Erfu Yang

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

ESPACENET: A Framework of Evolvable and Reconfigurable Sensor Networks for Aerospace–Based Monitoring and Diagnostics

There is an increasing need to develop flexible, reconfigurable, and intelligent multi-spacecraft sensing networks for aerospace-based monitoring and diagnostics. Technical advancements in ad hoc networking, MEMS devices, low-power electronics, adaptive and reconfigurable hardware, micro-spacecraft, and micro-sensors have enabled the design and development of such highly integrated space wireless sensor networks. This paper proposes the framework for an evolvable sensor network architecture, investigated as part of the ESPACENET project, collocated at the University of Edinburgh, Essex, Kent and Surrey. The aim is to design a flexible and intelligent embedded network of reconfigurable piconodes optimised by a hierarchical multi-objective algorithm. Although the project is targeted at aerospace applications, the same intelligent network can be used for many earth bound applications such as environmental and medical diagnostics

Multi-Objective Evolutionary Optimizations of a Space-Based Reconfigurable Sensor Network under Hard Constraints

Wireless sensor networks have emerged as a promising way to develop high security systems. This paper presents the optimizations of a space-based reconfigurable sensor network under hard constraints by employing an efficient multi-objective evolutionary algorithm (MOEA). First, a system model is proposed for cluster-based space wireless sensor networks. Second, the statement of multi-objective optimization problems is mathematically formulated under multiple constraints. Third, the MOEA is used to find multi- criteria solutions in the sense of Pareto optimizations. Finally, simulation results are provided to illustrate the effectiveness of applying the MOEA to the multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints.

An Improved Particle Swarm Optimization Algorithm for Power-Efficient Wireless Sensor Networks

This paper presents an improved particle swarm optimization (PSO) algorithm for onboard embedded applications in power-efficient wireless sensor networks (WSNs) and WSN-based security systems. The objective is to keep the main advantages of the standard PSO algorithm, such as simple form, easy implementation, low algorithmic complexity, and low computational burden while the performance and efficiency can be significantly improved. Numerical experiments are performed on a very difficult benchmark function to validate the performance of the improved PSO algorithm. The results show that the improved PSO algorithm outperforms the standard PSO algorithm.

Fuzzy Policy Reinforcement Learning in Cooperative Multi-robot Systems

A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This algorithm is used to deal with some control problems in cooperative multi-robot systems. Specifically, a leader-follower robotic system and a flocking system are investigated. In the leader-follower robotic system, the leader robot tries to track a desired trajectory, while the follower robot tries to follow the reader to keep a formation. Two different fuzzy policies are developed for the leader and follower, respectively. In the flocking system, multiple robots adopt the same fuzzy policy to flock. Initial fuzzy policies are manually crafted for these cooperative behaviors. The proposed learning algorithm finely tunes the parameters of the fuzzy policies through the policy gradient approach to improve control performance. Our simulation results demonstrate that the control performance can be improved after the learning.

Dual-controller approach to three-dimensional autonomous formation control

The formation-keeping control problem is addressed for the three-dimensional autonomous formation flight of multiple aircraft. The full nonlinear kinematics model describing the relative position and orientation of the formation flight system is used to develop the nonlinear formation-keeping controllers. To deal with the input-output invertibility problem of the formation control system under consideration, a dual-controller approach is presented in this study. First, the original nonlinear formation system is decomposed into two subsystems. Next, the corresponding controller for each subsystem is developed. By invoking the nonlinear dynamic inversion-based control scheme and the well-known structure algorithm, an output-tracking controller with asymptotic stability is achieved for the first subsystem. The second subsystem is simple, and a relative roll angle-hold controller is designed to achieve an exponential convergence rate. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

A Biologically Inspired Vision-Based Approach for Detecting Multiple Moving Objects in Complex Outdoor Scenes

In the human brain, independent components of optical flows from the medial superior temporal area are speculated for motion cognition. Inspired by this hypothesis, a novel approach combining independent component analysis (ICA) with principal component analysis (PCA) is proposed in this paper for multiple moving objects detection in complex scenes—a major real-time challenge as bad weather or dynamic background can seriously influence the results of motion detection. In the proposed approach, by taking advantage of ICA’s capability of separating the statistically independent features from signals, the ICA algorithm is initially employed to analyze the optical flows of consecutive visual image frames. As a result, the optical flows of background and foreground can be approximately separated. Since there are still many disturbances in the foreground optical flows in the complex scene, PCA is then applied to the optical flows of foreground components so that major optical flows corresponding to multiple moving objects can be enhanced effectively and the motions resulted from the changing background and small disturbances are relatively suppressed at the same time. Comparative experimental results with existing popular motion detection methods for challenging imaging sequences demonstrate that our proposed biologically inspired vision-based approach can extract multiple moving objects effectively in a complex scene.

A suboptimal model predictive formation control

We investigate the leader-following formation control of mobile robots through the model predictive control (MPC) in this paper. We establish its control stability by adding a terminal state penalty to the cost function and a terminal state region to the optimisation constraints. We also design a terminal state region based on an input-output feedback linearisation controller for the MPC. A suboptimal stable solution is sought to reduce the computational time used in the MPC. Simulations on the control are provided to verify the proposed control strategy.

A filtering based recursive least squares estimation algorithm for pseudo-linear auto-regressive systems

This paper uses the filtering technique, transforms a pseudo-linear auto-regressive system into an identification model and presents a new recursive least squares parameter estimation algorithm pseudo-linear auto-regressive systems. The proposed algorithm has a high computational efficiency because the dimensions of its covariance matrices become small compared with the recursive generalized least squares algorithm.

Multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints

Wireless sensor networks have emerged as a promising way to develop high security systems. This paper presents the optimizations of a space-based reconfigurable sensor network under hard constraints by employing an efficient multi-objective evolutionary algorithm (MOEA). First, a system model is proposed for cluster-based space wireless sensor networks. Second, the statement of multi-objective optimization problems is mathematically formulated under hard constraints. Third, the MOEA is used to find multi-criteria solutions in the sense of Pareto optimality. Finally, simulation results are provided to illustrate the effectiveness of applying the MOEA to the multi-objective evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints.