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  • 1.
    Emami, Reza
    et al.
    Department of Mechanical and Industrial Engineering, University of Toronto.
    Goldenberg, Andrew A.
    Department of Mechanical and Industrial Engineering, University of Toronto.
    Türksen, I. Burhan
    Department of Mechanical and Industrial Engineering, University of Toronto.
    Systematic design and analysis of fuzzy-logic control and application to robotics: Part 1. Modeling2000In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 33, no 2-3, p. 65-88Article in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Emami, Reza
    et al.
    Department of Mechanical and Industrial Engineering, University of Toronto.
    Goldenberg, Andrew A.
    Department of Mechanical and Industrial Engineering, University of Toronto.
    Türksen, I. Burhan
    Department of Mechanical and Industrial Engineering, University of Toronto.
    Systematic design and analysis of fuzzy-logic control and application to robotics: Part II. Control2000In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 33, no 2-3, p. 89-108Article in journal (Refereed)
    Abstract [en]

    This paper (Part II) follows the task of systematic synthesis and analysis of fuzzy-logic control (FLC) systems introduced in Part I [M.R. Emami, et al., Robotics and Autonomous Systems 33 (2000) 65–88]. First, a generalized formulation of the sliding mode control is obtained for a class of nonlinear multi-input–multi-output systems. This formulation has two distinguishing features that are necessary for the formulation of the proposed approach: (i) it is applicable to “black box” systems with no need to identify the internal parameters or to assume a specific structure; (ii) it is possible to design the robust controller for each system state independently while the stability and robustness of the entire system is guaranteed. The robust fuzzy control rules are designed based on the generalized formulation to guarantee the stability and satisfactory system performance. The proposed FLC has been applied to trajectory control of the four degree-of-freedom IRIS arm (Emami et al., 2000), and was compared with high-gain PID controllers. A superior tracking performance was achieved.

  • 3.
    Martin, Adrian
    et al.
    Institute for Aerospace Studies, University of Toronto.
    Emami, Reza
    Institute for Aerospace Studies, University of Toronto.
    A fault-tolerant approach to robot teams2013In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 61, no 12, p. 1360-1378Article in journal (Refereed)
    Abstract [en]

    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 m2 environment. © 2013 Elsevier B.V. All rights reserved.

  • 4.
    Martin, Peter
    et al.
    Institute for Aerospace Studies, University of Toronto.
    Emami, Reza
    Institute for Aerospace Studies, University of Toronto.
    A neuro-fuzzy approach to real-time trajectory generation for robotic rehabilitation2014In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 62, no 4, p. 568-578Article in journal (Refereed)
    Abstract [en]

    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. © 2014 Elsevier B.V. All rights reserved.

  • 5.
    Nayl, Thaker
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Effect of Kinematic Parameters on MPC based On-line Motion Planning for an Articulated Vehicle2015In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 70, p. 16-24Article in journal (Refereed)
    Abstract [en]

    The aim of this article is to analyze the effect of kinematic parameters on a novel proposed on-line motion planning algorithm for an articulated vehicle based on Model Predictive Control. The kinematic parameters that are going to be investigated are the vehicle's velocity, the maximum allowable change in the articulated steering angle, the safety distance from the obstacles and the total number of obstacles in the operating arena. The proposed modified path planning algorithm for the articulated vehicle belongs to the family of Bug-Like algorithms and is able to take under consideration, the mechanical and physical constraints of the articulated vehicle, as well as its full kinematic model. During the on-line motion planning algorithm, the MPC controller controls the lateral motion of the vehicle, through the rate of the articulation angle, while driving it accurately and safely over the on-line formulated desired path. The efficiency of the proposed combined path planning and control scheme is being evaluated under numerous simulated test cases, while exhaustive simulations have been made for analyzing the dependency of the proposed framework on the kinematic parameters.

  • 6.
    Nayl, Thaker
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nyberg, Rickard
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Design and experimental evaluation of a novel sliding mode controller for an articulated vehicle2018In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 103, p. 213-221Article in journal (Refereed)
    Abstract [en]

    This article presents the design and experimental evaluation of a novel sliding mode control scheme, being applied to the case of an articulated vehicle. The proposed sliding mode controller is based on a novel continuous sliding surface, being introduced for reducing the chattering phenomenon, while achieving a better tracking performance and a fast minimization of the corresponding tracking error. The derivation of the sliding mode controller relies on the fully nonlinear kinematic model of the articulated vehicle, while the overall stability of the control scheme is proven based on the Lyapunov's stability condition. The performance of the established control scheme is being experimentally evaluated through multiple path tracking scenarios on a small scale and fully realistic articulated vehicle

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