International Journal of Control Automation and Systems最新文献

This paper investigates secure semi-global bipartite consensus (SSGBC) of linear multi-agent systems (MASs) with input saturation under denial-of-service (DoS) attacks via dynamic event-triggered control (DETC). A distributed DETC protocol is proposed for avoiding redundant information transmission. Subsequently, by utilizing Lyapunov stability theory and a low-gain feedback based bipartite consensus algorithm, it is proved that SSGBC of linear MASs can be achieved under the proposed DETC protocol under the assumption that the frequency and the duration are limited. Moreover, Zeno behavior of each follower can be excluded. Finally, a simulation example is given to verify effectiveness of the proposed DETC protocol.

Finite control set model predictive control (FCS-MPC) strategy for power converter system outputs single optimal vector by vector ergodic optimization algorithm, with flaws of low vector control accuracy, unfixed switching frequency and high computational burden for microprocessor. In this paper, a hybrid vector and sector model predictive control (HVS-MPC) strategy is proposed for medium and small power converter systems with high control frequency to improve system performance on three aspects. First, a sector optimization algorithm is adopted to reduce computational burden according to radiation range of basic vector and sector location of target vector. Second, a vector synthesis algorithm is adopted in linear modulation area to improve vector control accuracy with fixed switching frequency by vector operation time calculation and sequence setting. Third, in order to improve system performance in over modulation area without extra computational burden, the hybrid vector modulation algorithm is proposed based on target vector length and modulation area. Finally, the proposed strategy is verified through experiment and simulation.

This paper proposes a switching control scheme for the Hunt-Crossley model, which represents the behavior of contact between two surfaces in mechanical systems. The scheme comprises a PID force feedback controller and a position/velocity feedback controller. Its objective is to apply the desired amount of pressure while ensuring closed-loop stability. The PID force feedback controller operates in contact mode, while the position/velocity feedback controller operates in non-contact mode. For the PID control, a non-quadratic Lyapunov function is devised together with an invariant domain of attraction within the contact region, including the equilibrium steady state. For the non-contact region where the state trajectories stay only temporarily, the position/velocity feedback control is equipped with a disturbance compensation term on the basis of a Lyapunov min-max approach, which leads to a quadratic Lyapunov function. Notice the two different ways of achieving infinite gain operation for handling modeling errors and disturbances: infinite gain at DC, resulting from integral action in the contact mode, and infinite gain at equilibrium, resulting from the use of a signum function in the non-contact mode. The asymptotic stability of the overall switching control system is proven by demonstrating that the two Lyapunov functions defined in the contact and non-contact regions satisfy certain decreasing properties. Simulations confirm that the applied force closely tracks the desired value in the presence of a DC disturbance and model uncertainty.

This paper investigates the issue of event-based consensus for double-integral heterogeneous hybrid multi-agent systems with communication delays under directed graphs. The system contains continuous-time and discrete-time subsystems, both of which are heterogeneous systems, including single-integral and double-integral agents. To achieve consensus among agents with different dynamics, two distributed event-based control algorithms are designed. Combined with the sampled-data approach, efficient event-triggered functions based on hybrid measurements are designed. Then, sufficient conditions of control parameters, sampling interval and coupling gains are proposed. By applying non-negative matrix theory, graph theory and other mathematical methods, consensus of fixed topology and switching topologies are proved with the assumption that the directed graph has a spanning tree. A simulation example finally confirms the effectiveness of the event-triggered protocols.

This paper addresses the control problem for a class of discrete event systems with disturbances, which is subject to mutual exclusion constraints (MECs). Our aim is to propose a new analytical method for designing control laws to guarantee a set of MECs in network timed event graphs (NTEGs) with uncontrollable input transitions. To develop this control law, linear min-plus equations are used to describe the behavior of the NTEGs and min-plus inequalities to translate the specifications to be satisfied. Sufficient conditions for the existence of causal control laws are established. These calculated laws are represented by control places that will supervise the NTEGs to ensure that constraints are respected. To demonstrate the effectiveness of the suggested approach, an illustrative application on an assembly system is carried out.

This paper presents a proposed strategy for improving the position-tracking accuracy of force-based impedance control in hydraulic single-leg robots. Initially, the mechanical structure and drive system of the single-leg robot are introduced. Subsequently, a kinetic and dynamic model is developed to determine the desired position and force for each joint based on the given action. The proposed strategy, called equivalent stiffness impedance control, is then presented. It combines a penalty function and the stiffness of each joint near the desired position to calculate the equivalent stiffness. Simulations and experiments are conducted to evaluate the performance of the control strategy. The results demonstrate that the proposed strategy achieves fast response speed and high position tracking accuracy. Moreover, the mechanical characteristics near the desired position are comparable to traditional impedance control. This research provides valuable insights for impedance control in bionic-legged robots.

In this paper, a voltage control system for an applicable Boost converter system under disturbances is presented. The voltage adjustment performance of the Boost converter is governed by a feedback PID controller system. The realism of the proposed power system is validated through using the Simscape environment, which is adopted to model and simulate the PID based Boost converter system. The presented Simscape system is more applicable in real-time than the Simulink model as the model of the Simscape components and devices is built based on practical considerations like tolerance and parasitic elements. PSO optimization algorithm is utilized to develop the performance of the PID controller through operating the converter system based on best values for PID gain parameters. The simulation design of the proposed power system is implemented in the Matlab tool and its results are presented and discussed to evaluate the performance of the presented feedback PID controller. To validate the robustness of the converter performance the voltage adjustment behavior of the PID controller is assessed based on disturbance of supply voltage, reference voltage and resistive load working conditions. Simulation results revealed the ability of the PID controller to reject the supply voltage disturbances and enable the Boost output to track the demand input trajectories effectively.

This paper aims to optimize the performance of a scalar continuous-time linear time-invariant system, whose feedback information is transmitted through a rate-constrained communication network with random network delay. Due to the limitation of feedback network resources, the feedback information can not be available to the controller precisely and continuously. Moreover, the random transmission delay will reduce the amount of feedback information and degrade the performance of the concerned system. To resolve the above issues, we propose an event-triggered sampling strategy that can utilize the transmission resources more efficiently. Compared with the optimal time-driven sampling strategies and the optimal Lebesgue sampling strategies, our event-triggered sampling strategy can guarantee better performance under the same transmission constraints. The effectiveness of the proposed event-triggered sampling strategies is further illustrated via simulations.

This paper investigates the distributed online convex optimization problem in multi-agent systems, where each node cannot directly access the gradient information of its own cost function. The communication topology is formed by the strongly connected time-varying directed graphs with the column stochastic weight matrices, where each node updates its own decisions by exchanging information with neighbouring nodes. It is not feasible to sample objective function values at several consecutive points simultaneously since the online setting is time-varying. To solve this problem over directed graphs, a push-sum one-point bandit distributed dual averaging (PS-OBDDA) algorithm is proposed, where the one-point gradient estimator is employed to estimate the true gradient information, to guide the updating of the decision variables. Moreover, by selecting the appropriate exploration parameter δ and step sizes α(t), the algorithm is shown to achieve the sublinear regret bound with the convergence rate (O({T^{{5 over 6}}})). Furthermore, the effect of one-point estimation parameters on the regret of the algorithm in online settings is explored. Finally, the performance of the algorithm is evaluated through simulation.

Lithium-ion batteries are currently used as a key energy source in various industrial facilities, electronics, and automotive industries. However, due to the frequent charging and discharging of batteries, overcharging and overdischarging can occur, leading to fire and safety accidents as well as additional financial damages due to equipment failure. Therefore, accurately estimating the battery state of charge (SOC) is very important. In this paper, the robustness of estimation models was analyzed in relation to data collected amidst sensor failures. This analysis was especially pertinent during the battery SOC estimation process, when voltage and current sensors were prone to failure. The impact of these sensor failures on the accuracy and reliability of the SOC estimation models was rigorously scrutinized. Normal data was trained as training data, and Gaussian distribution, Laplace and chi-square combined distribution, Add bias distribution were employed as the test data. Herein, multilayer neural network, long short-term memory, gated recurrent unit, gradient boosting machine (GBM) were used as neural networks, the failure signal processing performance of each estimation algorithm was compared and analyzed, and the failure diagnosis was performed using support vector machine and GBM.