Fuzzy sliding-mode formation control for multirobot systems: design and implementation

The Design and the Development of a Biped Robot Cooperation System

The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together.

Distributed Sliding-Mode Tracking Control of Second-Order Nonlinear Multiagent Systems: An Event-Triggered Approach

The event-triggered tracking control problem of second-order multiagent systems in consideration of system nonlinearities is investigated by utilizing the distributed sliding-mode control (SMC) approach. An event-triggered strategy is proposed to decrease the controller sampling frequency and save the network communication resources; the triggering condition is then established for leader-following multiagent systems. In this article, by utilizing the distributed event-based sliding-mode controller, the system state of second-order multiagent systems with system nonlinearities is capable of approaching the integral sliding-mode surface in finite time. A novel integral sliding-mode surface is constructed in this article to guarantee the consensus tracking performance in the existence of system nonlinearities as the state trajectories of second-order integrator systems move on the constructed sliding manifold. By employing the Lyapunov approach, sufficient conditions are deduced to ensure that the consensus tracking performance is obtained for the closed-loop system. Furthermore, it is presented that the triggering scheme can effectively reduce state updates and eliminate the Zeno behavior. A simulation example is provided to testify the validity of our proposed methodology.

Lyapunov-Based Formation Control of Underwater Robots

Today, automatic diving robots are used for research, inspection, and maintenance, extensively. Control of autonomous underwater robots (AUVs) is challenging due to their nonlinear dynamics, uncertain models, and the system underactuation. Data collection using underwater robots is increasing within the oceanographic research community. Also, the ability to navigate and cooperate in a group of robots has many advantages compared with individual navigations. Among them, the effectiveness of using resources, the possibility of robots’ collaboration, increasing reliability, and robustness to defects can be pointed out. In this paper, the formation control of underwater robots has been studied. First, the kinematic model of the AUV is presented. Next, a novel Lyapunov-based tracking control algorithm is investigated for the leader robot. Subsequently, a control law is designed using Lyapunov theory and feedback linearization techniques to navigate a group of follower robots in a desired formation associated with the leader and follow it simultaneously. In the obtained results for different reference paths and various formations, the effectiveness of the proposed algorithm is represented.

High-Gain Observer-Based Neural Adaptive Feedback Linearizing Control of a Team of Wheeled Mobile Robots

This paper addresses the neural network (NN) output feedback formation tracking control of nonholonomic wheeled mobile robots (WMRs) with limited voltage input. A desired formation is achieved based on the leader–follower strategy utilizing hyperbolic tangent saturation functions to reduce the risk of actuator saturation. The controller is developed by incorporating the high-gain observer and radial basis function (RBF) NNs using the inverse dynamics control technique. The high-gain observer is introduced to estimate velocities of the followers. The RBF NN preserves the robustness of the proposed controller against uncertain nonlinearities. The adaptive laws are also combined by a robust control term to estimate the weights of RBF NN, approximation errors, and bounds of unknown time-variant environmental disturbances. A Lyapunov-based stability analysis proves that all signals of the closed-loop system are bounded, and tracking errors are uniformly ultimately bounded. Finally, some simulations are carried out to show the effectiveness of the proposed controller for a number of WMRs.

A Barycentric Coordinate-Based Approach to Formation Control Under Directed and Switching Sensing Graphs

This paper investigates two formation control problems for a leader-follower network in 3-D. One is called the formation marching control problem, the objective of which is to steer the agents to maintain a target formation shape while moving with the synchronized velocity. The other one is called the formation rotating control problem, whose goal is to drive the agents to rotate around a common axis with a target formation. For the above two problems, we consider directed and switching sensing topologies while the communication is assumed to be bidirectional and switching. We develop approaches utilizing barycentric coordinates toward these two problems. Local control laws and graphical conditions are acquired to ensure global convergence in both scenarios.

Formation Control With Size Scaling Via a Complex Laplacian-Based Approach

We consider the control of formations of a leader-follower network, where the objective is to steer a team of multiple mobile agents into a formation of variable size. We assume that the shape description of the formation is known to all the agents, which is captured by a complex-valued Laplacian associated with the sensing graph, but the size scaling of the formation is not known or only known to two agents, called the leaders in the network. A distributed linear control strategy is developed in this paper such that the agents converge to the desired formation shape, for which the size of the formation is determined by the two leaders. Moreover, in order to make all agents in a formation move with a common velocity, the distributed control law also incorporates a velocity consensus component, which is implemented with the help of a communication network that may, in general, be of different topology from the sensing graph. Both the setup of single-integrator kinematics and the one of double-integrator dynamics are addressed in the same framework except that the acceleration control in the double-integrator setup has an extra damping term.

Robust adaptive fuzzy controller with supervisory compensator for MEMS gyroscope sensor

In this paper, a robust adaptive fuzzy controller is proposed to improve the robustness and position tracking of a MEMS gyroscope sensor. The proposed controller is designed as an indirect adaptive fuzzy controller with a supervisory compensator. It incorporates a fuzzy inference system with an adaptive controller in a unified Lyapunov framework, which can approximate and compensate for the unknown system dynamics and nonlinearities in the MEMS gyroscope. The parameters of the membership functions in the fuzzy controller can be adjusted online based on the Lyapunov method. Moreover, a supervisory controller is employed to guarantee the asymptotic stability of the closed-loop system and boundedness of the state variables in the MEMS gyroscope model. Numerical simulations demonstrate the proposed robust adaptive fuzzy controller has satisfactory tracking performance and robustness in the presence of external disturbances.

Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network

The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.

A consensus-based multi-agent approach for estimation in robust fault detection

This paper is devoted to distributed estimation in robust fault detection for sensor networks with networked-induced delays and packet dropouts by using a consensus-based multi-agent approach. Utilizing the information interaction and coordination among the neighboring networks based on multi-agent theory, we design novel and multiple agent-based robust fault detection filters (RFDFs) subject to only partial estimated and measured information. Asymptotically stable sufficient conditions for the innovative constructed filters are derived in the form of linear matrix inequality (LMI) and the threshold fit for each agent-based RFDF is determined. An illustrative example is given to demonstrate the effectiveness of the consensus-based multi-agent approach for the estimation in robust fault detection.

Distributed finite-time containment control for double-integrator multiagent systems

In this paper, the distributed finite-time containment control problem for double-integrator multiagent systems with multiple leaders and external disturbances is discussed. In the presence of multiple dynamic leaders, by utilizing the homogeneous control technique, a distributed finite-time observer is developed for the followers to estimate the weighted average of the leaders' velocities at first. Then, based on the estimates and the generalized adding a power integrator approach, distributed finite-time containment control algorithms are designed to guarantee that the states of the followers converge to the dynamic convex hull spanned by those of the leaders in finite time. Moreover, as a special case of multiple dynamic leaders with zero velocities, the proposed containment control algorithms also work for the case of multiple stationary leaders without using the distributed observer. Simulations demonstrate the effectiveness of the proposed control algorithms.

Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm

In this paper, a self-organizing fuzzy-neural-network with adaptive computation algorithm (SOFNN-ACA) is proposed for modeling a class of nonlinear systems. This SOFNN-ACA is constructed online via simultaneous structure and parameter learning processes. In structure learning, a set of fuzzy rules can be self-designed using an information-theoretic methodology. The fuzzy rules with high spiking intensities (SI) are divided into new ones. And the fuzzy rules with a small relative mutual information (RMI) value will be pruned in order to simplify the FNN structure. In parameter learning, the consequent part parameters are learned through the use of an ACA that incorporates an adaptive learning rate strategy into the learning process to accelerate the convergence speed. Then, the convergence of SOFNN-ACA is analyzed. Finally, the proposed SOFNN-ACA is used to model nonlinear systems. The modeling results demonstrate that this proposed SOFNN-ACA can model nonlinear systems effectively.

Robust adaptive control of MEMS triaxial gyroscope using fuzzy compensator

In this paper, a robust adaptive control strategy using a fuzzy compensator for MEMS triaxial gyroscope, which has system nonlinearities, including model uncertainties and external disturbances, is proposed. A fuzzy logic controller that could compensate for the model uncertainties and external disturbances is incorporated into the adaptive control scheme in the Lyapunov framework. The proposed adaptive fuzzy controller can guarantee the convergence and asymptotical stability of the closed-loop system. The proposed adaptive fuzzy control strategy does not depend on accurate mathematical models, which simplifies the design procedure. The innovative development of intelligent control methods incorporated with conventional control for the MEMS gyroscope is derived with the strict theoretical proof of the Lyapunov stability. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances compared with conventional adaptive control method.