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In this paper, we study the exponential attitude tracking and disturbance rejection problem of rigid body systems. We design a control law for the rigid body system that ensures the exponential convergence of the attitude tracking error and the angular velocity tracking error in the presence of unknown external disturbances. Furthermore, by tuning some design parameters, the exponential convergence rate can be made arbitrarily fast. The effectiveness of our approach is validated by a numerical example. [ABSTRACT FROM AUTHOR]

The existing results on the adaptive distributed observer for an uncertain leader system are restricted to connected static networks. In this article, we extend the existing results to acyclic every‐time connected switching networks. As an application of the adaptive distributed observer, we solve the leader‐following attitude consensus problem of multiple rigid body systems with an uncertain leader system over an acyclic every‐time connected switching network. A numerical example is provided to illustrate our design. [ABSTRACT FROM AUTHOR]

In this paper, we study the leader-following attitude consensus problem of multiple rigid body systems by a sampled-data distributed observer. Comparing with the continuous-time distributed observer, the sampled-data distributed observer consumes fewer communication resources and is less vulnerable to such communication failures as network congestions and packet dropouts. The sampled-data distributed observer consists of two parts. The first part is a sampled-data distributed observer for the angular velocity leader subsystem and the second part is a sampled-data distributed observer for the attitude leader subsystem. It is noted that, since the attitude leader subsystem is nonlinear, a special effort has to be made to establish the sampled-data distributed observer for the attitude leader subsystem. Moreover, we give an explicit upper bound for the sampling intervals to guarantee the validity of the sampled-data distributed observer. On the basis of the sampled-data distributed observer, we further synthesize a distributed control law to solve the leader-following consensus problem. The effectiveness of our approach is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

A standard assumption on the existence of the distributed observer or adaptive distributed observer for a linear leader system over jointly connected switching networks is that none of the eigenvalues of the system matrix of the leader system have positive real parts. In this paper, after establishing a stability result for a class of linear periodic switched systems, we present sufficient conditions for the existence of both the distributed observer and the adaptive distributed observer for general linear leader systems over periodic jointly connected switching networks. As an application of the distributed observer, we solve the cooperative output regulation problem for linear multi-agent systems with general linear leader systems over periodic jointly connected switching networks. A numerical example is provided to illustrate our design. [ABSTRACT FROM AUTHOR]

Current regenerative braking systems in electric vehicles have several problems, such as complex structures, too many control parameters, and inconsistent braking responses. To solve these problems, a control algorithm with multidisciplinary design optimization (MDO) is proposed based on the novel regenerative–mechanical coupled brake-by-wire system. A dynamic model of the novel regenerative braking system was established to analyze the mechanism of coupled braking and propose a braking torque distribution strategy. To realize a better balance between the optimum braking stability and the maximum regenerative energy recovery based on the braking torque distribution strategy and sample points, the MDO mathematical model was developed to optimize the control parameters with the collaborative optimization algorithm. The finite sample points comprising the vehicle speed, battery state-of-charge, and braking severity were obtained through an optimal Latin hypercube design and represent the overall design space. A network was established based on the sample points and the optimization results. Using this network, the in-depth characteristics of the sample points and the optimization results were obtained through supervised learning to develop the control algorithm for vehicle braking. A simulation was performed using the normal braking condition, and the simulation results demonstrated that the control algorithm has higher control precision than conventional methods and better real-time performance than online optimization. [ABSTRACT FROM AUTHOR]