M. Reza Emami

Fuzzy-logic control of dynamic systems: from modeling to design

A systematic methodology for the synthesis and analysis of fuzzy-logic controllers for multi-input multi-output nonlinear dynamic systems is proposed in this paper. A robust model-based control str ...

Systematic design and analysis of fuzzy-logic control and application to robotics : Part II. Control

Abstract A systematic methodology for synthesis and analysis of fuzzy-logic controllers is proposed in this paper (Part I) and its follow up (Part II) [M.R. Emami, et al., Robotics and Autonomous Systems 33 (2000) 89–108]. A robust model-based control structure is suggested that includes a fuzzy-logic inverse dynamics model and several robust fuzzy control rules. The model encapsulates the knowledge of the system dynamics in the form of IF–THEN rules. The paper focuses on how to obtain this knowledge systematically from the input–output data of a complex system; one that is ill-defined or contains complicated phenomena that are difficult to interpret analytically. All practical steps, from data acquisition to model validation, are illustrated using a four degree-of-freedom robot manipulator. Comparing the results with those of a complete analytical model and a heuristic fuzzy modeling technique illustrates the strength of the proposed methodology in terms of capturing effects that are difficult to model. In the follow-up paper, this model is used in the proposed control structure.

Modelling of a robotic leg using bond graphs

Abstract This paper studies the bond graph model of a robotic leg mechanism, and discusses methods of extracting significant features of system dynamics through simpler models. The goal is to determine a set of simpler mechanisms with similar dynamic behaviour to that of the original leg in various phases of its motion. The paper is divided in two sections. In the first section, a modular bond-graph representation of the leg mechanism is determined. In the second section, two algorithms are applied to simplify the bond graph representation. The first algorithm determines the relevant dynamic elements of the system for each phase of motion, and the second algorithm finds the simple mechanism described by the remaining dynamic elements. In addition to greatly simplifying the control system of the robotic leg, using simpler mechanisms with similar behaviour provides a greater insight into the dynamics of the system.

Design and Simulation of Robot Manipulators Using a Modular Hardware-in-the-loop Platform

The need for developing high quality systems with short and cost-effective design schedules has created an ongoing demand for efficient prototyping and testing tools (Wheelright & Clark, 1992). In many engineering applications failure of a system can have severe consequences, from loss of hardware and capital to complete mission failure, and can even result in the loss of human life (Ledin, 1999). The earliest form of prototyping, physical prototyping, began with the development of the first system, and it refers to fabricating a physical system to evaluate performance and test design alterations. There have been many advances in this field, such as the use of scaled models (Faithfull et al., 2001), but in most cases the time and cost involved in building complete physical prototypes are prohibitive. With the advent of computers a new form of prototyping, termed analytical prototyping, has become a second viable option (Ulrich & Eppinger, 2000). Computer models are generally inexpensive to develop and can be quickly modified to experiment with various aspects of the system. However, this flexibility often comes at the cost of approximations used to model complex physical phenomena, which in turn lead to inaccuracies in the model and system behaviour. A prototyping tool that has been gaining significant popularity in recent years is hardware-in-the-loop simulation, which can effectively combine the advantages of the two traditional prototyping methods. The underlying concept of hardware-in-the-loop (HIL) simulation is to use physical hardware for system components that are difficult or impossible to model and link them to a computer model that simulates the other aspects of the system. This technique has been successfully applied to development and testing in a wide range of engineering fields, including aerospace (Leitner, 1996), automotive (Hanselman, 1996), controls (Linjama et al., 2000), manufacturing (Stoeppler et al., 2005), and naval and defence (Ballard et al., 2002). This research investigates the application of HIL simulation as a tool for the design and testing of serial-link industrial manipulators, and proposes a generic and modular robotic hardware-in-the-loop simulation (RHILS) architecture. The RHILS architecture was implemented in the simulation of a standard industrial manipulator and evaluated on its ability to simulate the robot and its usefulness as a design tool. The remainder of this section briefly reviews the state-of-the-art in HIL simulation across a broad range of fields, highlighting some of the key benefits and considerations, and then summarizes the current work of other researchers in the specific field of robotic

Real-time fuzzy trajectory generation for robotic rehabilitation therapy

This paper proposes a method for the design of a real-time fuzzy trajectory generator for the robotic rehabilitation of patients with upper limb dysfunction due to neurological diseases. The system utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. This approach also allows for the fine-tuning of system dynamics using linguistic variables. The rule base for the system is trained using a fuzzy clustering approach based on experimental data gathered during traditional therapy sessions. The trajectory generator will be packaged as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations.

A neuro-fuzzy approach to real-time trajectory generation for robotic rehabilitation

This paper proposes a method for the design of a real-time neuro-fuzzy trajectory generator for the robotic rehabilitation of patients with upper limb dysfunction due to neurological diseases. The primary objective of the methodology is to assist therapists by allowing them to delegate repetitive therapy tasks to a mechatronic system. The trajectory generator is packaged as a platform-independent solution to facilitate the rehabilitation of patients using multiple manipulator configurations. The system utilizes a fuzzy-logic schema to introduce compliance into the human-robot interaction, and to allow the emulation of a wide variety of therapy techniques. This approach also allows for the fine-tuning of patient specific behaviour using linguistic variables. The rule base for the system is trained using a fuzzy clustering algorithm and applied to the experimental data gathered during traditional therapy sessions. The compliance rule base is combined with a hybrid neuro-fuzzy compensator to automatically tune the dynamics of the robot-patient interaction. Preliminary results indicate that the approach can accurately reproduce a prescribed patient/therapist interaction, validating the proposed approach.

Design and Development of Robotic Hardware-in-the-Loop Simulation

This paper details the design and development of a robotic hardware-in-the-loop simulation platform that can be used for rapid-prototyping industrial manipulators. The architecture of the proposed platform has been presented by Martin and Emami (2006). Potential benefits of such a platform include allowing concurrent development of hardware and control system components and providing a reusable platform for reconfigurable manipulators through a generic and modular structure. Some preliminary tests on the platform have also been discussed in the paper

A fault-tolerant approach to robot teams

As the applications of mobile robotics evolve it has become increasingly less practical for researchers to design custom hardware and control systems for each problem. This paper presents a new approach to control system design in order to look beyond end-of-lifecycle performance, and consider control system structure, flexibility, and extensibility. Towards these ends the Control ad libitum philosophy was proposed, stating that to make significant progress in the real-world application of mobile robot teams the control system must be structured such that teams can be formed in real-time from diverse components. The Control ad libitum philosophy was applied to the design of the HAA (Host, Avatar, Agent) architecture: a modular hierarchical framework built with provably correct distributed algorithms. A control system for mapping, exploration, and foraging was developed using the HAA architecture and evaluated in three experiments. First, the basic functionality of the HAA architecture was studied, specifically the ability to: (a) dynamically form the control system, (b) dynamically form the robot team, (c) dynamically form the processing network, and (d) handle heterogeneous teams and allocate robots between tasks based on their capabilities. Secondly, the control system was tested with different rates of software failure and was able to successfully complete its tasks even when each module was set to fail every 0.5-1.5 min. Thirdly, the control system was subjected to concurrent software and hardware failures, and was still able to complete a foraging task in a 216 m^2 environment.

Concurrent synthesis of robot manipulators using Hardware-in-the-loop Simulation

This paper discusses a practical approach to the concurrent synthesis of robot manipulators, which is based on the alternative design methodology of Linguistic Mechatronics (LM) as well as the utilization of a modular Robotic Hardware-in-the-loop Simulation (RHILS) platform. The RHILS platform involves physical joint modules and the control unit to reduce modeling complexities while taking into account various physical phenomena. The LM methodology simplifies the multi-objective constrained optimization problem into a single-objective unconstrained formulation and also brings subjective notions of design into the scope. The new approach is applied to redesigning kinematic, dynamic and control parameters of an industrial manipulator.

Concurrent Markov decision processes for robot team learning

Abstract Multi-agent learning, in a decision theoretic sense, may run into deficiencies if a single Markov decision process (MDP) is used to model agent behaviour. This paper discusses an approach to overcoming such deficiencies by considering a multi-agent learning problem as a concurrence between individual learning and task allocation MDPs. This approach, called Concurrent MDP (CMDP), is contrasted with other MDP models, including decentralized MDP. The individual MDP problem is solved by a Q-Learning algorithm, guaranteed to settle on a locally optimal reward maximization policy. For the task allocation MDP, several different concurrent individual and social learning solutions are considered. Through a heterogeneous team foraging case study, it is shown that the CMDP-based learning mechanisms reduce both simulation time and total agent learning effort.