Detecting sensor faults in automotive systems is vital to ensure the safe operation and reliability of vehicle controllers. The aim is to detect faults reliably and quickly while avoiding false positives. In particular, the road parameters in the form of banking angle and friction coefficient strongly influence the fault estimation. This work presents a framework for the reliable detection of sensor faults in the vehicle's Inertial Measurement Unit (IMU) up to the driving dynamics limit range. The unknown road parameters are estimated in parallel and enable the use of the proposed method under varying environmental conditions. The estimation of the vehicle states, the coefficient of friction and the sensor faults is carried out using a Two-Stage Unscented Kalman Filter with a nonlinear vehicle model. The estimation of the road inclination and banking angle is carried out using a separate Extended Kalman Filter. Measurements with an Audi SQ8 e-tron under high and low friction condition are presented and show a fault detection time of 100 ms while avoiding false-positive detections for quasi steady state friction conditions. Rapid variations in the coefficient of friction lead to a slight degradation in fault estimation performance.
In the control and operation of power inverters, proportional-resonant (PR) controllers are used to track references and reject disturbances that can be described by a sum of sinusoidal signals of known frequency. The relevance of these controllers is increasing due to the increase in distortion and volatility of three-phase signals in the grid. However, as reported in the literature, the tuning of PR controllers is far from trivial due to their large number of parameters and the presence of pure imaginary poles in the associated transfer function. To address these issues, a time-domain framework based on linear matrix inequalities (LMIs) is presented for the tuning of PR controllers with applications to voltage and current tracking. The advantage of this approach is the straightforward combination with other techniques such as H-infinity control. The effectiveness of the approach is illustrated through numerical simulations, where the injection of a constant active power is achieved in the presence of distorted voltages.
This article tackles the problem of designing ratelimiting elements, commonly found in feedback loops and used to filter signals before passing them to actuators. Under certain circumstances, these elements can introduce a phase delay, which can ultimately cause instability. For this reason, techniques to understand, analyse, and predict instability due to their phase loss have been proposed. Techniques to limit the rate of change of a signal without introducing the significant phase loss of a standard rate limiter have also been studied. However, said schemes have one or multiple of the following problems: the introduction of bias, the necessity to know the internal signals of actuators, difficult parameter tuning/interpretation, and the need to solve online optimisation problems. This study provides an alternative rate-limiting element that is competitive with state-of-the-art methods in phase-matching performance but avoids the mentioned drawbacks. The newly introduced phase anticipation scheme has been tested in step, sinusoidal, and mixed regimes. It proved able to recover up to 65 % of the lost phase without introducing any significant downside. To illustrate its performance in a concrete application, we show its effectiveness in avoiding phase loss-induced limit cycles in an aircraft ground handling task in a high-fidelity simulator.
This paper studies the resilient consensus of a multi-agent system in the presence of an external source and misbehaving agents. A dynamic model is considered that integrates an external signal from the source to the weighted-mean-subsequence-reduced (W-MSR) based algorithm. Graphtheoretic conditions for resilient consensus are derived based on a new notion of tier-$k$ conformity, ensuring that the consensus value remains independent of the misbehaving agents and is determined by the external signal. For the special case where all agents receive the external signal, exponential convergence to the consensus value is proven. Numerical examples are included to illustrate and validate the theoretical findings.
This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without requiring prior knowledge of system drift dynamics. Finally, the proposed method is validated through a real-time digital simulator (RTDS) with a standard power system representation.
