Decentralized Adaptive Output Feedback Fault Detection and Control for Uncertain Nonlinear Interconnected Systems

Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.

Attack-resilient fault detection for interconnected systems under DoS attack

In light of the expanding cyber-space applications, the imperative consideration of cyber-attack ramifications on system security is evident. This paper presents a resilient dynamic event-triggered fault detection scheme for a class of nonlinear interconnected systems subjected to denial of service (DoS) attacks. To counteract multifaceted threats, the co-design challenge involving switched-type fault detection filters and a resilient dynamic event-triggered transmission mechanism is addressed. In the design phase of the filters, the frequency information of the signal is considered comprehensively and linear solvable conditions ensuring desired augment system performance are delineated. Through a series of comparative simulation experiments, the findings support the conclusion that the proposed attack-tolerant fault detection mechanism not only conserves network resources but also demonstrates superior detection capabilities for specific frequency fault signals.

Fault-tolerant prescribed performance control of nonlinear systems with process faults and actuator failures

This paper investigates the fault-tolerant prescribed performance control problem for a class of multiple-input single-output unknown nonlinear systems subject to process faults and actuator failures. In contrast to the related works, we consider a general class of nonlinear systems with both multiplicative nonlinearities and additive nonlinearities corrupted by the process faults; only the boundedness of the process faults and the continuity of the nonlinear functions are required, without the explicit or fixed structures of the fault functions. To conquer this problem, a less-demanding and low-complexity fault-tolerant prescribed performance control approach is proposed. The controller is independent of the specific information of faults or the system model and does not invoke fault diagnosis or neural/fuzzy approximation to acquire such knowledge. It achieves the reference tracking with the predefined rate and accuracy. A comparative simulation on a single-link robot is conducted to illustrate the effectiveness and superiority of the proposed approach.

Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

Dynamic event-triggered fault detection for rotary steerable systems with unknown time-varying noise covariances

This paper is concerned with the fault detection problem for the rotary steerable drilling tool system under unknown vibrations and limited computational resources. Firstly, the drilling tool system can be modeled by a nonlinear stochastic system with unknown time-varying noise covariances. Then, the dynamic event-triggered mechanism is introduced to save computational resources, and the caused transmission error is completely decoupled by nonuniform sampling. Subsequently, a novel unscented Kalman filter is proposed by combining the expectation maximization method to estimate states when noise covariances are unknown. A residual and an evaluation function are constructed to detect faults. Finally, a numerical simulation and an experiment on a drilling tool prototype validate the superior performance of the proposed fault detection scheme, which has lower missed alarm rates and consumes less time than existing methods.

Cost-Sensitive Hypergraph Learning With F-Measure Optimization

The imbalanced issue among data is common in many machine-learning applications, where samples from one or more classes are rare. To address this issue, many imbalanced machine-learning methods have been proposed. Most of these methods rely on cost-sensitive learning. However, we note that it is infeasible to determine the precise cost values even with great domain knowledge for those cost-sensitive machine-learning methods. So in this method, due to the superiority of F-measure on evaluating the performance of imbalanced data classification, we employ F-measure to calculate the cost information and propose a cost-sensitive hypergraph learning method with F-measure optimization to solve the imbalanced issue. In this method, we employ the hypergraph structure to explore the high-order relationships among the imbalanced data. Based on the constructed hypergraph structure, we optimize the cost value with F-measure and further conduct cost-sensitive hypergraph learning with the optimized cost information. The comprehensive experiments validate the effectiveness of the proposed method.

Adaptive Switched Control for Connected Vehicle Platoon With Unknown Input Delays

A connected vehicle platoon with unknown input delays is studied in this article. The control objective is to stabilize the connected vehicles, ensuring all vehicles are traveling at the same speed while maintaining a safety spacing. A decentralized control law using only onboard sensors is designed for the connected vehicle platoon. A novel switching-type delay-adaptive predictor is proposed to estimate the unknown input delays. By using the estimated unknown input delays, the control law can guarantee the stability of the successive vehicles. The platoon control adopts a one-vehicle look-ahead topology structure and a constant time headway (CTH) policy, which makes the desired spacing between vehicles vary with time. In this framework, the stability of the connected vehicles can be derived through the analysis of each pair of two successive vehicles in the platoon. Finally, an example is presented to illustrate the applicability of the obtained results.

Event-Triggered Adaptive Neural Network Sensor Failure Compensation for Switched Interconnected Nonlinear Systems With Unknown Control Coefficients

In this article, a decentralized adaptive neural network (NN) event-triggered sensor failure compensation control issue is investigated for nonlinear switched large-scale systems. Due to the presence of unknown control coefficients, output interactions, sensor faults, and arbitrary switchings, previous works cannot solve the investigated issue. First, to estimate unmeasured states, a novel observer is designed. Then, NNs are utilized for identifying both interconnected terms and unstructured uncertainties. A novel fault compensation mechanism is proposed to circumvent the obstacle caused by sensor faults, and a Nussbaum-type function is introduced to tackle unknown control coefficients. A novel switching threshold strategy is developed to balance communication constraints and system performance. Based on the common Lyapunov function (CLF) method, an event-triggered decentralized control scheme is proposed to guarantee that all closed-loop signals are bounded even if sensors undergo failures. It is shown that the Zeno behavior is avoided. Finally, simulation results are presented to show the validity of the proposed strategy.

Fault Identification for a Class of Nonlinear Systems of Canonical Form via Deterministic Learning

In this article, through a combination of the deterministic learning (DL) method and the adaptive high gain observer (AHGO) technology, a fault identification approach for a class of nonlinear systems in canonical form is proposed. By using the DL method, the partial persistent excitation condition of the identification system is satisfied, and then, the AHGO technology is exploited to estimate the states and the neural network weights simultaneously. To analyze the convergence of the proposed method, we first analyze the uniformed completely observability (UCO) property of the linear part of the nonlinear identification system. Then, by using the Lipschitz property of the nonlinear item and the Bellman-Gronwall lemma, we show that the UCO property of the nonlinear identification system is depended on the UCO property of the linear part when the observer gain is chosen large. Therefore, by using the UCO property of the nonlinear identification system and the Lyapunov stability theorem, the convergence of the proposed learning observer is proven. The attraction of this article is based on the analysis of the UCO property of the identification system, and the convergence of the proposed learning observer can be directly proven. The simulation example is given to demonstrate the effectiveness of the proposed method.

Adaptive Fuzzy Decentralized Dynamic Surface Control for Switched Large-Scale Nonlinear Systems With Full-State Constraints

In this study, an adaptive fuzzy decentralized dynamic surface control (DSC) problem is investigated for switched large-scale nonlinear systems with deferred asymmetric and time-varying full-state constraints. Due to the existence of additional general nonlinearities, complicated output interconnections, and full-state constraints, it is difficult to address the above control problem using existing methods. Fuzzy-logic systems are, therefore, utilized to approximate the unknown nonlinear functions, and the DSC technique is adopted to overcome the "curse of dimensionality" problem. A novel fuzzy adaptive decentralized controller design is presented using the proposed convex combination technique. Furthermore, it is proven that under the proposed controller and state-dependent switching law, all states of the closed-loop system are bounded and deferred asymmetric, and the time-varying full-state constraints are strictly obeyed. The simulation results are presented to demonstrate the effectiveness of the proposed method.

Decentralized Time-Delay Control Using Partial Variables With Measurable States for a Class of Interconnected Systems With Time Delays

This article deals with the problems of stability and control of the interconnected system (IS) with unknown time-varying delays via decentralized time-delay control using partial variables with measurable states. First, the model of the IS with time delays is established, and the relevant control scheme is proposed. The control scheme just needs to control all or partial state variables corresponding to the elements on the main diagonal of the gain matrices, which can reduce the control cost and improve the flexibility of control. In addition, there are no additional restrictions in the process of designing the controller. Second, relevant lemmas are derived. The exponential boundedness and stability analysis of the IS with time delays are presented, respectively, by stability theory, and related results are derived. Meanwhile, the stability domain of the IS is estimated. Besides, the obtained results can also be used for many practical systems, such as the interconnected power system, the multislave teleoperation systems, the brushless dc motor (BLDCM) system, and the chaotic system. Finally, the effectiveness and application of the obtained results are verified by several examples.

Robust Asymptotic Fault Estimation of Discrete-Time Interconnected Systems With Sensor Faults

In this article, a robust asymptotic fault estimation (RAFE) design is proposed for discrete-time interconnected systems with sensor faults. By constructing a singular augmented system, an equivalent description of the considered interconnected systems is presented. Then, a novel RAFE observer is proposed for the singular augmented system. Furthermore, gain matrices of the RAFE observer are calculated based on multiconstrained design. Simulation results are illustrated to show the feasibility of the presented approaches.

Finite-Frequency H-/H∞ Fault Detection for Discrete-Time T-S Fuzzy Systems With Unmeasurable Premise Variables

This paper investigates a finite-frequency H_-/H_∞ fault detection method for discrete-time T-S fuzzy systems with unmeasurable premise variables. To minimize the effect of uncertainties on system performance and maximize that of actuator faults on the generated residual, both the H_∞ disturbance attenuation index and finite-frequency H_- fault sensitivity index are utilized. Since the premised variables are unmeasurable, the existing generalized Kalman-Yakubovich-Popov lemma cannot be directly extended to these nonlinear systems. In this paper, the conditions of allowing one to design the proposed H_-/H_∞ fault detection observer are established and transformed into linear matrix inequalities. Some scalars and slack matrices are introduced to bring extra degrees of freedom in observer design. Finally, a single-link robotic manipulator model is utilized to illustrate that the proposed technique can detect faults with smaller amplitude than that required by a normal H_∞ observer technique.