ABSTRACT With the presence of external disturbance and moving targets, how to establish new predefined time stability criteria to achieve prescribed performance under varying sea states is a challenging problem. A new predefined‐time stability lemma is introduced through employing an exponentially decaying function. It has more adjustable parameters for the size of residual set, and can enter a smaller residual set. Subsequently, according to this lemma, a practical predefined‐time prescribed performance control for cooperative target enclosing of unmanned surface vehicles is proposed. A predefined‐time observer is designed to compensate for uncertain parameters and disturbances. Finally, experimental results validate that this control scheme reduces tracking errors when compared to existing methods, and demonstrates robustness against external disturbances.
ABSTRACT This paper addresses the adaptive tracking control problem for a class of uncertain nonlinear systems. Conventional approximation‐based control methods usually employ fuzzy logic systems (FLSs) or neural networks to directly approximate unknown nonlinear functions. However, the resulting approximation error is highly dependent on the selection of basis‐function parameters, which are often empirically chosen and difficult to characterize precisely. This limitation may degrade the approximation capability and consequently affect the tracking performance. To overcome this issue, the states of the original system are augmented, and a fuzzy state observer is developed to construct an indirect fuzzy approximator for the unknown nonlinear functions. In the proposed framework, the approximation error is equivalently represented by the fuzzy observation error, which can be explicitly regulated through the observer gains. Based on the proposed indirect fuzzy approximator and an adaptive design strategy, an indirect‐approximator‐based adaptive tracking controller is developed. Compared with traditional direct approximation‐based methods, the proposed controller can achieve improved tracking performance by driving the tracking error into a smaller residual set. The stability of the closed‐loop system and the convergence property of the tracking error are rigorously analysed. Finally, simulation and experimental results are provided to demonstrate the effectiveness and superiority of the proposed method.
ABSTRACT Thunderstorm gale forecasting can be formulated as a competitive multi‐agent spatio‐temporal decision problem, where meteorological stations, radar grid cells and convective regions interact under nonlinear, time‐varying and uncertain atmospheric conditions. To address this problem, this paper proposes a data‐driven competitive multi‐agent forecasting and fuzzy decision‐control framework for thunderstorm gale warning. First, a dynamic spatio‐temporal graph convolutional forecasting model, termed DSTGFP, is developed for multi‐station wind speed prediction. By integrating dynamic time warping, mutual information and geographical proximity, DSTGFP constructs an adaptive dynamic adjacency matrix to identify competition–cooperation interactions among station agents. Multi‐head attention and multi‐scale time–frequency feature extraction further allocate representation weights across heterogeneous spatio‐temporal patterns. Second, a multi‐source spatio‐temporal attention network, termed MSTA‐UNet, is constructed for radar echo extrapolation by fusing radar reflectivity, atmospheric dynamic variables and static terrain constraints. Finally, Kriging interpolation and fuzzy logic are introduced to construct a joint thunderstorm gale potential index, where wind‐speed and radar‐reflectivity risks are treated as soft competitive decision constraints. Experimental results show that DSTGFP achieves the best wind‐speed forecasting performance. The overall framework achieves a CSI of 0.5641 and a POD of 0.6471, demonstrating its effectiveness in modelling, analysing and controlling competitive multi‐agent interactions for severe convective weather forecasting, together with a FAR of 0.1852
This paper presents a combination of communication, estimation and control techniques for path-following of a radio-controlled (RC) car. The proposed approach integrates: i) a periodic adaptive event-triggered communication (PAETC) mechanism that regulates sensor-to-controller updates according to vehicle dynamics, ii) a non-uniform extended Kalman filter (NUEKF) for state estimation from an irregular measurement signal and iii) a pure pursuit controller (PPC) for path-following with straightforward control action generation. Unlike traditional time-triggered communication, and periodic (static) event-triggered strategies, the PAETC mechanism further reduces resource usage by transmitting measurements only when some dynamic thresholds are exceeded. Interestingly, dual-rate sampling is also integrated to address some sensor limitations that lead to a slower working period, resulting in a further decrease in the number of transmissions. The NUEKF is able to provide the PPC with the estimates required to generate faster control actions and achieve satisfactory control performance. To accommodate a slower communication rate, the control signal is sent using a packet-based control mechanism. A complex, realistic model of the RC car is implemented in Simscape Multibody. The model has been experimentally validated using a high-fidelity platform such as Optitrack. The comprehensive approach leads to an excellent trade-off between resource savings and path-following behaviour, outperforming traditional time-triggered and periodic event-triggered mechanisms.
