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In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller. [ABSTRACT FROM AUTHOR]

This article proposes the fuzzy swarm control based on sliding-mode strategy with self-organized omnidirectional mobile robots (OMRs) system. In previous work, swarm control is realized by using sliding-mode strategy. However, chattering in the OMRs system is very serious. To overcome this drawback, a new fuzzy swarm control for OMRs system is designed, which introduces the fuzzy logic to replace switching control in traditional sliding-mode control (SMC). The fuzzy SMC (FSMC) can weaken chattering for sliding-mode strategy in the OMRs system. However, it is difficult to design swarm control law for OMRs because of model uncertainties, external disturbance and the introduction of fuzzy logic. The FSMC is proposed in this article and its stability is analyzed. Simulation and experiment results show that the fuzzy swarm control based on sliding-mode strategy with self-organized OMRs system can realize stability asymptotically and chattering is weakened. [ABSTRACT FROM AUTHOR]

In this work, a two-level mobile robot swarm system with self-organized formation network is proposed. Initially, based on the position information of the robots, a relation-invariable persistent formation (RIPF) algorithm can automatically organize the swarm network and construct an optimal persistent formation. At the upper formation planning level, the collision-free reference paths of the swarm can be planned for guiding the robots to reach and maintain a desired distance with their neighbors. Then, at the lower formation tracking control level, a neural-dynamic combined model predictive control (MPC) method is applied to drive the swarm moving on the reference paths. The MPC can reformulate the system into a convex minimization problem, which can further be transformed into a constrained quadratic programming (QP) problem such that an efficient QP solver, called primal-dual neural network (PDNN), is implemented to obtain the optimal control inputs online for the robots. In the end, simulation results show the effectiveness of the proposed formation system. [ABSTRACT FROM AUTHOR]

For constructing a distributed consensus formation scheme for the two-wheel mobile robots with directed communication topology and nonholonomic constraints, in this work, an integrated leader–follower consensus formation framework using neural-network-optimized distributed model predictive control (NNODMPC) strategy is presented. The proposed leader–follower formation framework can be regarded as integrating a consensus trajectories planning subsystem (CTPS) and a consensus tracking subsystem. The CTPS can plan the consensus trajectories for the mobile robots through a leader–follower structure, and in the consensus tracking subsystem, the robots are guided to the consensus. To simultaneously control these two subsystems, the NNODMPC based protocol is applied. The errors and constraints of the integrated system can be incorporated and formulated into a constrained quadratic programming (QP) problem whose optimal solution can be obtained by a primal–dual neural networks (PDNN) QP optimizer. Stability analysis illustrates the convergence of the proposed DMPC-based consensus formation system. The novelties of this work are rooted in the construction of a generalized consensus formation scheme for the practical wheeled robots with nonholonomic constraints, the formulation of an MPC-based distributed consensus controller and the consideration of system constraints. The experimental results on the mobile robots are conducted to verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]