Seyed Mojtaba Tabatabaeipour

Robust observer-based fault estimation and accommodation of discrete-time piecewise linear systems

Abstract In this paper a new integrated observer-based fault estimation and accommodation strategy for discrete-time piecewise linear (PWL) systems subject to actuator faults is proposed. A robust estimator is designed to simultaneously estimate the state of the system and the actuator fault. Then, the estimate of fault is used to compensate for the effect of the fault. By using the estimate of fault and the states, a fault tolerant controller using a PWL state feedback is designed. The observer-based fault-tolerant controller is obtained by the interconnection of the estimator and the state feedback controller. We show that separate design of the state feedback and the estimator results in the stability of the overall closed-loop system. In addition, the input-to-state stability (ISS) gain for the closed-loop system is obtained and a procedure for minimizing it is given. All of the design conditions are formulated in terms of linear matrix inequalities (LMI) which can be solved efficiently. Also, performance of the estimator and the state feedback controller are minimized by solving convex optimization problems. The efficiency of the method is demonstrated by means of a numerical example.

A Set-Valued Approach to FDI and FTC of Wind Turbines

A complete methodology to design robust fault detection and isolation (FDI) filters and fault-tolerant control (FTC) schemes for linear parameter varying systems is proposed, with particular focus on its applicability to wind turbines. This paper takes advantage of the recent advances in model falsification using set-valued observers (SVOs) that led to the development of FDI methods for uncertain linear time-varying systems, with promising results in terms of the time required to diagnose faults. An integration of such SVO-based FDI methods with robust control synthesis is described, to deploy new FTC algorithms that are able to stabilize the plant under faulty environments. The FDI and FTC algorithms are assessed by resorting to a publicly available wind turbine benchmark model, using Monte Carlo simulation runs.

Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed. In set-membership approaches, instead of a point-wise estimation of the states, a set-valued estimation of them is computed. If this set becomes empty the given model of the system is not consistent with the measurements. Therefore, the model is falsified. When more than one model of the system remains un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved by a number of linear and quadratic programming problems, which result in a computationally efficient algorithm. The method is tested on a numerical example as well as on the pitch actuator of a benchmark wind turbine.

Passive fault-tolerant control of discrete time piecewise affine systems against actuator faults

In this article, we propose a new method for passive fault-tolerant control of discrete time piecewise affine systems. Actuator faults are considered. A reliable piecewise linear quadratic regulator state feedback is designed such that it can tolerate actuator faults. A sufficient condition for the existence of a passive fault-tolerant controller is derived and formulated as the feasibility of a set of linear matrix inequalities (LMIs). The upper bound on the performance cost can be minimised using a convex optimisation problem with LMI constraints which can be solved efficiently. The approach is illustrated on a numerical example and a two degree of freedom helicopter.

Fault Detection and Isolation and Fault Tolerant Control of Wind Turbines using Set-Valued Observers

Abstract Research on wind turbine Operations & Maintenance (O&M) procedures is critical to the expansion of Wind Energy Conversion systems (WEC). In order to reduce O&M costs and increase the lifespan of the turbine, we study the application of Set-Valued Observers (SVO) to the problem of Fault Detection and Isolation (FDI) and Fault Tolerant Control (FTC) of wind turbines, by taking advantage of the recent advances in SVO theory for model invalidation. A simple wind turbine model is presented along with possible faulty scenarios. The FDI algorithm is built on top of the described model, taking into account process disturbances, uncertainty and sensor noise. The FTC strategy takes advantage of the proposed FDI algorithm, enabling the controller reconfiguration shortly after fault events. Additionally, a robust controller is designed so as to increase the wind turbines performance during low severity faults. Finally, the FDI algorithm is assessed within a publicly available benchmark model, using Monte-Carlo simulation runs.

A Set-Valued Approach to FDI and FTC: Theory and Implementation Issues

Abstract A complete methodology to design robust Fault Detection and Isolation (FDI) filters and Fault Tolerant Control (FTC) schemes for Linear Time-Varying (LTV) systems is proposed. The paper takes advantage of the recent advances in model invalidation using Set-Valued Observers (SVOs) that led to the development of FDI methods for uncertain linear time-varying systems, with promising results in terms of the time required to diagnose faults. An integration of such SVO-based FDI methods with robust control synthesis is described, in order to deploy new FTC algorithms that are able to stabilize the plant under faulty environments. The FDI algorithm is assessed within a wind turbine benchmark model, using Monte-Carlo simulation runs.

Control Reconfiguration of LPV Systems Using Virtual Sensor and Virtual Actuator

Abstract In this paper, a fault tolerant control method for linear parameter varying (LPV) systems using a virtual actuator and a virtual sensor is proposed. The basic idea of the method is to insert a reconfiguration block, which consists of an LPV virtual actuator and an LPV virtual sensor, between the plant and the nominal controller such that the fault tolerant goal is achieved without re-designing the nominal controller. The role of the reconfiguration block is to transform the signals from the faulty system such that its behavior is similar to the nominal system from the point of view of the controller and to transform the output of the controller for the faulty system such that the stability and performance goals are preserved. In this paper, we consider the weak fault-hiding goal and stability of the closed loop system. Input to state stabilizing LPV gains of the virtual actuator and sensor are found by solving linear matrix inequalities (LMIs). We show that separate design of these gains guarantees the closed loop input to state stability of the closed loop reconfigured system.

Wave disturbance reduction of a floating wind turbine using a reference model-based predictive control

Floating wind turbines are considered as a new and promising solution for reaching higher wind resources beyond the water depth restriction of monopile wind turbines. But on a floating structure, the wave-induced loads significantly increase the oscillations of the structure. Furthermore, using a controller designed for an onshore wind turbine yields instability in the fore-aft rotation. In this paper, we propose a general framework, where a reference model models the desired closed-loop behavior of the system. Model predictive control combined with a state estimator finds the optimal rotor blade pitch such that the state trajectories of the controlled system tracks the reference trajectories. The framework is demonstrated with a reference model of the desired closed-loop system undisturbed by the incident waves. This allows the wave-induced motion of the platform to be damped significantly compared to a baseline floating wind turbine controller at the cost of more pitch action.

Active Fault Diagnosis of Linear Hybrid Systems

Abstract A method for active fault diagnosis of linear discrete time hybrid systems is presented. The algorithm generates appropriate test signals that can be used for sanity check during system commissioning or later in the normal phase to detect faults which are impossible to detect by means of passive diagnosis methods because of regulatory actions of the controller. The algorithm is illustrated on a two tank benchmark example.

Fault detection of a benchmark wind turbine using interval analysis

This paper investigates a state estimation set-membership approach for fault detection of a benchmark wind turbine. The main challenges in the benchmark are high noise on the wind speed measurement and the nonlinearities in the aerodynamic torque such that the overall model of the turbine is nonlinear. We use an effective wind speed estimator to estimate the effective wind speed and then using interval analysis and monotonicity of the aerodynamic torque with respect to the effective wind speed, we can apply the method to the nonlinear system. The fault detection algorithm checks the consistency of the measurement with a closed set that is computed based on the past measurements and a model of the system. If the measurement is not consistent with this set, a fault is detected. The result demonstrates effectiveness of the method for fault detection of the benchmark wind turbine.