自动化学报最新文献

In this paper, the finite-time consensus problems of heterogeneous multi-agent systems composed of both linear and nonlinear dynamics agents are investigated. Nonlinear consensus protocols are proposed for the heterogeneous multi-agent systems. Some sufficient conditions for the finite-time consensus are established in the leaderless and leader-following cases. The results are also extended to the case where the communication topology is directed and satisfies a detailed balance condition on coupling weights. At last, some simulation results are given to illustrate the effectiveness of the obtained theoretical results.

A nonlinear control is proposed for trajectory tracking of a 6-DOF model-scaled helicopter with constraints on main rotor thrust and fuselage attitude. In the procedure of control design, the mathematical model of helicopter is simplified into three subsystems: altitude subsystem, longitudinal-lateral subsystem and attitude subsystem. The proposed control is developed by combining the sub-controls for the corresponding subsystems. The sub-controls for altitude subsystem and longitudinal-lateral subsystem are designed with hyperbolic tangent functions to satisfy the constraints; the sub-control for attitude subsystem is based on backstepping technique such that fuselage attitude tracks the virtual control for longitudinal-lateral subsystem. It is proved theoretically that tracking errors are ultimately bounded, and control constraints are satisfied. Performances of the proposed controller are demonstrated by simulation results.

In this paper, the cooperative control problem of networked Euler-Lagrange systems with parametric uncertainties and unidirectional interaction is addressed under dynamically changing topology. As the communication graph evolves over time, a distributed control law via local effective interactions is designed. Adaptive techniques are used to deal with parametric uncertainties in the dynamics. With a continuous Lyapunov function, it is obtained that synchronization can still be achieved asymptotically as long as the union graph of the switching topologies has a directed spanning tree frequently enough. Extensions to disturbance rejection problems are also addressed using simple disturbance-observer or sliding mode control scheme. Illustrative examples with comparing simulation in the context of attitude synchronization of five non-identical spacecraft are further presented to show the effectiveness of the proposed cooperative control strategy.

This paper investigates the average consensus problem in directed networks of multi-agent systems with uncertain time-varying delays. Fixed and switching topologies that are kept weakly connected and balanced are firstly analyzed. The original system is then transformed into a reduced dimension model. Based on Jensen's inequality and reciprocally convex approach, sufficient conditions for average consensus are further presented. Specially, a less conservative upper bound of time-varying communication delays is derived in comparison with the existing results. Numerical examples confirm the effectiveness of the proposed method.

Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions. In this paper, we propose an iterative dynamic diversity evolutionary algorithm (IDDEA) with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps. In IDDEA, a novel optimum estimation strategy with multi-agents evolving diversely is suggested to efficiently compute dominance trend and establish a subregion. In addition, a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration, which is based on a special dominance estimation scheme. Meanwhile, an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents. Furthermore, several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.

This paper considers the coordinated tracking problem for a group of Lagrange systems in the presence of parametric uncertainties. Distributed adaptive controllers are proposed with the aid of Lyapunov techniques. Compared with the previous work in the context of networked Lagrange systems control, the results in this paper are suitable for the general digraph communication topologies. Under the condition that the desired trajectory is only available to a portion of Lagrange systems, we discuss the cooperative tracking problem with general digraph communication topology, which contains a spanning tree with the root node being the active target system. Under the case where the neighbor's velocity is unavailable, a distributed filter is introduced to overcome this deficiency. Experimental results on networked robot-arms are provided to show the effectiveness of the proposed control algorithms.

This paper proposes a novel swing constraint-based trajectory planning method for nonlinear overhead crane systems. To enhance the efficiency and security of the transportation process, some desired trajectories are designed to achieve the following merits: 1) leading the trolley to reach the destination sufficiently fast; 2) keeping the payload swing in an acceptable domain; 3) eliminating the residue swing when the trolley stops at the desired position. Specifically, the trajectories are divided into three stages. For each stage, the desired curve of the swing angle is directly constructed in accordance with anti-swing and zero-residual swing requirements, based on which the trolley trajectory is then obtained by analyzing the nonlinear kinematics of the crane system. An optimization mechanism is introduced to make intelligent compromises among the indices of transportation time, maximal swing angle, and so on. Both simulation and experimental results are provided to demonstrate the performance of the proposed direct swing constraint-based trajectory planning method.

The distributed-power electric multiple units (EMUs) are widely used in high-speed railway. Due to the structural characteristic of mutual-coupled power units in EMUs, each power unit is set as an agent. Combining with the traction/brake characteristic curve and running data of EMUs, a subtractive clustering method and pattern classification algorithm are adopted to set up a multi-model set for every agent. Then, the multi-agent model is established according to the multi-agent network topology and mutual-coupled constraint relations. Finally, we adopt a smooth start switching control strategy and a multi-agent distributed coordination control algorithm to ensure the synchronous speed tracking control of each agent. Simulation results on the actual CRH380A running data show the effectiveness of the proposed approach.

A linear transformation is proposed to deal with the consensus problem of high-order linear multi-agent systems (LMASs). In virtue of the linear transformation, the consensus problem is equivalently translated into a partial stability problem. We discuss three issues of the LMASs under a generalized linear protocol: 1) to find criteria of consensus convergence; 2) to calculate consensus function; 3) to design gain matrices in the linear consensus protocol. Precisely, we provide a necessary and sufficient criterion of consensus convergence in terms of Hurwitz stability of a matrix and give an analytical expression of the consensus function. In addition, we set up a relation between the gain matrices in the protocol and the convergence time and consensus accuracy of the agents, and then design the gain matrices with respect to a pre-specified convergence time and a required consensus accuracy.

This paper deals with the problem of designing robust sequential covariance intersection (SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance (ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.