This paper presents a hybrid controller that combines sliding-mode control (SMC) with a fuzzy strategy to regulate the vertical displacement of a bioinspired robotic dolphin. The structure of the robot and a simplified mathematical model for depth control are described. An SMC based on the line-of-sight guidance law and the sliding-mode observer is developed to overcome systematic uncertainties and environmental disturbances. The Lyapunov stability theory is utilized to analyze the stability and convergence properties of the closed-loop system. Depth control in the physical robot is realized by utilizing a fuzzy logic controller, which is represented as the mapping of the input propulsion forces/moments and output control parameters. Numerical and experimental results show that the proposed control strategy successfully steers the robot toward and along the desired depth.

In this note, we investigate a continuous-time distributed optimization problem with time-varying cost functions and affine formation constraints, which are described by the stress matrices rather than the standard Laplacians. The objective is to minimize the sum of local time-varying cost functions, each of which is known by only one individual agent. The optimal solution is a time-varying affine transformation of a nominal configuration rather than some constants. To tackle the difficulty caused by the dynamic aspect of the local cost functions and handle affine formation constraints, the fixed-time distributed estimator and distributed gradient tracking technique are developed respectively to compensate the time variation of solution trajectory and calculate the weighted sum of local gradients to eliminate the tracking error. The time-varying optimal solution trajectory is thus accurately tracked with the proposed estimator-based gradient tracking algorithm. Using appropriately chosen coefficients, the tracking error is guaranteed to vanish at an exponential rate. The proposed estimator-based gradient tracking algorithm is further validated through numerical simulations.

This paper handles the distributed cooperative fault detection problem for multiagent systems (MASs) with additive type of faults and external disturbances. A distributed cooperative fault detection scheme is proposed through which the strict requirement on single agent's performance can be released. Moreover, a mixed H _∞ /H ₂ optimization approach is presented to guarantee that the cooperative fault detection scheme can get a proper compromise between sensitivity to faults and robustness to disturbances on the network level. In order to be applicable with MASs, a distributed implementation of the proposed scheme is developed by applying an iterative approach. Effectiveness and improvements of the proposed scheme are validated on a multivehicle experimental platform.

This technical note studies a class of distributed nonsmooth convex consensus optimization problem. The cost function is a summation of local cost functions which are convex but nonsmooth. Each of the local cost functions consists of a twice differentiable convex function and two lower semi-continuous convex functions. We call this problem as single-smooth plus double-nonsmooth (SSDN) problem. Under mild conditions, we propose a distributed double proximal primal-dual optimization algorithm. The double proximal splitting is designed to deal with the difficulty caused by the unproximable property of the summation of those two nonsmooth functions. Besides, it can also guarantee that the proposed algorithm is locally Lipschitz continuous. An auxiliary variable in the double proximal splitting is introduced to estimate the subgradient of the second nonsmooth function. Theoretically, we conduct the convergence analysis by employing Lyapunov stability theory. It shows that the proposed algorithm can make the states achieve consensus at the optimal point. In the end, nontrivial simulations are presented and the results demonstrate the effectiveness of the proposed algorithm.

In this article, the distributed cooperative control problem of redundant mobile manipulators is investigated. A novel method is proposed to solve the problem by integrating formation control with constrained optimization, which not only transports the object along a reference trajectory in a distributed manner but also obtains the dexterous joint postures and end-effector displacements under safety constraints for collision avoidance. For the constrained optimization, the cost function and safety constraints are designed to quantify the mobility and manipulability of mobile manipulators, and collision-free working ranges with the object and obstacles, respectively. A discontinuous projected primal-dual algorithm with damping terms is proposed to solve the constrained optimization problem, providing the joint postures and end-effector displacements, which minimize the cost function and satisfy safety constraints. For the formation control, a finite-time control law, guided by end-effector displacements from the primal-dual algorithm, is developed in order to transport the object by establishing a prescribed formation and moving its centroid to track the reference trajectory. The cooperative manipulation is therefore achieved by the proposed method, which is further validated through numerical simulations.

Rigidity graph theory has found broad applications in engineering, architecture, biology and chemistry, while systematic and computationally tractable construction of rigid frameworks is still a challenging task. In this paper, starting from any given configuration in general positions, we show how to construct a universally rigid tensegrity framework by looking into the kernel of the tensegrity framework's stress matrix. As one application, we show how to stabilize a formation of mobile agents by assigning a universally rigid virtual tensegrity framework for the formation and then design distributed controllers based on the forces determined by the stresses of the edges. Such formation controllers are especially useful when one needs to satisfy formation constraints in the form of strict upper or lower bounds on inter-agent distances arising from tethered robots.