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This paper considers distributed multi-objective optimisation problems with time-varying cost functions for network connected multi-agent systems over switching graphs. The scalarisation approach is used to convert the problem into a weighted-sum objective. Fixed-time consensus algorithms are developed for each agent to estimate the global variables, and drive all local copies of the decision vector to a consensus. The algorithm with fixed gains is first proposed, where some global information is required to choose the gains. Then, an adaptive algorithm is presented to eliminate the use of global information. The convergence of those algorithms to the Pareto solutions is established via Lyapunov theory for connected graphs. In case of disconnected graphs, the convergence to the subsets of the Pareto fronts is studied. Simulation results are provided to demonstrate the effectiveness of the proposed algorithms.

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Due to the long-distance transmission, external grid cannot effectively support the access point voltage (APV) of the large-scale wind farm (WF), which thus entails DFIGbased WF to provide its ancillary services on reactive power generation. Considering factors of limited down-sized converter capacity, various operating modes of wind turbines (WTs), and the large pressure of large-scale WF communication on the central governor, this paper proposes a distributed active power and reactive power coordination scheme for APV support. A reactive power self-allocation scheme is developed and operated in each WT for reactive power dispatch. Based on the mechanism of distributed consensus control and aligning with the principle of preferential and proportional utilization of spare reactive power capacity (RPC), the proposed method collaborates the rotor side converters and the grid side converters of all WTs but without the necessity of knowing the total RPC. Further, in case that the total RPC is insufficient for the demand, a coordinating factor for RPC extension is generated to weaken the active power. Through a current-enforced PQ coordinating loop, it guarantees a high utilization of converter capacity as well as an exact compensation on the reactive power imbalance, which thereby reduces the wind power curtailment for the RPC release. Several case studies verify the effectiveness of the proposed method.

Time-delay control (TDC) is widely recognized as a robust and straightforward model-free control approach for complex systems. However, the transient performance and settling time are often given less consideration in most TDC-based controllers. In this article, we propose an integrated control protocol that combines fixedtime prescribed performance control with time-delay estimation techniques for autonomous ground vehicles. The proposed control paradigm offers the advantages of being model-free while ensuring that the preview error converges to a neighborhood of zero within a fixed time, adhering to predefined constraint functions. To overcome the limitations of commonly used exponential decay boundaries, a prescribed performance function that remains independent of the initial conditions is employed. Furthermore, a highorder model-free fixed-time differentiator is constructed to observe the high-order dynamics of the preview error, which are essential for estimating unknown model dynamics. Finally, the simulations and practical experiments have been conducted to demonstrate the superiority of our proposed control protocol. Index Terms-Fixed-time convergence, model free, pathfollowing control, prescribed performance control (PPC), time-delay control (TDC). NOMENCLATURE δ fFront-wheel steering angle. β/γSideslip angle/yaw rate of vehicle.

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