For real-time computing systems, energy efficiency and reliability are two primary design concerns. In this research work, we study the problem of enhanced energy-aware standby-sparing for fixed-priority (FP) hard real-time systems under reliability requirement. The standby-sparing system adopts a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to keep the energy consumption for such kind of systems under control, we explore enhanced fixed-priority scheduling schemes to minimize the overlapped concurrent executions of the workloads on the primary processor and on the spare processor, enabling energy savings. Moreover, efficient online scheduling techniques are under development to boost the energy savings during runtime while preserving the system reliability.

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

Achieving recognizable visual event-related potentials plays an important role in improving the success rate in telepresence control of a humanoid robot via N200 or P300 potentials. The aim of this research is to intensively investigate ways to induce N200 potentials with obvious features by flashing robot images (images with meaningful information) and by flashing pictures containing only solid color squares (pictures with incomprehensible information). Comparative studies have shown that robot images evoke N200 potentials with recognizable negative peaks at approximately 260 ms in the frontal and central areas. The negative peak amplitudes increase, on average, from 1.2 μV, induced by flashing the squares, to 6.7 μV, induced by flashing the robot images. The data analyses support that the N200 potentials induced by the robot image stimuli exhibit recognizable features. Compared with the square stimuli, the robot image stimuli increase the average accuracy rate by 9.92%, from 83.33% to 93.25%, and the average information transfer rate by 24.56 bits/min, from 72.18 bits/min to 96.74 bits/min, in a single repetition. This finding implies that the robot images might provide the subjects with more information to understand the visual stimuli meanings and help them more effectively concentrate on their mental activities.

In this paper, we propose a hybrid FUZZY P + ID controller to stabilize the towing cable tension between a cable ship and a burying system. First, we develop the model of a winch system driven by valve-controlled hydraulic motors and evaluate the step responses yielded by the conventional PID and the proposed FUZZY P+ID controllers using simulations. The comparative studies show that the control performance yielded by the FUZZY P+ID controller is superior. We replace the existing PID controller implemented on the towing winch with the FUZZY P + ID controller for burying cable tasks at speed up to 1 knot under sea waves with significant variations (peak-to-peak) from 1.5 to 2.5 meters. The real applications demonstrate that the FUZZY P + ID controller is much more robust than the conventional PID controller.

A mission of chemical plume tracing (CPT) in near-shore and ocean environments is to find out an odor source via an autonomous underwater vehicle (AUV). It is necessary to confirm the detected odor source using a visual system interactively or automatically, when a chemical sensor identifies the odor source. However, color images taken in near-shore ocean environments are very vague due to dim illumination conditions and fluid advection effects. This paper presents a fuzzy algorithm for recognizing the chemical plume and its source in near-shore and ocean environments. This algorithm iteratively generates color patterns based on a defined reference color and extracts color components of the chemical plume and its source from fuzzy images using a fuzzy color extractor (FCE). The proposed approach to color image segmentation might be of general interest to robot vision.

This paper presents a strategy for chemical plume tracing (CPT) with soft obstacle avoidance, such as kelp forests or seaweed in near-shore, oceanic environments. The basic idea is to integrate a low-level fuzzy following obstacle behavior into a subsumption architecture for moth-inspired plume tracing. By on-line generation of dynamic reference targets (DRTs) on a course of the most recent CPT activity, an autonomous underwater vehicle (AUV) can quickly and correctly resume CPT maneuvering after passing obstacles. Simulation studies of CPT with obstacle avoidance are performed using a simulated plume with significant meander and filament intermittency in an environment with length scales of 100 m.

This paper develops new multiple sensor-based algorithms for identifying a chemical odor source in a near-shore and ocean environment via an autonomous underwater vehicle (AUV). Those algorithms implement two modules: source declaration and source verification, which are embedded in a subsumption architecture for chemical plume tracing (CPT). The source declaration module is based on chemical events detected by a chemical sensor, in combination with measured vehicle locations and fluid flow directions, while the source verification module uses a fuzzy color segmentation algorithm to process an image taken when the odor source is declared.

This paper explores the developing of a graphical simulation environment for multiple chemical plume tracing and source identification. A number of classes, including a graphical display class, make up the simulation program. Because the intent is to capture and simulate real life scenarios, the program simulates significant variation of fluid flow velocity and orientation, the significant intermittency between advection fluid flow, chemical filaments, and significant meander of chemical plumes to challenge a chemical plume tracing strategy. Thus, the advection fluid flow model class, plume model classes, sensor classes, plume tracing strategy classes, and vehicle model classes were created to bring this challenge alive in a measurable, three dimensional model. The outcome is highly promising, in that it can efficiently evaluate strategies for tracing multiple plumes, identifying their sources, and can do so even in three-dimensions.

This paper derives a single chemical sensor based algorithm from the moth-inspired chemical plume strategies for identifying plume sources in fluid-advected environments. We evaluate the algorithm performance using a simulated plume with significant meander and filament intermittency. The inwater test runs demonstrated the algorithm effective and robust to the variation of near-shore ocean environments.

A mission of chemical plume tracing (CPT) is to navigate an autonomous underwater vehicle (AUV) to find a chemical plume, to trace the plume to its source, and to declare the source location. This paper presents a multisensor integration strategy for source declaration subsumed in a behavior based control architecture with several individual behaviors. The performance of the proposed strategy is evaluated using a simulated turbulent fluid environment. The results show that, for 1000 test runs, the proposed strategy reaches a success rate in declaring the odor source of 98% and an average error of the declared source location about 3.27 meters in an operation area with length scales of 100 meter. Source verification is developed using a fuzzy reasoning based segmentation algorithm to recognize the odor source in color images acquired by a visual sensor in the vicinity of the odor source.

This paper presents a behavior-based adaptive mission planner (AMP)to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV)equipped with multiple types of sensors that measure chemical concentration,the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume,tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying up wind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants.

This paper addresses the problem of mapping likely locations of a chemical source using an autonomous vehicle operating in a fluid flow. The paper reviews biological plume-tracing concepts, reviews previous strategies for vehicle-based plume tracing, and presents a new plume mapping approach based on hidden Markov methods (HMM). HMM provide efficient algorithms for predicting the likelihood of odor detection versus position, the likelihood of source location versus position, the most likely path taken by the odor to a given location, and the path between two points most likely to result in odor detection. All four are useful for solving the odor source localization problem using an autonomous vehicle. The vehicle is assumed to be capable of detecting above threshold chemical concentration and sensing the fluid flow velocity at the vehicle location. The fluid flow is assumed to vary with space and time, and to have a high Reynolds number (Re>10).

We propose in this paper an enhanced fuzzy P+ID controller to improve control performance in both dynamic transient and steady-state periods for mechanical manipulators under uncertainty. The fuzzy P+ID controller adds only two additional parameters to be tuned relative to the original PID controller. One of these parameters is mainly used to reduce a steady-state error. The other is used to speed up the dynamic response. A simulation study and experimental results for a two-link manipulator with uncertainty demonstrate the superior control performance of the proposed fuzzy P+ID controllers.

Proposes a neuro-fuzzy method to model the dynamic behavior of complex systems based on real experimental data. First, we investigate the firing strength of rules by a fuzzy C-means clustering method. Then, we retrieve the membership functions of input variables by a neuro-fuzzy network. Finally, we identify the parameters of linear local models by recursive least squares. In particular, we applied this method to construct the dynamics of a boiler combustion process.

Presents approaches to the design of a hybrid fuzzy logic proportional plus conventional integral-derivative (fuzzy P+ID) controller in an incremental form. This controller is constructed by using an incremental fuzzy logic controller in place of the proportional term in a conventional PID controller, By using the bounded-input/bounded-output "small gain theorem", the sufficient condition for stability of this controller is derived. Based on the condition, we modify the Ziegler and Nichols' approach to design the fuzzy P+ID controller. In this case, the stability of a system remains unchanged after the PID controller is replaced by the fuzzy P+ID controller without modifying the original controller parameters. When a plant can be described by any modeling method, the fuzzy P+ID controller can be determined by an optimization technique. Finally, this controller is used to control a nonlinear system. Numerical simulation results demonstrate the effectiveness of the fuzzy P+ID controller in comparison with the conventional PID controller, especially when the controlled object operates under uncertainty or in the presence of a disturbance.

It is known that control signals from a fuzzy logic controller are determined by a response behavior of a controlled object rather than its analytical models. That implies that the fuzzy controller could yield a similar control result for a set of plants with a similar dynamic behavior. This idea lends to modeling of a plant with unknown structure by defining several types of dynamic behaviors. On the basis of dynamic behavior classification, a new method is presented for the design of a neuro-fuzzy control system in two steps: 1) we model a plant with unknown structure by choosing a set of simplified systems with equivalent behavior as "templates" to optimize their fuzzy controllers off-line; and 2) we use an algorithm for system identification to perceive dynamic behavior and a neural network to adapt fuzzy logic controllers by matching the "templates" online. The main advantage of this method is that convergence problem can be avoided during adaptation process. Finally, the proposed method is used to design neuro-fuzzy controllers for a two-link manipulator.

This paper presents a new method for 'perception-action' behavior control of a mobile robot in uncertain environments using fuzzy logic. 'Perception-action' behavior control suffers from two significant problems: 1. The quantitative formulation of 'perception-action' behaviors; 2. The efficient coordination of conflicts and competitions among multiple behaviors. The main idea of the present study is to incorporate fuzzy logic control with behavior-based control such that: 'perception-action' behaviors are formulated by fuzzy sets and fuzzy rules; conflicts and competitions among different behaviors are coordinated by fuzzy reasoning. Simulation results show that the proposed method can be applied to efficient robot navigation in complex and unknown environments by weighting multiple behaviors, such as avoiding obstacles, following edges, and moving to a target, and so forth. In addition, this method is suitable for robot navigation by multisensor fusion and integration.>

This paper presents an approach to automatic tuning of fuzzy logic controller for manipulators, which modifies the shape of membership functions automatically according to previous responses to achieve a desired control performance. On the basis of defining a set of cubic splines as membership functions regarding change-in-error e/spl dot//sub j/(t), the optimum membership functions are found by the Nelder and Mead's simplex algorithm. The proposed control scheme consists of a traditional fuzzy logic controller and a conventional derivative controller. In order to demonstrate the effectiveness and the robustness of the control scheme, we report a number of simulation results involving both step and tracking control of a two-link manipulator in the presence of load and friction changes.>