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This paper addresses the challenge of robust simultaneous estimation of state and actuator faults for Lipschitz nonlinear systems with unknown perturbations acting on both the state dynamics and output measurements. The existing methods enhance the estimation robustness by suppressing the perturbations or decoupling them under satisfaction of the matching condition. This work considers a total elimination of the perturbation effects through perturbation reconstruction. An adaptive sliding mode unknown input observer (ASMUIO) is developed to realize simultaneous asymptotic estimation of the state, faults and perturbations. It leverages a descriptor system reformulation of the original system by regarding partial perturbations as virtual state and the rest part as virtual faults. The proposed ASMUIO has feasibility guarantee and is solved via a linear matrix inequality (LMI) setting. Based on the ASMUIO, an adaptive backstepping fault-tolerant control (FTC) is further designed to achieve good tracking performance. The design efficacy is illustrated through comparative simulations of the proposed method against representative methods in the literature.

Vehicle platooning gains its popularity in improving traffic capacity, safety and fuel saving. The key requirements of an effective platooning strategy include keeping a safe inter-vehicle space, ensuring string stability and satisfying vehicular constraints. To meet these requirements, this paper proposes a distributed min-max model predictive control (MPC). One technical contribution is that the proposed MPC can guarantee input-to-state predecessor-follower string stability, in the presence of vehicle-to-vehicle communication delays and realistic constraints. Another technical contribution is the development of a new concept of input-to-state stability margin for analyzing the platooning system that is nonlinear under MPC. The proposed MPC is applicable to both homogeneous and heterogeneous platoons because only the point-mass vehicle model is needed. The proposed MPC also has reduced communication burden because each vehicle in the platoon only transmits its current acceleration to the adjacent follower. The design efficacy is verified by simulating a platoon composed of five vehicles under different uncertainties and communication delays.

This paper investigates cooperative adaptive cruise control (CACC) for mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). This research is critical because the penetration rate of AVs in the transportation system will remain unsaturated for a long time. Uncertainties and randomness are prevalent in human driving behaviours and highly affect the platoon safety and stability, which need to be considered in the CACC design. A further challenge is the difficulty to know the exact models of the HVs and the exact powertrain parameters of both AVs and HVs. To address these challenges, this paper proposes a data-driven model predictive control (MPC) that does not need the exact models of HVs or powertrain parameters. The MPC design adopts the technique of data-driven reachability to predict the future trajectory of the mixed platoon within a given horizon based on noisy vehicle measurements. Compared to the classic adaptive cruise control (ACC) and existing data-driven adaptive dynamic programming (ADP), the proposed MPC ensures satisfaction of constraints such as acceleration limit and safe inter-vehicular gap. With this salient feature, the proposed MPC has provably guarantee in establishing a safe and robustly stable mixed platoon despite of the velocity changes of the leading vehicle. The efficacy and advantage of the proposed MPC are verified through comparison with the classic ACC and data-driven ADP methods on both small and large mixed platoons.

This paper proposes a less-disturbed ecological driving strategy for connected and automated vehicles (CAVs). The proposed strategy integrates the offline planning and the online tracking. In offline planning, an energy efficient reference speed is created based on traffic information (such as the average traffic speed) and characteristics of the vehicle (such as the engine efficiency map) via dynamic programming. The consideration of average traffic speed in speed planning avoids selfish optimisations. In online tracking, model predictive control is employed to update the vehicle speed in real-time to track the reference speed. A key challenge in applying ecological driving strategies is that the vehicle has to consider other traffic participants when tracking the reference speed. Therefore, this paper combines both longitudinal and lateral control to achieve better speed tracking by overtaking the preceding vehicle when necessary. The proposed less-disturbed ecological driving strategy has been evaluated in simulations in both single road segment scenario and real traffic environment. Comparisons of the proposed method with benchmark strategies and human drivers are made. The results demonstrate that the proposed strategy is more effective in energy saving. Compared to human drivers, the less-disturbed eco-driving strategy improves the fuel efficiency of CAVs by 4.53%.

Cooperative adaptive cruise control (CACC) has attracted much research attention, due to its great potential in improving traffic throughput, safety and energy efficiency. This paper aims to address the following problems that are rarely investigated in the literature: (i) the time delay caused by online computation of the optimal control action, and (ii) robustly stable CACC under variable road geometry. To this end, a one-step ahead robust model predictive control (MPC) is developed for achieving CACC and lane keeping (LK) of the followers in the platoon, by leveraging vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. In the proposed design, the current MPC policy is generated one-step ahead during the previous sampling period to avoids the optimization-induced time delay existing in the traditional MPC. LK control is incorporated with CACC to ensure vehicle lateral stability and vehicle following under variable road geometry. The MPC design is formulated as an easily solved linear matrix inequality (LMI) optimization problem with consideration of the control input limits and constraints on platooning errors and lateral displacement. Effectiveness of the proposed MPC and its advantages over the traditional MPC are verified by simulating a vehicle platoon on roads with time-varying bank, curvature and grade.

This chapter presents an adaptive decoupling strategy to separate the FE and FTC designs. An adaptive sliding mode augmented state unknown input observer is designed to actively estimate the perturbations together with the system state and faults. The effects of estimation errors and perturbations are compensated together with the faults using an adaptive backstepping controller. This chapter considers a more general class of actuator faults that can be differentiable or non-differentiable, and matched or unmatched.

This chapter presents a sequential strategy for robust integration of FE and FTC. An augmented Luenberger state observer is used to estimate the state and faults, and a state feedback FTC controller is used to compensate the faults and stabilize the system. Under this scheme, the FTC controller is designed in the first step and used in the second step to determine the FE observer.

This chapter provides a fault-tolerant wind turbine pitch control example as a simple guide for designing FE-based FTC systems. A new FE observer is also developed for reconstructing the real shapes of multiplicative (component) faults. Simulations are performed on a 4.8 MW benchmark wind turbine with single or multiple pitch actuator faults.

This chapter extends the simultaneous integration strategy in Chap. 5 for large-scale interconnected systems subject to uncertain nonlinear interconnections. Decentralized FE and FTC strategies are developed for large-scale interconnected systems under actuator or sensor faults. The designs are validated on a 3-machine power system with actuator or sensor faults.