Gewei Zuo

Gewei Zuo, Lijun Zhu

The event-triggered control with intermittent output can reduce the communication burden between the controller and plant side over the network. It has been exploited for adaptive output feedback control of uncertain nonlinear systems in the literature, however the controller must partially reside at the plant side where the computation capacity is required. In this paper, all controller components are moved to the controller side and their dynamics use sampled states rather than continuous one with the benefit of directly estimating next triggering instance of some conditions and avoiding constantly checking event condition at the controller side. However, these bring two major challenges. First, the virtual input designed in the dynamic filtering technique for the stabilization is no longer differentiable. Second, the plant output is sampled to transmit at plant side and sampled again at controller side to construct the controller, and the two asynchronous samplings make the analysis more involving. This paper solves these two issues by introducing a new state observer to simplify the adaptive law, a set of continuous companion variables for stability analysis and a new lemma quantifying the error bound between actual output signal and sampled transmitted output. It is theoretically guaranteed that all internal signals in the closed-loop system are semiglobally bounded and the output is practically stabilized to the origin. Finally, the numerical simulation illustrates the effectiveness of proposed scheme.

Gewei Zuo, Lijun Zhu, Yujuan Wang et al.

A finite-time protocol for a multi-agent systems (MASs) can guarantee the convergence of every agent in a finite time interval in contrast to the asymptotic convergence, but the settling time depends on the initial condition and design parameters and is inconsistent across the agents. In this paper, we study the prescribed-time cooperative output regulation (PTCOR) problem for a class of linear heterogeneous MASs under a directed communication graph, where the settling time of every agent can be specified a priori and thus consistent. As a special case of PTCOR, the necessary and sufficient condition for prescribed-time output regulation of an individual system is first discussed. Then, the PTCOR problem is converted into two cascaded subsystem, where the first one composed of distributed estimate errors and local estimate errors and the second one is for local tracking errors. The criterion for prescribed-time stabilization of the cascaded system is proposed and is found to be different from that of traditional asymptotic stabilization of a cascaded system. Under the criterion and sufficient condition, the general PTCOR problem is studied in two scenarios including state feedback control and measurement output feedback control. In particular, a distributed prescribed-time observer for each subsystem is explicitly constructed to estimate the exosystem's state. Based on the observer, a distributed controller is proposed to achieve convergence of the regulated output to zero within a prescribed-time.

Gewei Zuo, Lijun Zhu, Yujuan Wang et al.

In this paper, we address the distributed prescribed-time convex optimization (DPTCO) problem for a class of nonlinear multi-agent systems (MASs) under undirected connected graph. A cascade design framework is proposed such that the DPTCO implementation is divided into two parts: distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Changing Lyapunov function method and time-varying state transformation method together with the sufficient conditions are proposed to prove the prescribed-time stabilization of the cascaded system as well as the uniform boundedness of internal signals in the closed-loop systems. The proposed framework is then utilized to solve robust DPTCO problem for a class of chain-integrator MASs with external disturbances by constructing a novel variables and exploiting the property of time-varying gains. The proposed framework is further utilized to solve the adaptive DPTCO problem for a class of strict-feedback MASs with parameter uncertainty, in which backstepping method with prescribed-time dynamic filter is adopted. The descending power state transformation is introduced to compensate the growth of increasing rate induced by the derivative of time-varying gains in recursive steps and the high-order derivative of local reference trajectory is not required. Finally, theoretical results are verified by two numerical examples.

Gewei Zuo, Mengmou Li, Lijun Zhu

In this paper, we address the distributed prescribed-time convex optimization (DPTCO) for a class of networked Euler-Lagrange systems under undirected connected graphs. By utilizing position-dependent measured gradient value of local objective function and local information interactions among neighboring agents, a set of auxiliary systems is constructed to cooperatively seek the optimal solution. The DPTCO problem is then converted to the prescribed-time stabilization problem of an interconnected error system. A prescribed-time small-gain criterion is proposed to characterize prescribed-time stabilization of the system, offering a novel approach that enhances the effectiveness beyond existing asymptotic or finite-time stabilization of an interconnected system. Under the criterion and auxiliary systems, innovative adaptive prescribed-time local tracking controllers are designed for subsystems. The prescribed-time convergence lies in the introduction of time-varying gains which increase to infinity as time tends to the prescribed time. Lyapunov function together with prescribed-time mapping are used to prove the prescribed-time stability of closed-loop system as well as the boundedness of internal signals. Finally, theoretical results are verified by one numerical example.

Yaohang Xu, Lianjie Ma, Gewei Zuo et al.

Reinforcement Learning (RL) and Imitation Learning (IL) are the standard frameworks for policy acquisition in manipulation. While IL offers efficient policy derivation, it suffers from compounding errors and distribution shift. Conversely, RL facilitates autonomous exploration but is frequently hindered by low sample efficiency and the high cost of trial and error. Since existing hybrid methods often struggle with complex tasks, we introduce Mixture of RL and IL Experts (MoRI). This system dynamically switches between IL and RL experts based on the variance of expert actions to handle coarse movements and fine-grained manipulations. MoRI employs an offline pre-training stage followed by online fine-tuning to accelerate convergence. To maintain exploration safety and minimize human intervention, the system applies IL-based regularization to the RL component. Evaluation across four complex real-world tasks shows that MoRI achieves an average success rate of 97.5% within 2 to 5 hours of fine-tuning. Compared to baseline RL algorithms, MoRI reduces human intervention by 85.8% and shortens convergence time by 21%, demonstrating its capability in robotic manipulation.