Adaptive and Robust Fault-Tolerant Tracking Control of Contact force of Pantograph-Catenary for High-Speed Trains

Abstract This paper presents a modified multi-body dynamic model and a linear time-invariant model with actuator faults (loss of effectiveness faults, bias faults) and matched and unmatched uncertainties. Based on the fault model, a class of adaptive and robust tracking controllers are proposed which are adjusted online to tolerate the time-varying loss of effectiveness faults and bias faults, and compensate matched disturbances without the knowledge of bounds. For unmatched uncertainties, optimal control theory is added to the controller design processes. Simulations on a pantograph are shown to verify the efficiency of the proposed fault-tolerant design approach

Adaptive Fault-Tolerant Formation Control for Quadrotors with Actuator Faults

In this paper, we investigate the fault-tolerant formation control of a group of quadrotor aircrafts with a leader. Continuous fault-tolerant formation control protocol is constructed by using adaptive updating mechanism and boundary layer theory to compensate actuator fault. Results show that the desired formation pattern and trajectory under actuator fault can be achieved using the proposed fault-tolerant formation control. A simulation is conducted to illustrate the effectiveness of the method

Adaptive Actuator Compensation of Position Tracking for High-Speed Trains with Disturbances

In this paper, the adaptive fault compensation prob-lem is investigated for high-speed trains in the presence of time-varying system parameters, disturbances and actuator failures. To deal with the time-varying system parameters, a new time-varying indicator function instead of commonly used 0-1 function, is proposed to model the train dynamics as a piecewise model with unparameterizable time-varying disturbances, which can cover more time variations and help parametrization for adaptation. A backstepping adaptive controller is designed for the healthy system with unknown piecewise model parameters and known piecewise bounds on disturbances. For both the parameterizable and unparameterizable failures, the backstepping adaptive fail-ure compensation with the adaptive laws are derived to achieve the position tracking under the known bound disturbances. The adaptive failure compensation for unknown bounds on disturbances is also discussed under the parameterizable failure. Through introducing the nonlinear damping in the proposed controller, the failure compensation controller is proposed for the model with unparameterizable system parameters to achieve an arbitrary degree of position tracking accuracy. The stability of the corresponding closed-loop system and asymptotic state tracking are proved via Lyapunov direct method, and validated using a high-speed train model

Adaptive Control Design and Evaluation for Multibody High-speed Train Dynamic Models

In this paper, the adaptive tracking control problem is investigated for multibody high-speed train dynamic model in the presence of unknown parameters, which is an open adaptive control problem. A 4-car train unit model with input signals acting on the 2nd and 3rd cars and output signals being the speeds of the 1st and 3rd cars is chosen as a benchmark model, in which the aerodynamic resistance force is also considered. To handel the nonlinear term, the feedback linearization method is employed to decompose the system into a control dynamics subsystem and a zero dynamics subsystem. A new and detailed stability analysis is conducted to show that such a zero dynamic system is Lyapunov stable and is also partially input-to-state stable under the condition that the speed error between the 1st and 3rd cars is exponentially convergent (to be ensured by a nominal controller) or belongs to the L1 signal space (to be achieved by a properly designed adaptive controller). The system configuration leads to a relative degree 1 subsystem and a relative degree 2 subsystem, for which different feedback linearization-based adaptive controllers and their nominal versions are developed to ensure the needed stabilization condition, the desired closed-loop system signal boundedness and asymptotic output speed tracking. Detailed closed-loop system stability and tracking performance analysis are given for the new control schemes. Simulation results from a realistic train dynamic model are presented to verify the desired adaptive control system performance

Robust Sliding Mode Observers for Large Scale Systems with Application to a Multimachine Power System

In this paper, a class of interconnected systems with structured and unstructured uncertainties is considered where the known interconnections and uncertain interconnections are nonlinear. The bounds on the uncertainties are employed in the observer design to enhance the robustness when the structure of the uncertainties is available for design. Under the condition that the structure distribution matrices of the uncertainties are known, a robust sliding mode observer is designed and a set of sufficient conditions is developed to guarantee that the error dynamics are asymptotically stable. In the case that the structure of uncertainties is unknown, an ultimately bounded approximate observer is developed to estimate the system states using sliding mode techniques. The results obtained are applied to a multimachine power system, and simulation for a two machine power system is presented to demonstrate the feasibility and effectiveness of the developed methods

Robust sliding mode observer design for interconnected systems

In this paper, a class of nonlinear interconnected systems is considered in the presence of structured and unstructured uncertainties. The bounds on the uncertainties are nonlinear and are employed in the observer design to reject the effect of the uncertainties. Under the condition that the structure matrices of the uncertainties are known, a robust sliding mode observer is designed and a set of sufficient conditions is developed such that the error dynamics are asymptotically stable. If the structure of the uncertainties is unknown, an ultimately bounded observer is developed using sliding mode techniques. The obtained results are applied to a multimachine power system to demonstrate the effectiveness of the developed methods

Fault-Tolerant Control for Systems with Unmatched Actuator Faults and Disturbances

A fault-tolerant control (FTC) scheme for a class of nonlinear systems with unmatched actuator redundancy and unmatched disturbances is proposed in this note. A methodology to construct unified smooth sliding mode control laws and update laws is proposed such that the equivalent injections of the first-order time derivatives of the unmatched actuator faults and unmatched disturbances can appear in the unmatched channels. The unmatched actuator faults and unmatched disturbances are completely canceled by these equivalent injections. Based on this methodology and using the backstepping design procedure, a set of smooth FTC sliding surfaces, FTC laws and update laws are then designed. With the help of the FTC law selecting mechanism, the output tracking errors of the closed-loop FTC system converge to zero asymptotically, and time-varying faults and disturbances are reconstructed. A simulation example is presented to illustrate the effectiveness of the proposed FTC method

Symphonize 3D Semantic Scene Completion with Contextual Instance Queries

3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal task for autonomous driving, as it involves predicting per-voxel occupancy within a 3D scene from partial LiDAR or image inputs. Existing methods primarily focus on the voxel-wise feature aggregation, while neglecting the instance-centric semantics and broader context. In this paper, we present a novel paradigm termed Symphonies (Scene-from-Insts) for SSC, which completes the scene volume from a sparse set of instance queries derived from the input with context awareness. By incorporating the queries as the instance feature representations within the scene, Symphonies dynamically encodes the instance-centric semantics to interact with the image and volume features while avoiding the dense voxel-wise modeling. Simultaneously, it orchestrates a more comprehensive understanding of the scenario by capturing context throughout the entire scene, contributing to alleviating the geometric ambiguity derived from occlusion and perspective errors. Symphonies achieves a state-of-the-art result of 13.02 mIoU on the challenging SemanticKITTI dataset, outperforming existing methods and showcasing the promising advancements of the paradigm. The code is available at \url{https://github.com/hustvl/Symphonies}.Comment: Technical report. Code and models at: https://github.com/hustvl/Symphonie