Fault estimation and fault-tolerant control for discrete-time dynamic systems

In this paper, a novel discrete-time estimator is proposed, which is employed for simultaneous estimation of system states, and actuator/sensor faults in a discrete-time dynamic system. The existence of the discrete-time simultaneous estimator is proven mathematically. The systematic design procedure for the derivative and proportional observer gains is addressed, enabling the estimation error dynamics to be internally proper and stable, and robust against the effects from the process disturbances, measurement noise, and faults. Based on the estimated fault signals and system states, a discrete-time fault-tolerant design approach is addressed, by which the system may recover the system performance when actuator/sensor faults occur. Finally, the proposed integrated discrete-time fault estimation and fault-tolerant control technique is applied to the vehicle lateral dynamics, which demonstrates the effectiveness of the developed techniques

A survey on gain-scheduled control and filtering for parameter-varying systems

Copyright © 2014 Guoliang Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This paper presents an overview of the recent developments in the gain-scheduled control and filtering problems for the parameter-varying systems. First of all, we recall several important algorithms suitable for gain-scheduling method including gain-scheduled proportional-integral derivative (PID) control, H 2, H ∞ and mixed H 2 / H ∞ gain-scheduling methods as well as fuzzy gain-scheduling techniques. Secondly, various important parameter-varying system models are reviewed, for which gain-scheduled control and filtering issues are usually dealt with. In particular, in view of the randomly occurring phenomena with time-varying probability distributions, some results of our recent work based on the probability-dependent gain-scheduling methods are reviewed. Furthermore, some latest progress in this area is discussed. Finally, conclusions are drawn and several potential future research directions are outlined.The National Natural Science Foundation of China under Grants 61074016, 61374039, 61304010, and 61329301; the Natural Science Foundation of Jiangsu Province of China under Grant BK20130766; the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning; the Program for New Century Excellent Talents in University under Grant NCET-11-1051, the Leverhulme Trust of the U.K., the Alexander von Humboldt Foundation of Germany

A review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information

Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German

A Sliding Mode based Cascade Observer for Estimation and Compensation Controller

The sliding mode observer can estimate the system state and the unknown disturbance, while the traditional single-layer one might still suffer from high pulse when the output measurement is mixed with noise. To improve the estimation quality, a new cascade sliding mode observer containing multiple discontinuous functions is proposed in this letter. It consists of two layers: the first layer is a traditional sliding mode observer, and the second layer is a cascade observer. The measurement noise issue is considered in the source system model. An alternative method how to design the observer gains of the two layers, together with how to examine the effectiveness of the compensator based closed-loop system, are offered. A numerical example is provided to demonstrate the effectiveness of the proposed method. The observation structure proposed in this letter not only smooths the estimated state but also reduces the control consumption

Direct thrust force control of primary permanent magnet linear motor based on improved extended state observer and model-free adaptive predictive control

A model-free adaptive predictive control algorithm based on an improved extended state observer (IESO) is proposed to solve the problem that the primary permanent magnet linear motor is susceptible to time-varying parameters and unknown disturbances. Firstly, a model-free adaptive control algorithm based on compact format is designed to achieve high control precision of the system and reduce thrust fluctuation, only through the input/output data of the system. Because the traditional model-free adaptive control is too sensitive to the internal parameters of the controller, a combination of model-free adaptive control and predictive control is further developed. By predicting the data for a future time in advance, the sensitivity to the internal parameters of the controller is reduced and the control performance is further improved. Since the load change and other nonlinear disturbances in practical applications have a great impact on the control effect of the system, an improved extended state observer is further used to compensate for the impact of nonlinear disturbances on the control system. In addition, the stability of the closed-loop system is analyzed. Comparable simulation results clearly demonstrate the good tracking performance and strong robustness of the proposed control

Stabilization of Continuous-Time Random Switching Systems via a Fault-Tolerant Controller

This paper focuses on the stabilization problem of continuous-time random switching systems via exploiting a fault-tolerant controller, where the dwell time of each subsystem consists of a fixed part and random part. It is known from the traditional design methods that the computational complexity of LMIs related to the quantity of fault combination is very large; particularly system dimension or amount of subsystems is large. In order to reduce the number of the used fault combinations, new sufficient LMI conditions for designing such a controller are established by a robust approach, which are fault-free and could be solved directly. Moreover, the fault-tolerant stabilization realized by a mode-independent controller is considered and suitably applied to a practical case without mode information. Finally, a numerical example is used to demonstrate the effectiveness and superiority of the proposed methods

Transmissibility operators for state and output estimation in nonlinear systems

Transmissibility operators are mathematical objects that characterize the relationship between two subsets of responses of an underlying system. The importance of transmissiblity operators comes from the fact that these operators are independent on the system inputs. This work develops the transmissibility theory for nonlinear systems for the first time. The system nonlinearities are assumed to be unknown, which gives a wide range of possible engineering applications in different disciplines. Four different methods are developed to deal with these nonlinearities. The first method is by re-constructing the system nonlinearities as independent excitations on the system. This method handles the inherent unmodeled nonlinearities within the system. The second method is by designing a transmissibility-based sliding mode control. This method rejects unwanted nonlinearities such as system faults. The third method is by constructing the system as time-variant linear system, and use recursive least squares to solve it. This method can handle nonlinear systems with time-variant dynamics. The fourth method is by designing a new robust estimation technique called high-gain transmissibility (HGT) that is inspired by high-gain observers. This estimator has the ability to robustly estimate the system states in a high-gain form. The majority of modern fault detection, control systems, and robots localization depend on mathematically estimating the system states and outputs. Transmissibility-based estimation is incorporated in this work with these three theoretical applications. For fault detection, transmissibility operators are used along a set of outputs to estimate the measurements of another set of outputs. Then faults are detected by comparing the estimated and measured outputs with each other. Control approaches use the transmissibility-based estimation to construct the control signal, in which is injected back to the original system. Robots localization fuses the transmissibility-based estimation with real-time sensor measurements to minimize the error in determining the robot displacements. These three theoretical applications are applied on four different systems. The first system is Connected Autonomous Vehicles (CAV) platoons. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Transmissibilities are proposed along with the measurements from sensors available in CAV platoons to identify transmissibility operators. This will be then developed to mixed autonomous and human-driven vehicle platoons. Besides the wide range of physical and cyber faults in such systems, this is also motivated by the fact that on-road human-drivers’ behaviour is unknown and difficult to be predicted. Transmissibility operators are used here to handle both cyber-physical faults as well as the human-drivers’ behaviour. The platoon faults are then proposed to be mitigated using a transmissibility-based sliding mode controller. Moreover, transmissibilities are integrated with Kalman filter to localize CAV platoons while operating under non-Gaussian environment as unknown nonlinearities. The second system is a multi-actuator micro positioning system that is used in the semi-conductors industry. Transmissibility operators are applied on this system for fault detection and fault-tolerant control. Fault detection is represented in applying the proposed developments to actuator fault detection. Some of the most common actuator faults such as actuator loss of effectiveness and fatigue crack in the connection hinges will be considered. Transmissibilities then will be used for fault detection without knowledge of the dynamics of the system or the excitation that acts on the system. Next, a transmissibility-based sliding mode control will be implemented to mitigate common actuator faults in multi-actuator systems. The third system is flexible structures subjected to unknown and random excitations. Structures used in applications subjected to turbulent fluid flow such as aerospace and underwater applications are subjected to random excitations distributed along the structure. Transmissibility operators are used for the purpose of structural fault detection and localization during the system operation. The fourth system is robotic manipulators with bounded nonlinearities and time-variant parameters. Both parameter variation and system nonlinearities are considered to be unknown. Transmissibility operators are integrated with Recursive Least Squares (RLS) to overcome the unknown variant parameters. RLS identifies transmissibilities used in the structure of noncausal FIR (Finite Impulse Response) models. While parameter variation can be treated as system nonlinearities, the RLS algorithm is used to optimize what time-variant dynamics to include in the transmissibility operator and what dynamics to push to the system nonlinearities over time. The identified transmissibilities are then used for the purpose of fault detection in an experimental robotic arm with variant picked mass

Asynchronous switching control for fuzzy Markov jump systems with periodically varying delay and its application to electronic circuits

This article focuses on addressing the issue of asynchronous H∞ control for Takagi-Sugeno (T-S) fuzzy Markov jump systems with generally incomplete transition probabilities (TPs). The delay is assumed to vary periodically, resulting in one monotonically increasing interval and one monotonically decreasing interval during each period. Meanwhile, a new Lyapunov-Krasovskii functional (LKF) is devised, which depends on membership functions (MFs) and two looped functions formulated for the monotonic intervals. Since the modes and TPs of the original system are assumed to be unavailable, an asynchronous switching fuzzy controller on the basis of hidden Markov model is proposed to stabilize the fuzzy Markov jump systems (FMJSs) with generally incomplete TPs. Consequently, a stability criterion with improved practicality and reduced conservatism is derived, ensuring the stochastic stability and H∞ performance of the closed-loop system. Finally, this technique is employed to the tunnel diode circuit system, and a comparison example is given, which verifies the practicality and superiority of the method