Maneuvering and control of a biomimetic autonomous underwater vehicle

This work presents an approach for maneuvering and controlling a biomimetic autonomous underwater vehicle (BAUV). The BAUV swims forward by oscillating its body and caudal fin. It turns by bending its body and caudal fin toward the intended direction of motion. A body-spline function is specified by a set of parameters. Genetic algorithms are then used to find the values of the parameter by evaluating a fitness function over several swimming trials in a water tank. The fitness function is defined as the ratio of the kinetic energy of the forward motion to the required driving power of the joint motors. A control law that uses the oscillating frequency to control the forward speed, and applies a body-spline offset parameter to control the yawing rate is proposed. Moving averages of swimming speeds and heading angles are utilized as feedback signals to control the forward speed and heading angle of the BAUV. The effectiveness of the control algorithm is experimentally confirmed.

基于模糊技术的水下机器人运动控制

发展水下机器人有着重大而深远的意义,水下机器人的运动控制是实现其智能化的关键,是国内外研究的热点。本文将模糊技术应用于水下机器人,根据水下机器人的六自由度运动模型,设计了水下机器人模糊控制器与PID控制器,并进行了仿真环境下的试验比较。试验结果表明,基于模糊技术的方法显然有较大优势,更有利于控制水下机器人的运动。     

Pseudo-fuzzy discrete-event simulation for on-line production control

In this paper we propose an alternative use of commercial object-oriented discrete-event simulators. We attempt to bridge the intrinsic inaccuracy of simulators in modelling real systems affected by fuzziness. The strategy adopted, called pseudo-fuzzy discrete event simulation, models fuzziness through a set of several classic simulation runs to trace an output fuzzy performance function. The idea behind the approach proposed is to use the simulator as a fuzzy operator, which embeds some stochastic functions.A benchmark industrial setting has been used to build a reference simulation model and perform evaluations of the simulation strategy proposed for a specific working case.     

CPG-based control of a turtle-like underwater vehicle

This paper presents biologically inspired control strategies for an autonomous underwater vehicle (AUV) propelled by flapping fins that resemble the paddle-like forelimbs of a sea turtle. Our proposed framework exploits limit cycle oscillators and diffusive couplings, thereby constructing coupled nonlinear oscillators, similar to the central pattern generators (CPGs) in animal spinal cords. This paper first presents rigorous stability analyses and experimental results of CPG-based control methods with and without actuator feedback to the CPG. In these methods, the CPG module generates synchronized oscillation patterns, which are sent to position-servoed flapping fin actuators as a reference input. In order to overcome the limitation of the open-loop CPG that the synchronization is occurring only between the reference signals, this paper introduces a new single-layered CPG method, where the CPG and the physical layers are combined as a single layer, to ensure the synchronization of the physical actuators in the presence of external disturbances. The key idea is to replace nonlinear oscillators in the conventional CPG models with physical actuators that oscillate due to nonlinear state feedback of the actuator states. Using contraction theory, a relatively new nonlinear stability tool, we show that coupled nonlinear oscillators globally synchronize to a specific pattern that can be stereotyped by an outer-loop controller. Results of experimentation with a turtle-like AUV show the feasibility of the proposed control laws.     

水下航行器智能航向滑模控制

为了实现对自主水下航行器参考航向的平滑、快速跟踪,提出了一种基于干扰观测器的航向跟踪自适应反演滑模控制算法。针对水池试验中不同程度的水流干扰,利用一种干扰观测器观测系统的不确定性和外部干扰,未观测到的部分干扰采用自适应滑模控制器进行补偿。控制器的设计消除了传统滑模控制的"抖振"现象,且保证了闭环系统的稳定性。同时该控制系统可智能选取控制策略,通过判断水流循环等外界干扰的程度,自动选取合适的控制策略,最终消除外界干扰,提高了跟踪的稳定性和快速性。仿真结果表明,该控制策略能很好地实现智能航向跟踪控制,使跟踪误差在有限时间内快速收敛到零,对外界扰动的变化具有强鲁棒性和良好的自适应性。     

自主水下航行器模糊自校正航迹控制

为解决自主水下航行器AUV航迹控制问题,提出了一种基于模糊增益调节的模糊自校正控制方法。该方法完全使用马丹尼型模糊控制器实现,包括1个基本模糊控制器和3个模糊增益调节装置。基本模糊控制器用于实现AUV航迹保持,模糊增益调节装置为基本模糊控制器提供可以实时调整的量化因子。分别对模糊自校正控制方法和基本模糊控制方法进行了对比仿真试验,试验结果显示模糊自校正控制方法相比于基本模糊控制方法,其控制效果更好,特别是对海流扰动具有更强的适应性。     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Hierarchical discrete-event simulation on hypercube architectures

The simulation of systems that include components at varying levels of abstraction is addressed. A performance model of a hierarchical discrete-event simulation algorithm running on a hypercube architecture is presented. The model allows the performance impact of decisions made in the design of the parallel processor as well as in the design of the simulation algorithm to be examined. Three static component partitioning strategies are considered: random partitioning, heuristic partitioning, and simulated annealing. The performance model is applied to digital system simulation     

Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control

This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.     

基于多传感器信息融合的水下运动体速度测量

针对水下运动体测速中存在的难度大、精度低等问题,提出一种基于加速度计和机械测速装置的融合测速方法。该方法实质上是一种改良的模糊自适应卡尔曼滤波,在常规卡尔曼滤波的基础上,引入了自适应参数,并通过模糊控制器对残差的监控来实时调整自适应参数。经Matlab仿真和实际试验证实:该方法可以有效地提高卡尔曼滤波器的跟踪性,并改善滤波效果,适用于通用的水下运动体测速。     

A coordinated control of an underwater vehicle and robotic manipulator

Remotely operated underwater robotic vehicles (URVs) have been used for various tasks: inspection, recovery, construction, etc. With the increased utilization of remotely operated vehicles in subsea applications, the development of autonomous vehicles becomes highly desirable to enhance operator efficiency. However, engineering problems associated with the high density, nonuniform and unstructured seawater environment, and the nonlinear response of the vehicle make a high degree of autonomy difficult to achieve. The vehicles are usually equipped with mechanical manipulators that are utilized during the working mode. The accurate performance of the vehicle during the working mode can be achieved by controlling the vehicle and manipulator at the same time and compensating the end-effector error due to the vehicle motion. This article describes an adaptive control strategy for the coordinated control of an underwater vehicle and its robotic manipulator. The effectiveness of the control system is investigated by case study. The results show that the presented control system can provide the high performance of the vehicle and manipulator in the presence of unpredictable changes in the dynamics of the vehicle and its environment.     

Strategies for simultaneous multiple autonomous underwater vehicle operation and control

Under sampling of the coastal oceans remains a persistent problem for standard oceano-graphic measurement practice wherein an instrument package is tethered to a research vessel. The overhead costs associated with operating a large research vessel impose a strict minimum on the cost of data collected. Owing to the overheads, significant improvements in sampling technology on the tethered platform can only produce modest gains in the cost effectiveness. In contrast, untethered vehicles if operated simultaneously have the potential to increase cost effectiveness significantly by distributing the overhead costs over several sampling platforms. Furthermore, synoptic and pseudosynoptic data can be collected with multiple autonomous underwater vehicles (AUVs), thereby providing the type of information critical to dynamic process modeling unattainable with non-synoptic data. While the goal of simultaneous multiple-vehicle operation has been espoused over the last few years, AUV technology and practice have until recently been too immature to realize that potential. Recently, Florida Atlantic University (FAU) has developed a new series of modular AUVs with the express purpose of supporting multiple sensors and multiple-vehicle operation. This series of vehicle is called the Ocean Explorer of which three have been produced so far. This paper will explore some of the associated navigation, tracking, control and deployment problems associated with multiple-vehicle operation in coastal applications. In addition, the characteristics of the component level intelligent distributed control system, integrated data logger and vehicle control system will be discussed. In particular this paper will discuss how FAU has applied the concepts of elastic constraint propagation and the symmetric fuzzy decision-making model to AUV control systems. Some results of early experiments in synoptic data collection with a conductivity, temperature and depth (CTD) sensor using multiple AUVs for the determination of horizontal structure will be described.     

Network-centric control based on Petri nets in the structured discrete-event system

Using the concepts of network-centric control, we develop a design methodology for a controlling Petri net in real-time automation systems. The methodology involves the structured discrete-event system (SDES) model to analyze the functionality and coherence of the object. We define structure of the model, as well as propose a technique for analyzing and modeling the object via the process Petri net. Finally, we suggest an analysis technique for the process Petri net and develop a design method for a supervisor, i.e., a controlling Petri net which implements the SDES specification (in coherence with the process net).     

车辆自适应巡航控制策略研究与仿真

为了保证车辆自适应巡航控制(ACC)系统的安全性、跟踪效果和稳定性,基于车辆安全距离模型建立了车辆自适应巡航控制模型,在Trucksim中搭建CACC跟驰控制系统;通过Simulink与Trucksim联合仿真,验证系统的可行性.仿真结果表明,在车辆编队的行驶过程中,前后车的车距始终在设定的安全距离范围内波动,具有良好...     

Experiences with implementing parallel discrete-event simulation on GPU

The Journal of Supercomputing - Modern graphics processing units (GPUs) offer much more computational power than recent CPUs by providing a vast number of simple, data-parallel, multithreaded...     

A new approach to fuzzy modeling and control of discrete-time systems

We present a new approach to fuzzy modeling and control of discrete-time systems which is based on the formulation of a novel state-space representation using the hyperbolic tangent function. The new representation, designated the hyperbolic model, combines the advantages of fuzzy system theory and classical control theory. On the one hand, the hyperbolic model is easily derived from a set of Mamdani-type fuzzy rules. On the other hand, classical control theory can be applied to design controllers for the hyperbolic model that not only guarantee stability and robustness but are themselves equivalent to a set of Mamdani-type fuzzy rules. Thus, this new approach combines the best of two worlds. It enables linguistic interpretability of both the model and the controller, and guarantees closed-loop stability and robustness.     

水下拖曳体多源信息监控系统设计与实现

基于海洋电法探测系统中水下拖曳体对多源信息实时监控的需求,设计了一种准实时监测拖曳体的运动状态与环境信息的监控系统。该系统可与水面监控器保持命令与数据沟通,并在监控器的指令下启动水下电法仪执行参数设置、数据采集等操作。经系统样机的海上测试结果表明,系统性能稳定可靠,能够在其他水下信息采集作业中得到推广应用。     

Vehicle-manipulator system dynamic modeling and control for underwater autonomous manipulation

Underwater autonomous manipulation is a challenging task, which not only includes a complicated multibody dynamic and hydrodynamic process, but also involves the limited observation environment. This study systematically investigates the dynamic modeling and control of the underwater vehicle-manipulator multibody system. The dynamic model of underwater vehicle-manipulator system has been established on the basis of the Newton–Euler recursive algorithm. On the basis of dynamic analysis, a motion planning optimization algorithm has been designed in order to realize the coordinate motions between AUV and manipulator through reducing the restoring forces and saving the electric power. On the other hand, a disturbance force observer including the coupling and restoring forces has been designed. An observer-based dynamic control scheme has been established in combination with kinematic and dynamic controller. Furthermore, from the simulations, although the disturbance forces such as restoring and coupling forces are time varying and great, the observer-based dynamic coordinate controller can maintain the AUV attitude stable during the manipulator swing and pitch motions. During the precise manipulation simulation, the stable AUV attitude and minimization of disturbance forces have been realized through combination of optimal motion planning and the observer-based dynamic coordinate controller.     

Hierarchical control of discrete-event systems

An abstract hierarchical control theory is developed for discrete-event systems, based on the concepts of control structures and observers. Control structure is an abstract generalization of the family of controllable sublanguages in the Ramadge-Wonham framework. We establish a general version of Zhong's hierarchical consistency by first achieving control consistency — preservation of control structures through the aggregation mapping in a two-level hierarchy. For a refinement of hierarchical consistency with preservation of nonblocking, the concept of observer is introduced via congruences on nondeterministic transition structures.