IET Control Theory and Applications最新文献

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.

This paper presents the design of a complex-step extended Kalman filter (CS-EKF) to estimate the states of the twin-rotor MIMO system (TRMS) which is a non-linear system. Since the model of TRMS is quite complex and contains discontinuous functions, it is very difficult to calculate the Jacobian matrix in the TRMS analytically by hand. This makes it difficult to implement control methods that require Jacobian matrix calculation for TRMS. Herein, to calculate the Jacobian matrix, the CS-EKF uses the complex-step derivative approach, which is a numerical technique and offers near-analytical accuracy in a single function evaluation. The effectiveness of the CS-EKF is demonstrated through simulation and real-time experiments. Also, The CS-EKF is compared to the finite-difference extended Kalman filter (FD-EKF) and the unscented Kalman filter (UKF) in terms of estimation accuracy, computational load, and ease of implementation.

This article addresses the issue of collaborative wildfire monitoring using a group mobile mecanum-wheeled omnidirectional vehicles (MWOVs) affected by nonlinear uncertainties and external disturbances. By integrating finite-time extended state observers (FTESO) and backstepping nonsingular fast terminal sliding mode (BNFTSM) control method, an observer-based finite-time time-varying elliptical formation control scheme is proposed for a group of MWOVs tasked with monitoring the propagation of wildfires in an elliptical pattern. First, the FTESO is employed to estimate the unavailable velocity system states and the lumped disturbances. Then, a novel nonsingular fast terminal sliding surface, enhanced with an exponential term, is introduced to improve the convergence rate. Through the Lyapunov theorem, the convergence of position and velocity cooperative tracking errors to zero in fast finite-time is demonstrated. To showcase the effectiveness of the proposed control scheme, comparative simulation results are presented.

With the increase of wind power generation, the safety and economy of power system operations are greatly influenced by the intermittency and fluctuation of wind power. To take the advantage of the complementary characteristics between different energy storage devices, a Hybrid Energy Storage System (HESS) consisting of Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS) can alleviate the uncertainty of wind power. This article has proposed a coordinated control strategy through group consensus algorithm based on Model Predictive Control (MPC) for Hybrid Energy Storage Array (HESA) to smooth wind power fluctuations. To allocate power commands to the FESS and BESS, the fluctuation of wind power output is extracted with different frequency domain characteristics as instructions by Empirical Mode Decomposition (EMD) technology. Moreover, a group consensus algorithm based on MPC is proposed to complete the adaptive power allocation of energy storage units. Eventually, the actual wind farm data is used for the simulation to verify the effect of control strategy proposed in this paper. It can be seen that the developed group consensus algorithm based on MPC can cope with different frequency power commands, avoid overcharging and discharging of energy storage media, and smooth wind power effectively.

Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed-loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed-loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear-quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL-controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non-linear system and then tested on the corresponding real-world hardware platform without any fine-tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU).

With the rapid development of unmanned aerial vehicle (UAV) clusters, they will become the main means in urban transportation, traffic monitoring and other fields. The positioning accuracy of UAV clusters is the core basis of the application, this paper proposes a UAV cluster information geometry fusion positioning method based on low-orbit satellite system, which uses the geometric relationship between UAV clusters to transform the low-orbit constellation navigation information of each UAV into an information geometry probability model, reducing the influence of the low-orbit satellite system time asynchrony, and through Kullback–Leibler average fusion to achieve UAV cluster real-time high-precision positioning. The method effectively solves the problem caused by the easy loss of GNSS signals in the obscured environment, and significantly improves the reliability and stability of UAV cluster positioning in urban environments. Comparing the method of this paper with the existing UAV fusion navigation methods in terms of positioning accuracy stability, positioning real-time and error mutation under the occlusion scenario, the experimental results show that the method of this paper has obvious superiority in the above indexes.