulator system are provided to verify the validity of the proposed control strategy.

Event‐triggered decentralized optimal fault tolerant control for mismatched interconnected nonlinear systems through adaptive dynamic programming

AbstractIn this paper, we propose an event‐triggered decentralized optimal fault tolerant control (ETDOFTC) scheme based on adaptive dynamic programming for mismatched interconnected nonlinear systems with actuator failures. For fault‐free dynamic models, the decentralized control problem is addressed by developing a set of decentralized optimal control strategies for isolated subsystems with modified value functions, which are approximated by critic neural networks. Meanwhile, the neural network‐based decentralized observer is established to approximate actuator failures and mismatched unknown interconnections. The weights of the neural networks are aperiodically updated at the designed triggering instants. Then, the proposed ETDOFTC scheme is obtained by combining the event‐triggered decentralized optimal control strategies with the adaptive fault compensators. Furthermore, it is proved that all the signals of the closed‐loop system are uniformly ultimately bounded via the Lyapunov stability analysis. Finally, simulation results of two examples are presented to confirm the effectiveness of the proposed ETDOFTC scheme.

Prescribed performance incremental adaptive optimal fault-tolerant control for nonlinear systems with actuator faults

In this paper, a model-free incremental adaptive optimal fault-tolerant controller with prescribed performance is proposed for nonlinear systems subject to actuator faults. Considering the system actuator redundancy, an actuator grouping scheme is introduced according to the actuator functions. An incremental adaptive fault observer is designed to estimate the faults. The recursive least squares (RLS) identification is utilized to identify the incremental system parameters. Both dynamic process requirements and optimal performance index of the closed loop system are taken into consideration by combining prescribed performance bound (PPB) and incremental adaptive dynamic programming (IADP). Simulations are given to verify the effectiveness of the proposed IADP reconfigurable fault-tolerant control scheme.

Policy Iteration Based Online Adaptive Optimal Fault Compensation Control for Spacecraft

In this paper, a novel online adaptive optimal fault-tolerant control (FTC) scheme based on policy iteration (PI) algorithm is developed for the spacecraft attitude angular velocity tracking control with actuator faults. First, for the fault-free attitude dynamic model, an adaptive optimal controller is designed based on PI algorithm to solve the Hamilton-Jacobi-Bellman (HJB) equation. In this process of solving, the value function is approximated by means of a critic neural network (NN). Then, a novel adaptive compensation law is proposed for estimation of actuator faults. The closed-loop system is proved to be uniformly ultimately bounded (UUB) via the Lyapunov’s method. Finally, the effectiveness of the proposed control scheme is evaluated via simulation experiments.