Haibo Ji

Adaptive output-feedback tracking of stochastic nonlinear systems

We address the adaptive stabilization and tracking problems for a class of output feedback canonical systems driven by Wiener noises of unknown covariance. Filtered transformation and backstepping techniques are employed in the stochastic control design. We obtain two adaptive controllers that guarantee the global stability in probability for vanishing perturbations or the input-to-state stability in probability for nonvanishing perturbations respectively. The tracking error can converge to a small residual set around the origin in the sense of mean quartic value.

Integrated guidance and control for dual-control missiles based on small-gain theorem

An integrated guidance and control (IGC) design approach is proposed based on small-gain theorem for missiles steered by both canard and tail controls. The angle of attack and pitch rate commands, which are aimed at producing desired aerodynamic lift to achieve robust tracking of a maneuvering target, are generated by a guidance law that is designed using input-to-state stability (ISS) theory. An IGC law is developed utilizing generalized small-gain theorem to enforce the commands, and it can be shown that both the line-of-sight (LOS) rate and the tracking error are input-to-state practically stable (ISpS) with respect to target maneuvers and missile model uncertainties. The algorithm is tested using computer simulations against a maneuvering target.

Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.

Leader-following consensus of multi-agent systems under directed communication topology via distributed adaptive nonlinear protocol

Abstract In this paper, we study the leader-following consensus problem of general linear multi-agent systems under directed communication topology. To avoid using any global information, an adaptive nonlinear protocol is proposed based only on the relative state information. It is proved that, for any directed communication graph that contains a spanning tree with the root node being the leader agent, the proposed control law solves the leader-following consensus problem. A numerical example is provided to illustrate the effectiveness of the theoretical results.

Guidance Laws Based on Input-to-State Stability and High-Gain Observers

A novel three-dimensional guidance law based on input-to-state stability (ISS) and high-gain observers for interception of maneuvering targets is proposed. The ISS method is introduced to design a guidance law to achieve robust tracking of a maneuvering target. Since in practice the line-of-sight (LOS) rate is difficult for a pursuer to measure accurately, a high-gain observer is utilized to estimate it. Stability analysis as well as simulation results show that the presented approach is effective.

Adaptive multi-agent containment control with multiple parametric uncertain leaders

In this paper, an adaptive containment control is considered for a class of multi-agent systems with multiple leaders containing parametric uncertainties. The agents are heterogeneous though their dynamics have the same relative degree and are minimum phase, while the interconnection topology is described by a general directed graph. A distributed containment control is proposed for the agents to enter the moving convex set spanned by the leaders, based on an adaptive internal model and a recursive stabilization control law.

Leader-following consensus for an integrator-type nonlinear multi-agent systems using distributed adaptive protocol

This paper deals with the leader-following consensus problem for a class of multi-agent systems with nonlinear dynamics and directed communication topology. Two distributed adaptive nonlinear control laws are proposed based on the relative state information between neighboring agents, which solve the leader-following consensus problem and the inverse optimal adaptive leader-following consensus problem, respectively. Compared with the existing results in the literature, we do not put any restrictions on nonlinear functions and the adaptive consensus protocols are in a fully distributed fashion. A numerical example is given to verify the theoretical analysis.

Leader-following consensus for a class of second-order nonlinear multi-agent systems☆

Abstract This paper deals with the leader-following consensus problem for a class of multi-agent systems with nonlinear dynamics and directed communication topology. The control input of the leader agent is assumed to be unknown to all follower agents. A distributed adaptive nonlinear control law is constructed using the relative state information between neighboring agents, which achieves leader-following consensus for any directed communication graph that contains a spanning tree with the root node being the leader agent. Compared with previous results, the nonlinear functions are not required to satisfy the globally Lipschitz or Lipschitz-like condition and the adaptive consensus protocol is in a distributed fashion. A numerical example is given to verify our proposed protocol.

Adaptive Stabilization for Stochastic Parametric-Strict-Feedback Systems with Wiener Noises of Unknown Covariance

An adaptive control design for a class of stochastic parametric-strict-feedback systems is given. The disturbance considered herein is Wiener noise of unknown covariance, which is represented as a simplified unknown parameter. By using stochastic Lyapunov method and backstepping techniques, an adaptive controller was obtained that guaranteed the global asymptotic stabilization in probability.

Robust almost output consensus in networks of nonlinear agents with external disturbances

This paper considers robust almost output consensus control for heterogeneous and disturbed multi-agent nonlinear systems by distributed output feedback control. Taking the effect of inherent unmodeled disturbances into account, we develop a reduced order observer based consensus protocol with a performance constraint. Our study not only encompasses internal asymptotic output consensus, but also assures certain disturbance attenuation to the closed-loop system. Substantial difficulties due to nonidentical relative degrees and directed interaction graphs are surmounted in this result. Moreover, we show that in the linear case, it comes up with a novel H ∞ almost output consensus design that improves some results recently developed in the literature.