George K. I. Mann

Analysis of direct action fuzzy PID controller structures

The majority of the research work on fuzzy PID controllers focuses on the conventional two-input PI or PD type controller proposed by Mamdani (1974). However, fuzzy PID controller design is still a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. This paper investigates different fuzzy PID controller structures, including the Mamdani-type controller. By expressing the fuzzy rules in different forms, each PLD structure is distinctly identified. For purpose of analysis, a linear-like fuzzy controller is defined. A simple analytical procedure is developed to deduce the closed form solution for a three-input fuzzy inference. This solution is used to identify the fuzzy PID action of each structure type in the dissociated form. The solution for single-input-single-output nonlinear fuzzy inferences illustrates the effect of nonlinearity tuning. The design of a fuzzy PID controller is then treated as a two-level tuning problem. The first level tunes the nonlinear PID gains and the second level tunes the linear gains, including scale factors of fuzzy variables. By assigning a minimum number of rules to each type, the linear and nonlinear gains are deduced and explicitly presented. The tuning characteristics of different fuzzy PID structures are evaluated with respect to their functional behaviors. The rule decoupled and one-input rule structures proposed in this paper provide greater flexibility and better functional properties than the conventional fuzzy PHD structures.

New methodology for analytical and optimal design of fuzzy PID controllers

Describes a methodology for the systematic design of fuzzy PID controllers based on theoretical fuzzy analysis and, genetic-based optimization. An important feature of the proposed controller is its simple structure. It uses a one-input fuzzy inference with three rules and at most six tuning parameters. A closed-form solution for the control action is defined in terms of the nonlinear tuning parameters. The nonlinear proportional gain is explicitly derived in the error domain. A conservative design strategy is proposed for realizing a guaranteed-PID-performance (GPP) fuzzy controller. This strategy suggests that a fuzzy PID controller should be able to produce a linear function from its nonlinearity tuning of the system. The proposed PID system is able to produce a close approximation of a linear function for approximating the GPP system. This GPP system, incorporated with a genetic solver for the optimization, will provide the performance no worse than the corresponding linear controller with respect to the specific performance criteria. Two indexes, linearity approximation index (LAI) and nonlinearity variation index (NVI), are suggested for evaluating the nonlinear design of fuzzy controllers. The proposed control system has been applied to several first-order, second-order, and fifth-order processes. Simulation results show that the proposed fuzzy PID controller produces superior control performance to the conventional PID controllers, particularly in handling nonlinearities due to time delay and saturation.

Two-level tuning of fuzzy PID controllers

Fuzzy PID tuning requires two stages of tuning; low level tuning followed by high level tuning. At the higher level, a nonlinear tuning is performed to determine the nonlinear characteristics of the fuzzy output. At the lower level, a linear tuning is performed to determine the linear characteristics of the fuzzy output for achieving overall performance of fuzzy control. First, different fuzzy systems are defined and then simplified for two-point control. Non-linearity tuning diagrams are constructed for fuzzy systems in order to perform high level tuning. The linear tuning parameters are deduced from the conventional PID tuning knowledge. Using the tuning diagrams, high level tuning heuristics are developed. Finally, different applications are demonstrated to show the validity of the proposed tuning method.

Integrated fuzzy logic and genetic algorithmic approach for simultaneous localization and mapping of mobile robots

This paper presents a novel method of integrating fuzzy logic (FL) and genetic algorithm (GA) to solve the simultaneous localization and mapping (SLAM) problem of mobile robots. The core of the proposed SLAM algorithm is based on an island model GA (IGA) which searches for the most probable map(s) such that the associated pose(s) provides the robot with the best localization information. Prior knowledge about the problem domain is transferred to GA in order to speed up the convergence. Fuzzy logic is employed to serve this purpose and allows the IGA to conduct the search starting from a potential region of the pose space. The underlying fuzzy mapping rules infer the uncertainty in the robots location after executing a motion command and generate a sample-based prediction of its current position. This sample set is used as the initial population for the proposed IGA. Thus the GA-based search starts with adequate knowledge on the problem domain. The correspondence problem in SLAM is solved by exploiting the property of natural selection, which supports better performing individuals to survive in the competition. The proposed algorithm follows essentially no assumption about the environment and has the capacity to resolve the loop closure problem without maintaining explicit loop closure heuristics. The algorithm processes sensor data incrementally and therefore, has the capability of real time map generation. Experimental results in different indoor environments are presented to validate robustness of the algorithm.

Developments in hardware systems of active upper-limb exoskeleton robots

The very first application of active exoskeleton robot was to provide external power to a soldier so that he can carry additional weight than his strength. Since then this technology has focused on developing systems for assisting and augmenting human power. Later this technology is expanded into other applications such as limb rehabilitation and tele-operations. Exoskeleton research is still a growing area and demands multi-disciplinary approaches in solving complex technical issues. In this paper, the developments of active upper-limb exoskeleton robots are reviewed. This paper presents the major developments occurred in the history, the key milestones during the evolution and major research challenges in the present day context of hardware systems of upper-limb exoskeleton robots. Moreover, the paper provides a classification, a comparison and a design overview of mechanisms, actuation and power transmission of most of the upper-limb exoskeleton robots that have been found in the literature. A brief review on the control methods of upper-limb exoskeleton robots is also presented. At the end, a discussion on the future directions of the upper-limb exoskeleton robots was included. Reviews developments of active upper-limb exoskeleton robots.Presents major developments of exoskeleton hardware systems occurred in history.Identifies major research challenges in exoskeleton robots.Provides a classification, a comparison and a design overview of mechanisms and actuation.Presents future directions in upper-limb exoskeleton robots.

An Object-Based Visual Attention Model for Robotic Applications

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

Behavior-modulation technique in mobile robotics using fuzzy discrete event system

This paper presents a novel behavior-modulation technique using a fuzzy discrete event system (FDES) for behavior-based robotic control. The method exploits the multivalued feature of fuzzy logic (FL) and event-driven property of a discrete event system (DES) to generate the activity of a behavior using fuzzy state vectors. State-based prediction of an activity is accomplished using fuzzily defined event matrices. A central arbiter employs priority-based arbitration among the activity state vectors and generates new event matrices to modify the activity states of the behaviors. The method combines aspects of both command fusion and behavior arbitration. Furthermore, the proposed approach has the ability to define state-based observability and controllability to handle sensory uncertainty and environmental dynamics. Observability describes decision vagueness associated with sensory data, whereas controllability specifies undesirable state-reach within the observed environment. Real-time results of FDES-based mobile robot navigation are presented and compared against four different modulation methods to validate its superior performance

Mobile robot navigation using motor schema and fuzzy context dependent behavior modulation

This paper presents a novel technique to autonomously select different motor schemas using fuzzy context dependant blending of robot behaviors for navigation. First, a set of motor schemas is formed as behaviors. Both strategic and reactive type schemas have been employed in order to facilitate both the aspects of global and local motion planning. While strategic schemas are formed using the prior knowledge of the environment, the reactive schemas are activated using current sensory data of the robot. For global path planning, a safe path is first created using a Voronoi diagram. For local planning, the Voronoi vertices are treated as immediate subgoals and are used to form schemas leading to achieve optimized traveled distance and goal oriented robot navigation. Two motor schemas are formed as reactive behaviors for obstacle avoidance. The unknown obstacles are modeled using the sensory data. The coordinated behavior is achieved while employing weighed vector summation of the schemas. The adaptation of weights are achieved through a fuzzy inference system where fuzzy rules are used to dynamically generate the weights during navigation. A novel approach is proposed for fuzzy context-dependent blending of schemas. Fuzzy rules are formed using two main criteria into account: the first criterion reasons out the context dependent activity of a schema for achieving goal and the second criterion reasons out cooperative activity of strategic schemas with high priority reactive schemas. Comprehensive results validate that the proposed technique eliminates the existing drawbacks of motor schema approaches available in literature and provides collision free goal oriented robot navigation.

Design and Tuning of Standard Additive Model Based Fuzzy PID Controllers for Multivariable Process Systems

This paper describes a design and two-level tuning method for fuzzy proportional-integral derivative (FPID) controllers for a multivariable process where the fuzzy inference uses the inference of standard additive model. The proposed method can be used for any multiinput-multioutput process and guarantees closed-loop stability. In the two-level tuning scheme, the tuning follows two steps: low-level tuning followed by high-level tuning. The low-level tuning adjusts apparent linear gains, whereas the high-level tuning changes the nonlinearity in the normalized fuzzy output. In this paper, two types of FPID configurations are considered, and their performances are evaluated by using a real-time multizone temperature control problem having a 3times3 process system.

A Probabilistic Model of Overt Visual Attention for Cognitive Robots

Visual attention is one of the major requirements for a robot to serve as a cognitive companion for human. The robotic visual attention is mostly concerned with overt attention which accompanies head and eye movements of a robot. In this case, each movement of the camera head triggers a number of events, namely transformation of the camera and the image coordinate systems, change of content of the visual field, and partial appearance of the stimuli. All of these events contribute to the reduction in probability of meaningful identification of the next focus of attention. These events are specific to overt attention with head movement and, therefore, their effects are not addressed in the classical models of covert visual attention. This paper proposes a Bayesian model as a robot-centric solution for the overt visual attention problem. The proposed model, while taking inspiration from the primates visual attention mechanism, guides a robot to direct its camera toward behaviorally relevant and/or visually demanding stimuli. A particle filter implementation of this model addresses the challenges involved in overt attention with head movement. Experimental results demonstrate the performance of the proposed model.