There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

Survey Research on gain scheduling

Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized path planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. The path planning algorithm retains the convergence properties of its kinematic counterparts. System safety is also addressed in the face of finite computation times by analyzing the behavior of the algorithm when the available onboard computation resources are limited, and the planning must be performed in real time. The proposed algorithm can be applied to vehicles whose dynamics are described either by ordinary differential equations or by higher-level, hybrid representations. Simulation examples involving a ground robot and a small autonomous helicopter are presented and discussed.

/pdf/real-time-motion-planning-for-agile-autonomous-vehicles-4l6obiq3kn.pdf

Real-Time Motion Planning for Agile Autonomous Vehicles

Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. Simulation examples involving a ground robot and a small autonomous helicopter, are presented and discussed.

/pdf/real-time-motion-planning-for-agile-autonomous-vehicles-4eeud9gl71.pdf

Real-time motion planning for agile autonomous vehicles

Procedures exist to rapidly accommodate certain types of faults, based on a priori specification of the postfault dynamics. A methodology is presented for accommodating the remaining unanticipated faults. For these two approaches, a tradeoff exists between the time to attain a solution to the reconfiguration problem and the generality of the approach. Unanticipated faults are represented as unmodeled forces and torques. Models of these forces and torques are developed online using a hybrid estimation/learning approach. The hybrid system is designed for fast estimation during the initial transient when a fault occurs, with continually improving performance as postfault information is accumulated by the learning system. Fault accommodation is achieved by a feedforward/feedback control architecture that employs an actuator distribution system to convert desired forces into individual actuator commands. This approach is demonstrated on a simulated autonomous vehicle, where the addition of a hybrid estimation/learning capability is shown to increase performance greatly over time. >

Using learning techniques to accommodate unanticipated faults

We propose an alternative to gain scheduling for stabilization of nonlinear systems. For a useful class of nonlinear systems, the characterization of a region of stability based on a control Lyapunov function is computationally tractable, in the sense that computation times vary polynomially with the state dimension for a fixed number of scheduling variables. Using this fact, we develop a procedure to expand the region of stability by constructing control Lyapunov functions to various trim points of the system. A Lyapunov-based control synthesis algorithm is used to construct a control law that guarantees closed-loop stability for initial conditions in the expanded region of state space. This control asymptotically recovers the optimal stability margin in the sense of a Lyapunov derivative, which in turn can be seen as a performance measure. Robustness to bounded disturbances and stabilization under bounded control are easily incorporated into this framework. In the worst case, the computational complexity of the analysis problem that develops in the new method is increased by an exponential in the disturbance dimension. Similarly, we can handle control constraints with an increase in computational complexity of no more than an exponential in the control dimension. We demonstrate the new control design procedure on an example.

A computationally efficient Lyapunov-based scheduling procedure for control of nonlinear systems with stability guarantees

A robust failure detection and isolation (RFDI) algorithm for linear dynamic systems that is insensitive to failure mode, noise, and plant model uncertainties is presented. For robustness to failure mode uncertainties, the algorithm assumes that the failure is the output of a shaping filter, or specifically, a Gauss-Markov model. Such a model embraces a large class of failures. For robustness to noise and plant model uncertainties, the algorithm relies on either a robust risk sensitive (exponential Gaussian) or a robust H/sub /spl infin// filter for the synthesis of failure residuals. It is shown that the algorithm is a generalization of likelihood ratio tests for failure detection and isolation in dynamic systems. Numerical results are presented for an underwater vehicle application, demonstrating that the RFDI algorithm can provide good performance over the desired range of model uncertainties. By contrast, the performance of the nominally optimal likelihood ratio test can considerably degrade in the presence of the same uncertainties.

https://ieeexplore.ieee.org/iel2/3470/10222/00480694.pdf

A robust failure detection and isolation algorithm

A system in accordance with the present invention tasks a team of autonomous unmanned vehicles. The system includes a first team member and a second team member. The first team member has a first level of autonomy. The second team member has a second level of autonomy. The first level of autonomy is different than the first level of autonomy. The first team member is given instructions corresponding to the first level of autonomy. The second team member is given instructions corresponding to the second level of autonomy.

Mission planning system for vehicles with varying levels of autonomy

Modeling errors present a significant and difficult challenge in the design of analytic fault detection mechanisms. The authors discuss the sensitivity to model uncertainty of estimator-based failure detection techniques. In particular, they discuss desired statistical properties for the decision variable, and why these characteristics are difficult to achieve in situations involving significant uncertainty in the noise, fault, or plant dynamic modeling assumptions. This discussion motivates the use of robust estimation techniques in failure detection. An aircraft example is presented to illustrate the effect of modeling error on the failure detection performance of detection test designs based on a Kalman filter and an H/sub infinity // mu estimator. >

http://ieeexplore.ieee.org/iel5/1040/8509/00371378.pdf

Brent Appleby

Papers

Using learning techniques to accommodate unanticipated faults

A computationally efficient Lyapunov-based scheduling procedure for control of nonlinear systems with stability guarantees

A robust failure detection and isolation algorithm

Mission planning system for vehicles with varying levels of autonomy

Robust estimation in fault detection