This paper investigates dynamic event-triggered finite-time adaptive consensus tracking control for high-power nonlinear multi-agent systems (MASs). Firstly, new error coordinate transformations are designed, which not only effectively separate local and neighbour information but also describe the properties of high-power nonlinear MASs. Secondly, by improving the adaptive technology using inequalities, the smoothness of the controller is enhanced, thereby avoiding singular value issues. By defining a new measurement of control error, named the adding power variable, and improving the trigger threshold, a dynamic event-triggered strategy is designed. This strategy reduces the frequency of data transmission in the network. The proposed control strategy ensures the consensus of MASs within a finite time, and the dynamic event-triggered mechanism prevents Zeno behaviour. Finally, simulation results demonstrate the effectiveness of the proposed control strategy.

This study analyzes the scientific production related to the theory of control in negative imaginary (NI) systems through a bibliometric analysis in the Web of Science (WoS) and Scopus databases. The main purpose is to evaluate the research output, identify key trends, and map the research landscape in NI systems. The significance of this analysis is rooted in the ability of NI system theory to improve the robust control of resonant and dynamic systems, making it applicable across various engineering disciplines. The methodology involves specific search configurations, data collection and unification (2004–2024), and cleaning and preprocessing tasks. A classification algorithm is implemented to compare and detect duplicate titles between both scientific databases, followed by a quantitative analysis and visualisation of results. Finally, the results are mapped and interpreted to identify key trends and patterns in the emerging areas of the NI system. The results indicate significant growth in scientific production, with applications ranging from nanotechnology to robotics. Advances in the characterisation and robustness of NI systems and data-driven control synthesis methods have paved the way for their adoption as solid theory in control systems. Recent extensions of non-linear systems underscore their innovative potential, versatility, and relevance in complex system control engineering.

In this paper, the fixed-time bipartite consensus of linear multi-agent systems (MASs) under event-triggered intermittent control (ETIC) is studied. In existing bipartite consensus, both cooperative and competitive relationships among agents are necessary. However, we focus on the scenario where all agents exhibit cooperative behaviour. Firstly, a new control protocol is devised by integrating the event-triggered strategy with the centralised sampled-data control (CSDC), and auxiliary functions are introduced to determine the protocol interval. Secondly, because the CSDC requires global information, to overcome this limitation, an improved ETIC embedded with distributed sampled-data control (DSDC) is proposed. Additionally, using Lyapunov stability theory and inequality methods, some sufficient conditions for fixed-time bipartite consensus of MASs are provided, while the Zeno phenomenon can be avoided. Ultimately, two numerical examples are employed to verify the theoretical conclusions.

In this paper, we are interested in how to achieve consensus among normal agents for a class of multi-agent systems (MASs) with malicious agents, in which the states of agents are determined by minimizing their cost functions. We first equip each agent with two states: desired state and behavior state, in which the behavior state is used to interact with the neighbors. Then, using the multi-round information filtering technique, we design a security fault-tolerant algorithm, and develop a sufficient topological condition that can ensure the consensus of the desired states among the normal agents. Finally, we provide a numerical example to demonstrate the effectiveness of the theoretical results.

This paper deals with the problem of admissibility analysis and control design for two-dimensional (2D) T–S fuzzy singular discrete systems described by the Roesser state space model with interval time-varying delays. A new LMIs condition is derived within the framework of linear matrix inequalities (LMIs) by constructing a newly singular augmented Lyapunov–Krasovskii functional and employing the auxiliary function-based summation inequalities combined with the reciprocally convex technique and Finsler lemma. The obtained LMIs conditions are then used to design a memory state feedback controller that ensures the admissibility of the closed-loop system. Finally, three numerical examples are provided to illustrate the effectiveness and merits of the proposed method.

This paper investigates the adaptive event-triggered stabilisation for a class of uncertain parabolic PDE-ODE systems. Remarkably, different from the related literature where uncertainties are severely constrained, more serious uncertainties are involved since both the system matrices in ODE subsystems are linearly parameterised by unknown constants while the diffusion coefficient and the reaction coefficient matrix in PDE subsystems are unknown. Such an ingredient leads to the incapability of the traditional methods. For this, a novel control strategy is proposed by a skilful combination of infinite-dimensional backstepping method, adaptive technique with the constructive method for dynamic event-triggered mechanism. Specifically, a couple of infinite-dimensional backstepping transformations are first introduced to change the original system into a new one. For the new system, an adaptive controller is explicitly designed joint with an event-triggered mechanism in which the updating law of a pivotal dynamic threshold is smartly chosen. Then, much effort has to be taken to show its stability, i.e. all the states of the resulting closed-loop system is bounded while the original system states converge to zero along with the avoidance of the infinitely fast sampling/execution. Finally, an example is given to validate the effectiveness of the proposed results.

This paper investigates the problem on tracking control for a medium-scale unmanned autonomous helicopter (UAH) under external disturbances and full state constraints. First, the nonlinear UAH model is divided into position and attitude subsystems, both of which incorporate the disturbance. Second, for each subsystem, a coordinated disturbance observer (CDO) is developed to accurately estimate the disturbance for compensation. Third, an improved log-type barrier Lyapunov function (LBLF) approach is employed to address the constraint issues on position, velocity, and attitude angle, while a new type of integral-type barrier Lyapunov function (IBLF) is offered to tackle the constraint on attitude angular rate. Fourth, by integrating disturbance estimation and tracking errors, two tracking controllers are presented for the position and attitude loops, resulting in the overall closed-loop system. Then, by utilizing the Lyapunov stability theory, a sufficient condition is established to ensure the uniform boundedness of all error signals. Finally, simulation results are presented to validate the effectiveness and advantage of our proposed control method.

Driven by the global pursuit of energy transition and carbon neutrality, the electric vehicle (EV) market has experienced exponential expansion in recent years. Nevertheless, the widespread adoption of EVs continues to be impeded by range anxiety–a phenomenon characterised by substantial heterogeneity arising from individual driver behaviours and contextual variability across travel scenarios. To address these challenges, this study investigates the planning of fast-charging stations using distributionally robust optimisation (DRO) approach with Wasserstein distance, aiming to minimise the aggregate range anxiety across transportation network while jointly considering path coverage and planning costs. A chance-constrained programming model is first established to dynamically model EV market penetration. Subsequently, data-driven nonparametric distributions within Wasserstein ambiguity sets are derived, allowing the original DRO problem to be reformulated into a tractable mixed-integer linear program via duality theory, thereby facilitating large-scale planning. The effectiveness of the proposed approach is demonstrated through a case study based on real-world traffic data from nine central urban districts of Chongqing, China. The model offers stronger application capability by avoiding the distribution assumptions of SP and the excessive robustness of RO, while endogenously modelling market penetration dynamics.