IET Control Theory and Applications最新文献

This paper investigates the dynamic cluster consensus problem for second-order multi-agent leader–follower systems operating under directed communication topologies. Focusing on two scenarios, including single-chain leadership systems and multi-connection leadership systems, the study constructs a three-layer cooperative architecture that decouples the system into a leader layer, an intermediate follower layer, and a bottom follower layer. A consensus control protocol is then designed based on a hierarchical dynamic clustering strategy. For the single-chain system, a dynamic adjustment mechanism is proposed for the bottom-layer topology, and this mechanism utilizes a composite position-velocity error to achieve adaptive optimization of the communication links among neighbouring followers. For the multi-connection system, the method is extended to include dynamic adjustment of the leader–follower communication topology and an anchoring mechanism, thereby resolving issues of cluster partitioning and leader isolation. Furthermore, the design of a distributed control protocol with weight adjustment and an anti-ambiguity mechanism ensures that the system can form stable multi-cluster structures, achieve uniform convergence of states and velocities within each cluster, and maintain communication isolation between clusters. The theoretical analysis, grounded in graph theory and Lyapunov stability theory, proves the convergence of the system under weaker assumptions, while simulation results verify the effectiveness of the proposed method.

In this paper, a flight control scheme utilizing a fully actuated auxiliary system is investigated to improve the manoeuvring flight performance and safety of manoeuvrability-limited unmanned autonomous helicopters (UAHs). A saturation-like function is employed to approximate state constraints. A dedicated fully actuated auxiliary system is integrated into the control framework to address the detrimental impacts induced by input saturation and state constraints. The control architecture is developed through a backstepping methodology. The stability of the closed-loop system is verified by Lyapunov stability analysis. Simulation results demonstrate that the proposed control scheme effectively resolves the flight control challenges associated with manoeuvrability limitations in UAHs and achieves superior tracking performance under the presence of state constraints and input saturation.

This paper investigates the error accumulation problem in centralized training and decentralized execution (CTDE) policy-based multi-agent reinforcement learning (MARL) algorithms, which arises from local observation inaccuracies. To address this issue, we propose a novel MARL algorithm that incorporates imitation learning using local observations. Firstly, by analysing the multi-agent proximal policy optimization algorithm and examining the problems arising when global states are replaced with local observations, it is proved that insufficient observations can lead to information loss, thereby introducing errors of advantage function, and it is demonstrated that the generalized advantage estimation method accumulates errors during the training process. Then, imitation learning is introduced and a novel training framework that combines reinforcement learning and imitation learning is proposed. During the reinforcement learning phase, an MARL agent trained with global observations acts as an expert. Subsequently, imitation learning is applied to train another agent that mimics the expert's decisions using only local observations. Finally, the effectiveness of this algorithm is verified in some commonly used multi-agent environments, which demonstrates its superior performance compared to traditional multi-agent reinforcement learning algorithms.

Willems' fundamental lemma (WFL) is widely used in the data-driven model predictive controllers (MPCs) to model the plant and predict its response to the given input sequence. The advantage of this model is that it can be incorporated as a linear matrix equality constraint into the optimization problem of MPC. However, the response of a dynamical system depends not only on the input applied to it but also on its initial condition. The WFL does not directly state anything about how the initial condition of the system should be defined and incorporated into the data-driven model equations. The conventional method for incorporating the initial condition of the system in a data-driven model is to insert a number of the most recent input and output samples of the system into the same data-driven model used for predicting the system's response, and this equation is then added as a new constraint to the optimization problem. This paper shows that the conventional method of incorporating the system's initial condition into the data-driven model is inaccurate and leads to errors in predicting the system's output. Moreover, the correct method for doing this has been presented for linear time-invariant systems, and the results have also been extended to the non-linear case. The proposed method has been used to design a data-driven MPC for a lab-scale wind turbine and experimental results have been presented.

This paper examines the issue of collision-avoidance trajectory tracking control in a multi-quadrotor unmanned aerial vehicle (UAV) slung load system, with particular emphasis on the scenario where the reference trajectory is unreachable. The challenge of tracking an unreachable reference trajectory is effectively addressed by integrating a trajectory planner and a trajectory tracking controller within a unified distributed model predictive control (DMPC) framework. Moreover, the nonlinear system is linearized using the first-order Taylor approximation, significantly simplifying the computation in DMPC. To ensure collision avoidance with both dynamic and static obstacles, the MINVO basis is employed to calculate the minimum volume of the exterior polyhedral approximation of the obstacles' trajectories, which is significantly smaller than that achieved using the B-spline or Bernstein bases typically utilized in the planning literature. Simulation experiments involving four UAVs, one payload, two static obstacles, and one dynamic obstacle are conducted to evaluate the effectiveness of the proposed DMPC method.

This paper presents a fault detection method for uncertain systems under denial-of-service (DoS) attack using a probabilistic approach. Model uncertainties, disturbances, and faults are systematically described using probabilistic parameter models. An aperiodic DoS attack model is introduced, with limitations only on the duration and frequency of the attack. By using a randomized algorithm, a switched fault detection filter is constructed, and a threshold is designed to achieve a balance between the false alarm and fault detection rate. The feasibility of the developed method is illustrated through experimental validation conducted on an unmanned surface vehicle system.

Studies have suggested that parametric strict-feedback nonlinear systems with relative degree $�n$ exhibit higher-order tracking characteristics. A natural question that arises is whether the adaptive control of the output feedback system still exhibits higher-order tracking characteristics. These characteristics have not been previously documented in the literature. Therefore, this paper investigates the adaptive control of output feedback systems and demonstrates that such systems exhibit higher-order tracking error convergence (i.e., the $��i�\mathrm{th}�$-order derivatives of the output tracking error converge to zero), thereby providing an affirmative response to the aforementioned question. The simulation results verify the presence of higher-order tracking performance in an electromechanical system.

This paper explores the design of optimal controllers for systems with logarithmic quantised inputs, using reinforcement learning. We introduce a novel method that ensures global optimality in Guaranteed Cost Control (GCC) while achieving quadratic stabilisation through the selection of an optimal scaling gain. By transforming the uncertain system into an $�H_{\infty }$​ control framework, we derive the optimal solution using a Zero-Sum Dynamical Game (ZSDG). We then reformulate the problem using a virtual input, eliminating reliance on the scaling gain. Based on the introduced virtual input, we develop a novel model-free Q-function and an algorithm for controller synthesis that is independent of the scaling gain. The proposed Q-function matches the dimension of a standard Q-function, minimising the number of decision variables. Simulation results on real-world systems demonstrate that the proposed approach consistently outperforms both model-free and model-based methods, delivering superior optimality and computational efficiency.

This paper investigates a predefined-time compound tracking control scheme for hybrid-actuator missiles to address the concurrent challenges posed by external disturbances, actuator saturation, and partial actuator failures. An improved predefined-time prescribed performance function (PTPPF) with single-sided monotonic boundaries is proposed to confine tracking errors within preassigned ranges, which effectively suppresses potential overshoot and prevents excessive initial control commands. By combining the improved PTPPF with a novel nonsingular sliding mode manifold based on hyperbolic functions, a predefined-time prescribed performance control (PTPPC) is developed to guarantee accelerated convergence and superior transient tracking performance. Besides, an auxiliary system is introduced to compensate for mismatches between virtual and actual control inputs caused by actuator saturation and allocation inaccuracies, which can effectively enhance overall control accuracy. Furthermore, a predefined-time adaptive control allocation (PTACA) strategy is designed to dynamically distribute virtual control commands among aerodynamic surfaces and lateral thrusters. The proposed PTACA explicitly accounts for actuator saturation and partial actuator failures, which improves control efficiency and minimises energy utilisation. The theoretical stability of the entire control framework is established via Lyapunov analysis. Finally, comparative numerical simulations are conducted to validate the effectiveness and superiority of the proposed scheme.