IET Control Theory and Applications最新文献

This paper concentrates on synthesizing robust predictive controllers in uncertain models with time-delay and saturated input. To handle the complexities simultaneously and reach the desired objective, a state-feedback structure with unknown gains is considered the compensator. Then, to specify the instantaneous values of the regulator's gains, an optimization issue will be derived relying on linear matrix inequality. Accordingly, in uncertain continuous-time systems with some constraints and time-delays, the controller's coefficients would be found in an online way from such an optimization. Numerous continuous-time simulations are numerically performed to reveal the merit and robustness of the suggested methodology over similar techniques.

This paper considers the equilibrium-free stability and performance analysis of discrete-time nonlinear systems. Two types of equilibrium-free notions are considered. Namely, the universal shifted concept, which considers stability and performance w.r.t. all equilibrium points of the system, and the incremental concept, which considers stability and performance between trajectories of the system. This paper shows how universal shifted stability and performance of discrete-time systems can be analysed by making use of the time-difference dynamics. Moreover, the existing results are extended for incremental dissipativity for discrete-time systems based on dissipativity analysis of the differential dynamics to more general state-dependent storage functions for less conservative results. Finally, it is shown how both these equilibrium-free notions can be cast as a convex analysis problem by making use of the linear parameter-varying framework, which is also demonstrated by means of an example.

This article focuses on the problem of stability for a class of linear networked control systems (NCSs) subjected to network communication delays and random deception attacks. A new memory event-triggered mechanism (METM) is proposed to reduce the unnecessary transmitted data through the communication channel and then enhance the network resources. In this context, a new memory stochastic state feedback controller is proposed to stabilize the closed-loop networked control system. A new randomly occurring deception attacks model is employed to deal with the security problem of NCSs. Sufficient stability conditions are derived based on a suitable Lyapunov-Krasovskii functional (LKF). The designed methodology is proposed in terms of linear matrix inequality to synthesize both event-triggered parameters and controller gains, and to reduce the conservatism of the system some integral lemma are exploited to bind the time derivative of the LKF. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed method which provides a maximal upper bound value of the network-induced delay and less transmitted packet regarding the maximal value delay obtained in other works, so less conservatism results are obtained, compared to previous ones in the literature.

Due to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi-agent systems. Approximation-free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation-free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.

This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications.

Aiming at the problem of sub-synchronous oscillations induced by direct-drive wind farms with series-compensated lines, this paper proposes an unknown system dynamics estimation-based PI controller to achieve sub-synchronous oscillation suppression. According to the mathematical model of the direct-drive wind farm grid-connected system, the relationship between the direct-drive wind turbine grid-side converter current inner-loop PI controller and the sub-synchronous components is first established. Secondly, the uncertain sub-synchronous current components and voltage components in the series-compensated lines of the direct-drive wind farm are taken as the total disturbance, and an unknown system dynamic estimator-based PI controller is designed by introducing the first-order low-pass filter operation. Then, the stability and convergence of the closed-loop system are proved by Lyapunov theory. Finally, the Prony method is used to analyse the current signal output by the direct-drive wind turbine, and the inherent characteristics of the negative damping of the SSO induced by the series-compensated line of the direct-driven wind farm are revealed. A comparative numerical simulation is carried out to demonstrate that the sub-synchronous oscillations of the direct-drive wind farm with series-compensated lines can be suppressed under different operating conditions.

The purpose of this article is to develop the receding horizon (RH) estimation for multi-rate sampled systems with measurement delays and packet losses in the measurements. An event-triggered transmission scheme is introduced to remove measurements that are unnecessary for the design of the estimator. The stochastic diagonal matrixes are introduced to represent the phenomenon of packet losses, where each component is subject to an individual Bernoulli process. The original system is firstly transformed into a delay-free one by using the reorganized observation method. Further, a batch form and an iterative form of RH estimation are proposed by minimizing a given cost function that includes some terminal weighting terms based on the new system. The stability of the proposed RH estimation is guaranteed by the natural assumptions and a simulation instance is given to verify the effectiveness of the proposed method.

The complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding-based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding-based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks.

In this study, it is considered that the question of adaptive tracking control of nonlinear systems containing unknown time-varying parameters. To optimize the control effect, a fixed-time control strategy is adopted, in which the upper bound of the settling time is connected with the designed parameters, but not to the initial conditions. In the design, the unknown time-varying parameters are treated as the multiplication of known and unknown vectors, and the adaptive laws are constructed to estimate the mean value of the components in the unknown ones, which availably solve the difficulty of unknown control gains. Besides, the application of command filtered technique, which combined with the compensation signals containing the adaptive parameters, effectively avoids the “complexity explosion” problem. Further, Lyapunov stability theory verifies that the tracking error can always be kept within the boundaries of the performance functions, and converges to a designated neighbourhood of the origin within a fixed time, beyond that all signals of the closed-loop system are bounded. Finally, the simulation results prove the effectiveness of the proposed control scheme.

This paper presents an edge-based event-triggered delay distributed algorithm for solving the economic dispatch problem (EDP) in smart grids. The objective of the EDP is to minimize the total generation cost by allocating power to individual generators, each with its local generation constraint. To save the overhead of communication resources between agents, an edge-based event-triggered mechanism is suggested. By setting distinct triggering thresholds for each communication edge, the agent may efficiently regulate the communication frequency among all neighbors. However, in practice, owing to the instability of the network, the agent may receive information regarding its neighbors, leading to communication lags for every communication link. The virtual agent technique and double-stochastic matrix augmentation technique provide an equivalent delay-free EDP. It is demonstrated that the proposed algorithm can asymptotically converge to the global optimal solution as long as the communication delay is arbitrary, time-varying, and random, but bounded. Objective and clear evidence is provided through a case study in smart grids to verify the algorithm's feasibility.