IET Control Theory and Applications最新文献

In multi-agent systems (MASs), not only do agents engage in cooperative relationships, but they also compete with one another. The topic of collaborative group in MASs has garnered significant attention. Due to the possibility of explicit interaction among agents, the complete sharing of known information among nodes presents a risk of privacy leakage. Therefore, this article explores the problem of asymmetric bipartite consensus control for MASs with strong-privacy-preserving. A novel state decomposition privacy-preserving algorithm is proposed to achieve asymmetric bipartite consensus of MASs. This algorithm can ensure that the initial and final states of each agent will not be compromised, via a preservation coefficient designed in proposed algorithm. By employing algebraic graph theory and automatic control theory, a thorough analysis of the algorithmic performance is conducted. It is demonstrated that our algorithm can preserve the privacy of all agents in MASs while achieving asymmetric bipartite consensus, i.e. the consensus values are different in sign and modulus. Finally, the effectiveness of the main results is validated through simulation examples.

This study is concerned with the stabilization analysis and controller design for networked systems with stochastic sampling and two-channel deception attacks. First, we give a general matrix decomposition approach which is applicable to scenarios where the system matrix $�A$ contains complex-value eigenvalues. Then, a discrete stochastic framework is established for a class of networked systems which considers the joint effects of sampling errors and two-channel deception attacks. Utilizing the matrix decomposition approach introduced in this study, it becomes feasible to decouple the expectation operations for specific coupling matrices characterized by substantial nonlinearity and randomness. Based on this, a stabilization controller is constructed that ensures the exponential mean-square stability of the resulting discrete stochastic system. Finally, three simulation examples are provided to validate the effectiveness of the proposed approach.

In response to the problem of disturbance in the imaging equipment of stratospheric airships, this study designs a vibration isolation and stability system for carrying imaging equipment. The system mainly consists of microprocessor, gimbal, voice coil motor (VCM) and rubber ring. By using adaptive control law based on characteristic model to control the double closed control loop of the motor composed of position loop and speed loop, the camera deflection caused by disturbance is effectively eliminated. Furthermore, in vertical vibration suppression, the adaptive control law based on the characteristic model is also applied to the double closed control loop of the voice coil motor, which removes most of the vertical vibration and realizes active vibration isolation. The passive isolation of the system is completed by rubber rings. The experimental results show that the vibration isolation and stabilization system have good performance in complex disturbance environments, effectively stabilizing the imaging equipment.

The group-weighted containment (GWC) problem is studied for swarms of robots, where the information flow among agents is heterogeneous (weighted) cooperative-competitive and the whole networked systems are composed of multiple different groups. Each group is formed by multiple harmonic oscillator leaders and multiple robot followers governed by Euler-Lagrange (EL) equations. After introducing the definition of group-weighted containment to the networked robot systems, the control algorithms for the agents containing followers and leaders are formulated over newly weighted cooperative-competitive network. Some necessary conditions for solving the weighted containment control problem are established. It is shown that the followers in every group can achieve a kind of novel coordinated behaviors. Specifically, the followers can converge to the dynamic convex hull spanned by the corresponding leaders' weighted coordinates under some conditions. Simulation results are provided to illustrate the effectiveness of the proposed control schemes.

This study investigates a novel tracking control with prescribed performance for an uncertain nonlinear 2-DOF helicopter subject to actuator failure. The control design aims to address partial actuator faults, and to mitigate their effects, an adaptive auxiliary variable is introduced. Additionally, a neural network is employed to accurately approximate the system uncertainties. The prescribed performance is formulated as time-varying constraints, and the barrier Lyapunov function (BLF) technique is applied to achieve the constrained control. Finally, the stability analysis of the closed-loop system is provided, and the validity of the proposed control scheme is illustrated through a simulation study on the 2-DOF helicopter system.

This article considers the problem of output feedback stabilization for a class of stochastic high-order nonlinear time-delay systems with unknown output function. For stochastic high-order nonlinear time-delay systems, based on the Lyapunov stability theorem, by combining the addition of one power integrator and homogeneous domination method, the maximal open sector $�\Delta$ of output function is given. As long as output function belongs to any closed sector included in $�\Delta$, an output feedback controller can be developed to guarantee the closed-loop system globally asymptotically stable in probability.

Due to time-varying external disturbances and uncertain system models, distributed cooperative controllers with poor adaptability are unable to meet the cooperative control requirements of multiple permanent magnetic maglev trains in virtual coupling mode. In this work, a new effective distributed auto disturbance rejection resilient controller based on the optimized deep deterministic policy gradient algorithm (DDPG) is proposed. The DDPG algorithm is used to improve the adaptability of the controller against the time-varying disturbances. An adaptive particle swarm optimization method (APSO) is also proposed to optimize the hyperparameters of DDPG in the search space. The simulation results show that, compared to the particle swarm optimization (PSO)-actor-critic (AC), PSO-policy gradient (PG), and PSO-DDPG algorithms, the proposed APSO-DDPG algorithm performs better during training and verification. The proposed method achieves adaptive online adjustment of the controller parameters effectively and greatly improves the stability of cooperative control.

This paper is concerned with the fuzzy functional observer-based sliding mode control for T-S fuzzy cyber-physical systems subject to disturbances and deception attacks. First, the deception attack is modeled by an exogenous system with state dependent nonlinearity. Second, a fuzzy learning functional observer with fuzzy logic systems learning unknown nonlinear function, whose parameters can be directly found, is designed to estimate the partly unavailable states, deception attacks and lumped disturbances simultaneously. Then, a fuzzy sliding mode control is designed to compensate the effect of deception attack and reject the disturbances. Furthermore, some sufficient conditions are given to guarantee that the closed-loop fuzzy system is exponential convergent. Finally, a numerical example is given to illustrate the validity of the proposed method.

The concurrent existence of model uncertainties, external disturbances, and actuator faults in a nonlinear system such as a robotic manipulator can significantly affect the trajectory tracking performance of these systems. Therefore this study is devoted to proposing a control approach based on fixed-time control theory to improve the trajectory tracking performance of the robotic manipulator in the presence of lumped disturbance, including uncertainties, external disturbances, and actuator faults. The control approach in this paper is designed based on the integration of a fixed-time adaptive sliding mode observer and a fixed-time non-singular fast terminal sliding mode control design strategy. Firstly, a new fixed-time adaptive sliding mode observer is designed based on fixed-time theory and adaptive control theory to estimate the lumped disturbance present in the system. Then, using the information from the disturbance observer, the fixed-time non-singular fast terminal sliding mode control is devised based on a non-singular fixed-time sliding surface and a fixed-time reaching approach. Furthermore, in the sense of the Lyapunov theorem, through rigorous analysis, it is demonstrated that the tracking errors of the closed-loop system converge to a small neighbourhood within a fixed time, regardless of the information about the initial conditions of the states of the system. Finally, extensive comparative simulations are performed using the PUMA560 robot to manifest the feasibility and validity of the proposed control strategy in terms of trajectory tracking accuracy and fast convergence in the presence of uncertainties, disturbances, and actuator faults.

This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend-state-observer is utilized to estimate the velocity signals; then, a neural-network-based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural-network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade-off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two-degree-of-freedom robot manipulator.