IET Control Theory and Applications最新文献

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.

This paper presents decentralized fuzzy filter design techniques for discrete-time nonlinear large-scale systems with uncertain interconnections. The subsystems of the nonlinear large-scale system and the decentralized filters are represented using Takagi–Sugeno (T–S) fuzzy model. Based on the closed-loop system with estimation error, the decentralized fuzzy filter design problem is addressed to guarantee the $�H_{\infty }$ filtering performance. A sufficient condition is derived to satisfy both the stability condition and the $�H_{\infty }$ filtering performance, and its constructive condition is reformulated into various linear matrix inequality (LMI) formats. Finally, the effectiveness of the proposed methods and procedures is demonstrated through a simulation example. The main novelties of this paper can be broadly summarized into two points: First, the decentralized fuzzy filter design techniques are effectively proposed for uncertain interconnection by using the maximum interconnection bound. Second, the decentralized fuzzy filters are designed when the premise variable is not only measurable but also non-measurable.

This study addresses the adaptive dynamic quantization control problem for Markov jump systems (MJSs) subject to multimode injection attack (IA). For the controlled system, the Markov chain and multimode IA, modelled as a categorical-distributed stochastic process, are jointly characterized by a hidden Markov model. Firstly, a sliding mode controller without quantization signals is designed and a mode-dependent Lyapunov function is constructed to establish the exponential ultimate boundedness condition of the MJSs. And then, under the frame that the state signal is quantized before transmitted to controller, an adaptive dynamic quantization strategy is further proposed such that the above controller can achieve the same control performance as the non-quantized case, eliminating the need for re-designing new controller. Finally, simulation results validate the effectiveness and robustness of the proposed quantized control strategy under combined dynamic quantization and IA scenarios.

Control barrier functions (CBFs) offer provable safety guarantees for dynamical systems. However, the existing control syntheses heavily depend on safety priors, which limits their applicability to data-driven scenarios. To deal with this issue, we propose an optimization-based approach to obtain data-driven high-order CBFs (HOCBFs) from the offline dataset. We first construct a group of safety-guaranteed sets from safe trajectories and provide verification conditions for valid HOCBF candidates via Lipschitz properties. Next, we propose a novel optimization framework to obtain a data-driven HOCBF with enhanced feasibility. To solve the proposed optimization problem, we provide convex and non-convex optimization approaches with different neural network implementations. Finally, the proposed approaches are illustrated via two numerical examples from the safe exploration of an unmanned aerial vehicle in static but unknown environments.

This study introduces a two-stage estimation framework designed to mitigate the online computational burden associated with traditional state estimation algorithms, such as the Kalman filter and extended Kalman filter, which require continuous computation of time-varying gains. The proposed framework integrates a fixed-gain Luenberger observer for base estimation with a lightweight neural network-based compensation module, which is trained offline and deployed online to enhance accuracy by correcting residual errors caused by fixed-gain limitations. To handle both linear and non-linear systems effectively, distinct loss functions are constructed by integrating prior knowledge of the system model, enabling the network to produce physically meaningful and interpretable outputs. Numerical simulations validate the effectiveness of the proposed method, demonstrating improved estimation accuracy and reduced computational cost compared to conventional estimators.

This study presents a robust state estimator for linear impulsive switched systems subject to unknown input signals and measurement noise. Such systems exhibit highly complex behaviour due to sudden dynamic changes and external disturbances, which significantly complicates the state estimation process. The presence of unknown inputs and measurement noise may not only degrade estimation accuracy but also challenge the stability of conventional observers. To overcome these difficulties, a novel full-order observer design is proposed that does not require output differentiation, thereby eliminating impulsive effects typically caused by differentiating noisy outputs. Moreover, the proposed framework explicitly accounts for measurement noise, an aspect that has rarely been addressed in the context of impulsive or switched systems. The proposed estimator ensures exponential convergence of estimation errors while simultaneously enhancing accuracy and robustness under impulsive switching conditions. The effectiveness of the method is demonstrated through simulations on an electrical circuit with variable loads. The results confirm that accurate and stable state estimation is achieved even under fast switching and in the presence of disturbances, thereby highlighting the practical applicability of the proposed approach.

The challenges of communication interruptions have caused unmanned vehicles to lose the real-time transmission of essential data. The traditional control architecture, designed for continuous measurement, is inadequate to address the constraints posed by the fragility of facilities in harsh maritime conditions. Therefore, it is necessary to enhance the transient stability performance in non-ideal conditions and environments. This work proposes a novel intermittent predefined-time control algorithm. This approach eliminates the need for frequent measurement and recalculation of control laws at every moment, and extends the usage scenarios of intermittent control from first-order systems to second-order systems. During the holding interval, the control input remains as the last control signal in the driving interval, provided that the validated stability conditions are satisfied. The designed controller also ensures stability with zero control inputs during the holding interval. Furthermore, the proposed method aims to optimise energy consumption in control and measurement. Using the Lyapunov theorem and mathematical induction, sufficient conditions for achieving predefined-time convergence in intermittent control are derived and verified with unmanned vehicles. The proposed method demonstrates a 5.4% reduction in tracking error compared to the fixed-time control method, and a 7.9% reduction in tracking error compared to the event-triggered predefined-time control method. This approach reduces the frequency of control law recalculations, measurements and energy consumption.