Tolerant Control For Nonlinear Systems Research Articles

Robust adaptive fault tolerant control for nonlinear systems with actuator failure and mismatched disturbance

In this paper, a class of nonlinear systems with mismatched disturbance and actuator failure is investigated. A disturbance observer is proposed to estimate the disturbance first and the error of the estimation converges to zero exponentially. By introducing an integral sliding mode surface, the disturbance observer-based integral sliding mode fault tolerant control scheme is proposed to attenuate the disturbance and guarantee the stability of the system. In particular, the control law is designed for decoupling the partial disturbance and attenuating the disturbance that cannot be decoupled. Finally, two examples are given to illustrate the effectiveness of the proposed method.

Passivation blocks for fault tolerant control of nonlinear systems

This paper describes a novel passivity/dissipativity based approach for fault tolerant control (FTC) of nonlinear systems by means of fault hiding, i.e., by inserting reconfiguration blocks (RBs) between the plant and controller to mitigate the fault effects. The proposed approach is used to design a new kind of RB, called passivation block (PB), which is generically employed for sensor and actuator faults and achieves simultaneously series, feedback and feedfoward passivation of the controller during a fault occurrence. Based on the dissipativity theory, new conditions are obtained to design a dynamic PB (DPB) which requires minimum information on the system model. In particular, the proposed DPB can be systematically obtained by combining the LMI-based conditions based on the knowledge about the passivity indices. Numerical simulations are carried out and indicate that the PBs are able to stabilize an example of a faulty nonlinear system.

Distributed adaptive output-feedback fault tolerant control for nonlinear systems with sensor faults

Distributed adaptive output-feedback fault tolerant control for nonlinear systems with sensor faults

TS fuzzy reconfiguration blocks for fault tolerant control of nonlinear systems

This paper introduces a new Takagi–Sugeno (TS) reconfiguration block structure for control reconfiguration of nonlinear systems with sensor and actuator faults represented by TS models and provides conditions for the existence of such blocks that ensures the stability recovery by fault hiding. The proposed reconfiguration block structure consists of a convex sum of linear transformations of the control signals and the sensor measurements hiding the fault occurrence from the nominal controller. Similar static reconfiguration blocks were previously proposed in the literature, however the conditions for their feasibility are more restrictive. This paper presents novel conditions based on the Lyapunov stability theory for obtaining those TS reconfiguration blocks for stability recovery after actuator and sensor faults. These conditions are obtained by assuming that the Lyapunov function that ensures the stability of fault-free closed-loop TS system is a valid Lyapunov candidate for the reconfigured TS system under faulty conditions. In particular, stability recovery conditions for nonlinear systems represented by TS models with nonlinear consequent and networked TS models are provided. Simulation results for a power system and a network of pendulum systems are used to illustrate the effectiveness of the proposed reconfiguration block.

Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator

This paper proposes a new fault tolerant control scheme for a class of nonlinear systems including robotic systems and aeronautical systems. In this method, a sliding mode control is applied to maintain system stability under the post-fault dynamics. A neural network is used as on-line estimator to reconstruct the change rate of the fault and compensate for the impact of the fault on the system performance. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimator is guaranteed to converge to the fault change rate, while the entire closed-loop system stability and tracking control is guaranteed. Compared with the existing methods, the proposed method achieved fault tolerant control for time-varying fault, rather than just constant fault. This greatly expands the industrial applications of the developed method to enhance system reliability. The main contribution and novelty of the developed method is that the system stability is guaranteed and the fault estimation is also guaranteed for convergence when the system subject to a time-varying fault. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results demonstrated that the post-fault is stable and the performance is maintained.

Output Integral Sliding Mode Fault Tolerant Control for Nonlinear Systems With Actuator Fault and Mismatched Disturbance

In this paper, an adaptive output sliding mode fault tolerant control (AOSMFTC) strategy is proposed for a class of nonlinear systems with actuator fault and mismatched disturbance. A composite observer is initially proposed to estimate the state, disturbance, and actuator fault efficiency factor. Subsequently, by introducing a sliding mode observer, the bias actuator fault is reconstructed. Furthermore, in accordance with the estimated information, the AOSMFTC scheme is presented to tolerate the fault, meanwhile the disturbance is attenuated. The $H_{\infty } $ performance is considered for the robustness of the system. Finally, a B747-100/200 aircraft model and an electric circuit system are simulated; the simulation results illustrate the effectiveness of the proposed method.

A quadratic boundedness approach to fault tolerant control for nonlinear system

In the paper, a methodology of actuator fault estimation and fault tolerant control for nonlinear systems is proposed. To solve such a challenging problem, a quadratic boundedness (QB) approach is used to design observer in such a way that the state and fault estimation error will converge to the origin (as close as possible) irrespective of the external disturbances. The same approach (QB) is used to design fault tolerant control to compensate the effect of an actuator fault in system, which is suitably integrated with state and fault estimation schemes. Sufficient conditions for the existence and stability of the proposed state and fault estimation scheme as well as the fault tolerant control are expressed in the form of linear matrix inequalities (LMIs). To show effectiveness of the proposed approach, the final part of the paper presents simulation results concerning a quadruple–tank system.

Sensor fault tolerant control of nonlinear Takagi-Sugeno systems. Application to vehicle lateral dynamics

This paper presents a new scheme for sensor fault tolerant control for nonlinear systems based on the Takagi–Sugeno modeling. First, a structured residual generator aimed at detecting and isolating sensor faults is designed. A bank of observers controlled either by only one system output or a set of outputs is then implemented, leading to a set of state estimates. The parallel distributed compensation structure is adopted to design the fault tolerant controller. The novelty in this paper is that the estimated state used in the controller is a weighted state vector obtained from all the estimated states provided by the different observers. The weighting functions depend on the residual vector signals delivered by the residual generator. They are designed to avoid crisp switches in the control law. Indeed, the interesting feature of the proposed approach is to avoid the commonly used switching strategy. For each residual component, the greater its magnitude is, the less the weight affected to the corresponding state estimate is. Consequently, the controller only uses estimations computed on the basis of healthy measurements. The closed-loop stability is studied with the Lyapunov theory, and the obtained conditions are expressed as a set of linear matrix inequalities. The proposed residual generation and fault tolerant controller are applied to a vehicle lateral dynamics affected by sensor faults. Copyright © 2015 John Wiley & Sons, Ltd.

Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust L 2 norm fault estimation and robust L 2 norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference.

Nonlinear observer based sensor fault tolerant control for nonlinear systems

This paper deals with the problem of sensor fault tolerant control for Takagi-Sugeno nonlinear systems. Firstly, a residual generator is designed in order to detect and isolate sensor faults. Secondly, a nonlinear observer based controller, adopting the so-called parallel distributed compensation structure is designed. This controller is based on a weighted blending of the estimated states provided by different observers. Each observer is constructed to estimate the system state from the inputs and only one output. The blending of the estimates depends on the residual magnitudes in order to minimize the use of faulty estimates in the control law. The stability of the global closed-loop system is studied by Lyapunov theory and the gains of the fault tolerant controller are obtained by solving Linear Matrix Inequalities.

Active fault tolerant control of nonlinear systems: The cart-pole example

Active fault tolerant control of nonlinear systems: The cart-pole exampleThis paper describes the design of fault diagnosis and active fault tolerant control schemes that can be developed for nonlinear systems. The methodology is based on a fault detection and diagnosis procedure relying on adaptive filters designed via the nonlinear geometric approach, which allows obtaining the disturbance de-coupling property. The controller reconfiguration exploits directly the on-line estimate of the fault signal. The classical model of an inverted pendulum on a cart is considered as an application example, in order to highlight the complete design procedure, including the mathematical aspects of the nonlinear disturbance de-coupling method based on the nonlinear differential geometry, as well as the feasibility and efficiency of the proposed approach. Extensive simulations of the benchmark process and Monte Carlo analysis are practical tools for assessing experimentally the robustness and stability properties of the developed fault tolerant control scheme, in the presence of modelling and measurement errors. The fault tolerant control method is also compared with a different approach relying on sliding mode control, in order to evaluate benefits and drawbacks of both techniques. This comparison highlights that the proposed design methodology can constitute a reliable and robust approach for application to real nonlinear processes.

Adaptive Neural-based Fault Tolerant Control for Nonlinear Systems

A neural network based fault tolerant control for unknown nonlinear systems is proposed. The faultless system is controlled by a nonlinear Internal Model Controller (IMC), where both the direct and inverse models of the plant are carried out by neural networks. Using the residual signal generated from the fault detection path, an extra neural-network fault compensation loop is introduced. This neural network is a two layer perceptron and the weights and bias are updated on-line by the modified-gradient approach, which tries to minimize the control error induced by the fault. In this context, a fault tolerant control scheme is obtained. This scheme is tested in simulation in a pH plant with good results.

Adaptation of diagonal recurrent neural network model

An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems.

Sensor Fault Tolerant Control of Nonlinear Systems with Application to a Three-Tank-System

Based on a modified Strong Tracking Filter (MSTF), a new approach to sensor fault tolerant control of nonlinear systems is proposed. Generic Model Control (GMC) is adopted to control the nonlinear system during the system runs normally. A sensor bias vector is then introduced to the output equation of the system model, which is estimated on-line based on the MSTF during every control period. With the estimated sensor bias vector, a fault detection mechanism is developed to supervise the sensors. When the sensor fault is detected, the conventional GMC is switched to a state estimation and output prediction based GMC. This constitutes a reconfigurable control scheme. The laboratory experimental results on a three-tank-system demonstrated the effectiveness of the proposed approach.