Dongbing Gu

Receding horizon tracking control of wheeled mobile robots

In this paper, a receding horizon (RH) controller is developed for tracking control of a nonholonomic mobile robot. The control stability is guaranteed by adding a terminal-state penalty to the cost function and constraining the terminal state to a terminal-state region. The stability analysis in the terminal-state region is investigated, and a virtual controller is found. The analysis results show that the RH tracking control has simultaneous tracking and regulation capability. Simulation results are provided to verify the proposed control strategy. It is shown that the control strategy is feasible.

Neural Predictive Control for a Car-like Mobile Robot

This paper presents a new path-tracking scheme for a car-like mobile robot based on neural predictive c ontrol. A multi-layer back-propagation n eural network is employed to model non-linear kinematics of the robot i nstead o f a linear r egression estimator in o rder to adapt t he robot t o a large operating range. The neural predictive c ontrol for path tracking is a model-based p redictive c ontrol based on neural network modelling, which can generate its output in term of the robot kinematics and a desired p ath. The desired p ath for the robot i s produced b y a polar polynomial with a simple c losed form. The multi-layer back-propagation n eural network is constructed b y a wavelet orthogonal decomposition to form a wavelet neural network that can o vercome the problem caused b y the local minima when training the neural network. The wavelet neural network has the a dvantage of using an explicit way to d etermine the number of the hidden nod es and initial value of weights. Simulation results for the modelling and control are provided to justify the proposed scheme.

Distributed EM Algorithm for Gaussian Mixtures in Sensor Networks

This paper presents a distributed expectation-maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbors and estimate global sufficient statistics in each node. By using this consensus filter, each node can gradually diffuse its local information over the entire network and asymptotically the estimate of global sufficient statistics is obtained. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximize the log-likelihood in the same way as in the standard EM algorithm. Because the consensus filter only requires that each node communicate with its neighbors, the distributed EM algorithm is scalable and robust. It is also shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm. Thus, it converges to a local maximum of the log-likelihood. Several simulations of sensor networks are given to verify the proposed algorithm.

A Differential Game Approach to Formation Control

This paper presents a differential game approach to formation control of mobile robots. The formation control is formulated as a linear-quadratic Nash differential game through the use of graph theory. Finite horizon cost function is discussed under the open-loop information structure. An open-loop Nash equilibrium solution is investigated by establishing existence and stability conditions of the solutions of coupled (asymmetrical) Riccati differential equations. Based on the finite horizon open-loop Nash equilibrium solution, a receding horizon approach is adopted to synthesize a state-feedback controller for the formation control. Mobile robots with double integrator dynamics are used in the formation control simulation. Simulation results are provided to justify the models and solutions.

Cooperative Target Tracking Control of Multiple Robots

This paper focuses on the problem of moving target tracking with a group of mobile robots. Each robot in the group has a pan/tilt camera to detect the target and has limited communication capability to communicate with neighbor robots. The problem is solved by separating it into two parts. One part is the estimation of target position and another is the flocking control of multiple robots moving toward the estimated position. In the target estimation part, we propose to use a novel distributed Kalman filter to estimate the target position. The distributed Kalman filter is deduced based on a standard Kalman filter by modeling the neighbors information as one of measurements. In the motion control part, a distributed flocking algorithm is developed. It is used to track the estimated target and avoid collision. In both parts, only local communication between neighbor robots is required. Finally, the tracking algorithms are simulated with 2-D and 3-D robots to verify their performance. The simulation results provide a firm conclusion that the proposed algorithms are able to track a moving target. A group of real ground mobile robots is used to test the proposed algorithm. The experiment results show that multiple robots are able to cooperate to track the target under the proposed algorithms and the tracking result outperforms the result produced by individual robots without cooperation.

Leader–Follower Flocking: Algorithms and Experiments

In this paper, we investigate a leader-follower flocking system where few members are group leaders who have global knowledge (a desired trajectory), while majority of the members are group followers who can communicate with neighbors but do not have global knowledge. The followers do not even know who the leaders are in the group. The flocking group is able to track a specific trajectory led by group leaders. In this system, all group members estimate the position of flocking center by using a consensus algorithm via local communication in order to keep the flocking group connected. Based on the estimated position of flocking center, a leader-follower flocking algorithm is proposed, and its stability is proved. A group of real robots ldquowifibotsrdquo are used to test the feasibility of the algorithm. Experiments show that this leader-follower flocking system can track the desired trajectory led by group leaders.

Nonlinear Formation-Keeping and Mooring Control of Multiple Autonomous Underwater Vehicles

This paper deals with the nonlinear formation-keeping and mooring control of multiple autonomous underwater vehicles (AUVs) in chained form. The AUV formation under consideration is constrained by the desired separations and orientations of follower AUVs with respect to a time-varying leader AUV. First, a time-varying, smooth feedback control law for the formation-keeping of multiple nonholonomic AUVs is presented by taking advantage of the Lyapunov direct method. Its asymptotical convergence to a desired formation trajectory prescribed by a leader-follower pair is guaranteed. Second, a time-varying, smooth feedback control law with asymptotic stability is designed to collaboratively moor the follower AUV to its desired docking position and orientation with respect to the leader by using the integrator backstepping method. Third, the realization problems of physical AUV system and singularity avoidance are investigated for applying the aforementioned control laws to a real formation system of AUVs. Finally, simulation results are provided to illustrate the effectiveness of the proposed control laws

Distributed Particle Filter for Target Tracking

This paper investigates target tracking using a distributed particle filter over sensor networks. Gaussian mixture model is adopted to approximate the posterior distribution of weighted particles in this distributed particle filter. The parameters of Gaussian mixture model are exchanged between neighbor sensor nodes. Each node can obtain the Gaussian mixture model representing particles posterior distribution through the parameter exchange. With the posterior distribution, the distributed particle filter can draw particles from it, predicted particles and observations, update particle weights, and re-sample particles based the predicted weights. The parameter exchange is key to implement the distributed operation. It is implemented by using an average consensus filter. Through this consensus filter, each sensor node can gradually diffuse its local statistics of weighted particles over the entire network and asymptotically obtain the estimated global statistics. The parameters of Gaussian mixture model can be calculated by using the estimated global statistics. Because the average consensus filter only requires that each sensor node communicate with its neighbors, the proposed distributed particle filter is scalable and robust. Simulations of tracking tasks in a sensor network with 100 sensor nodes are given.

Consensus based distributed particle filter in sensor networks

This paper presents a distributed particle filter over sensor networks. We propose two major steps to make a particle filter to work in a distributed way. The first step is the estimation of global mean and covariance of weighted particles by using an average consensus filter. The second step is the propagation of the estimated global mean and covariance through state transition distribution and likelihood distribution by using an unscented transformation. Through this transformation, partial high order information of the estimated global mean and covariance can be incorporated into the estimates for non-linear models. Simulations of tracking tasks in a sensor network with 100 sensor nodes are given.

Using Fuzzy Logic to Design Separation Function in Flocking Algorithms

Flocking algorithms essentially consist of three components: alignment, cohesion, and separation. To track a desired trajectory, the flock center should move along the desired trajectory, and thus, the fourth component, navigation, is necessary. The alignment, cohesion, and navigation components are well implemented through consensus protocols and tracking controls, while the separation component is designed through heuristic-based approaches. This paper proposes a fuzzy logic solution to the separation component. The TS rules and Gaussian membership functions are used in fuzzy logic. For fixed network flocking, a standard stability proof by using LaSalles invariance principle is provided. For dynamic network flocking, a Filipov solution definition is given for nonsmooth dynamics. Then, a LaSalles invariance principle for nonsmooth dynamics is used to prove the stability. A group of mobile robots with double integrator dynamics is simulated for the flocking algorithms in a 2-D environment.