Stochastic Event-Triggered Fault Detection and Isolation Based on Kalman Filter.

In this article, the robust fault detection and isolation (FDI) Kalman filter is extended based on stochastic event-triggered schedulers for discrete linear systems consisting of deterministic/stochastic unknown inputs with nonzero mean and colored measurement noise. In the proposed FDI method, first, a subspace of the main system that significantly attenuates disturbance effects is proposed. After that, the fusion method is proposed for dealing with the colored measurement-noise problem in designing the Kalman filter and preventing from leading to measurement noise with zero mean and zero covariance. The stochastic event-triggered schedulers are considered as Gaussian functions. Therefore, the Gaussian property of innovation sequence is preserved, and consequently, the recursive equation of the Kalman filter need not be extended based on approximation techniques. Finally, design parameters are chosen based on the convex optimization problem such that the lowest communication rate between the sensor node and FDI filter, and also the best FDI performance are achieved. The proposed FDI method is evaluated to detect and isolate stator interturn short circuit and broken rotor bars faults with unbalanced voltage as disturbance in three-phase induction motors.

A new approach for robust fault estimation in nonlinear systems with state-coupled disturbances using dissipativity theory.

The problem of fault estimation for nonlinear systems with Lipschitz nonlinearities is addressed in this work for the estimation of both the system fault and states. In the proposed approach disturbance is regarded to be a function which is nonlinear and coupled with states of the system, and fault to be a function which is additive. In order to diagnose the fault and reduce the disturbances effects by dissipativity theory, Luenberger and two unknown input observers (UIOs) are designed separately. If the system satisfies the matching condition, the first UIO can accurately estimate faults by decoupling the effects of state-coupled disturbances. Otherwise, the second UIO estimates faults by decoupling partial disturbances, and attenuating the disturbances which cannot be decoupled. The essential conditions for all designed observers to exist are stated. Finally, the suggested method is applied to a robot by simulation to analyse its performance.

Robust state estimation for a class of uncertain nonlinear systems: Comparison of two approaches.

In this paper, two approaches for robust state estimation of a class of Lipschitz nonlinear systems are proposed. First, a novel Unknown Input Observer (UIO) is designed without observer matching condition satisfaction. Then, an H∞ observer for approximate disturbance decoupling is proposed. Sufficient conditions for the existence of both proposed observers are derived based on a Lyapunov function. The achieved conditions are formulated in terms of a set of linear matrix inequalities (LMIs) and optimal gain matrices are obtained. The minimum values of the disturbance attenuation levels for both methods are obtained through solving optimization problems. Finally, the proposed approaches are compared by simulation studies of an automated highway system.

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks.

The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method.

Distributed observers for pose estimation in the presence of inertial sensory soft faults.

Distributed Particle-Kalman Filter based observers are designed in this paper for inertial sensors (gyroscope and accelerometer) soft faults (biases and drifts) and rigid body pose estimation. The observers fuse inertial sensors with Photogrammetric camera. Linear and angular accelerations as unknown inputs of velocity and attitude rate dynamics, respectively, along with sensory biases and drifts are modeled and augmented to the moving body state parameters. To reduce the complexity of the high dimensional and nonlinear model, the graph theoretic tearing technique (structural decomposition) is employed to decompose the system to smaller observable subsystems. Separate interacting observers are designed for the subsystems which are interacted through well-defined interfaces. Kalman Filters are employed for linear ones and a Modified Particle Filter for a nonlinear non-Gaussian subsystem which includes imperfect attitude rate dynamics is proposed. The main idea behind the proposed Modified Particle Filtering approach is to engage both system and measurement models in the particle generation process. Experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.

Minimum variance lower bound estimation and realization for desired structures.

The Minimum Variance Lower Bound (MVLB) represents the best achievable controller capability in a variance sense. Estimation and realization of MVLB for nonlinear systems confront some difficulties. Hence, almost all methods introduced so far estimate MVLB for a certain structure (e.g., NARMAX) or controller (e.g. PID). In this paper, MVLB for desired structures (not restricted to a certain type) is studied. The situation when the model is not in hand, is not accurate, or is not invertible has been considered. Moreover, in order to realize minimum variance controllers for nonlinear structures, a recursive model-free MVC design is utilized. Finally, a simulation study has been used to clarify the effectiveness of the proposed control scheme.

Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation.

Based on a cascaded Kalman-Particle Filtering, gyroscope drift and robot attitude estimation method is proposed in this paper. Due to noisy and erroneous measurements of MEMS gyroscope, it is combined with Photogrammetry based vision navigation scenario. Quaternions kinematics and robot angular velocity dynamics with augmented drift dynamics of gyroscope are employed as system state space model. Nonlinear attitude kinematics, drift and robot angular movement dynamics each in 3 dimensions result in a nonlinear high dimensional system. To reduce the complexity, we propose a decomposition of system to cascaded subsystems and then design separate cascaded observers. This design leads to an easier tuning and more precise debugging from the perspective of programming and such a setting is well suited for a cooperative modular system with noticeably reduced computation time. Kalman Filtering (KF) is employed for the linear and Gaussian subsystem consisting of angular velocity and drift dynamics together with gyroscope measurement. The estimated angular velocity is utilized as input of the second Particle Filtering (PF) based observer in two scenarios of stochastic and deterministic inputs. Simulation results are provided to show the efficiency of the proposed method. Moreover, the experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.

Optimal controller design using discrete linear model for a four tank benchmark process.

This paper deals with implementation of an optimal linear controller for a laboratory liquid four-tank system. A discrete linear model is used for modeling a class of MIMO nonlinear systems. It is shown that this model can identify these nonlinear systems with any desired accuracy, as a result the designed controller is accurate. The global stability condition is studied. The advantage of the proposed method against other controllers is its simplicity in design, there is no need for physical equations of the nonlinear system, and it is more accurate than other proposed linear methods. We present a step-by-step description of all steps leading towards the derivation and implementation of such a controller. The proposed method is tested on an experimental four tank benchmark process.

Novel system identification method and multi-objective-optimal multivariable disturbance observer for electric wheelchair.

Electric wheelchair (EW) is subject to diverse types of terrains and slopes, but also to occupants of various weights, which causes the EW to suffer from highly perturbed dynamics. A precise multivariable dynamics of the EW is obtained using Lagrange equations of motion which models effects of slopes as output-additive disturbances. A static pre-compensator is analytically devised which considerably decouples the EW's dynamics and also brings about a more accurate identification of the EW. The controller is designed with a disturbance-observer (DOB) two-degree-of-freedom architecture, which reduces sensitivity to the model uncertainties while enhancing rejection of the disturbances. Upon disturbance rejection, noise reduction, and robust stability of the control system, three fitness functions are presented by which the DOB is tuned using a multi-objective optimization (MOO) approach namely non-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable performance and robust stability of the proposed algorithm.

Designing a robust minimum variance controller using discrete slide mode controller approach.

Designing minimum variance controllers (MVC) for nonlinear systems is confronted with many difficulties. The methods able to identify MIMO nonlinear systems are scarce. Harsh control signals produced by MVC are among other disadvantages of this controller. Besides, MVC is not a robust controller. In this article, the Vector ARX (VARX) model is used for simultaneously modeling the system and disturbance in order to tackle these disadvantages. For ensuring the robustness of the control loop, the discrete slide mode controller design approach is used in designing MVC and generalized MVC (GMVC). The proposed method for controller design is tested on a nonlinear experimental Four-Tank benchmark process and is compared with nonlinear MVCs designed by neural networks. In spite of the simplicity of designing GMVCs for the VARX models with uncertainty, the results show that the proposed method is accurate and implementable.

Application of MIMO Disturbance Observer to Control of an Electric Wheelchair Using NSGA-II.

Electric wheelchairs (EW) experience various terrain surfaces and slopes as well as occupants with diverse weights. This, in turn, imparts a substantial amount of perturbation to the EW dynamics. In this paper, we make use of a two-degree-of-freedom control architecture called disturbance observer (DOB) which reduces sensitivity to model uncertainties, while enhancing rejection of disturbances caused due to entering slopes. The feedback loop which is designed via characteristic loci method is then augmented with a DOB with a parameterized low-pass filter. According to disturbance rejection, sensitivity reduction, and noise rejection of the whole controller, three performance indices are defined which enable us to pick the filter's optimal parameters using a multi-objective optimization approach called non-dominated sorting genetic algorithm-II. Finally, experimental results show desirable improvement in stiffness and disturbance rejection of the proposed controller as well as its robust stability.

Detection of broken rotor bars in induction motors using nonlinear Kalman filters.

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.