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  • 1.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    A non-monotonic fuzzy logic.1991In: International Fuzzy Systems Association, Fourth World Congress,1991, 1991Conference paper (Refereed)
  • 2.
    Doherty, Patrick
    et al.
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    Driankov, Dimiter
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Nonmonotonicity, fuzziness, and multi-values.1993In: Fuzzy Logic: State of the Art. Series D: System Theory, Knowledge Engineering and Problem Solving. / [ed] R. Lowen and M. Roubens, Dordrecht ; Boston: Kluwer Academic Publishers , 1993Chapter in book (Other academic)
    Abstract [en]

      

  • 3.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Hellendoorn, H.
    Fuzzy if-then-unless rules and their implementation.1992In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU92,1992, Springer , 1992Conference paper (Refereed)
  • 4.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Hellendoorn, H.
    Fuzzy if-then-unless rules and their implementation.1992Report (Other academic)
  • 5.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Hellendoorn, Hans
    Fuzzy if-then-unless rules and their implementation1993In: International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, ISSN 0218-4885, Vol. 1, no 2, p. 167-182Article in journal (Refereed)
    Abstract [en]

    We consider the possibility of generalizing the notion of a fuzzy If-Then rule to take into account its context dependent nature. We interpret fuzzy rules as modeling a forward directed causal relationship between the antecedent and the conclusion, which applies in most contexts, but on occasion breaks down in exceptional contexts. The default nature of the rule is modeled by augmenting the original If-Then rule with an exception part. We then consider the proper semantic correlate to such an addition and propose a ternary relation which satisfies a number of intuitive constraints described in terms of a number of inference rules. In the rest of the paper, we consider implementational issues arising from the unless extension and propose the use of reason maintenance systems, in particular TMS's, where a fuzzy If-Then-Unless rule is encoded into a dependency net. We verify that the net satisfies the constraints stated in the inference schemes and conclude with a discussion concerning the integration of qualitative IN-OUT labelings of the TMS with quantitative degree of membership labelings for the variables in question.

  • 6.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Tsoukias, A.
    Partial logics and partial preferences.1992In: International Conference on Economics/Management and Information Technology,1992, 1992, p. 525-Conference paper (Refereed)
  • 7.
    Doherty, Patrick
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Tsoukias, A.
    Partiality, para-consistency and preference modeling: Preliminary version.1992Report (Other academic)
  • 8.
    Driankov, Dimiter
    et al.
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Doherty, Patrick
    Linköping University, Department of Computer and Information Science, KPLAB - Knowledge Processing Lab. Linköping University, The Institute of Technology.
    A non-monotonic fuzzy logic1992In: Fuzzy Logic for the Management of Uncertainty / [ed] Lotfi A. Zadeh, Janusz Kacprzyk, New York: John Wiley & Sons , 1992, p. 171-190Chapter in book (Other academic)
  • 9.
    Kadmiry, Bourhane
    et al.
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Bergsten, Pontus
    Örebro University .
    Driankov, Dimiter
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Autonomous Helicopter Control Using Fuzzy-Gain Scheduling2001In: Proceedings of the IEEE International Conference on Robotic & Automation (ICRA), IEEE , 2001, Vol. 3, p. 2980-2985Conference paper (Refereed)
    Abstract [en]

    The work reported in the paper is aimed at achieving aggressive manoeuvrability for an unmanned helicopter APID MK-III by Scandicraft AB in Sweden. The manoeuvrability problem is treated at the level of attitude (pitch, roll, yaw) and the aim is to achieve stabilization of the attitude angles within much larger ranges than currently available. We present a fuzzy gain scheduling control approach based on two different types of Iinearization of the original nonlinear APID MK-III model. The performance of the fuzzy gain scheduled controllers is evaluated in simulation and shows that they are effective means for achieving the desired robust manoeuvrability.

  • 10.
    Kadmiry, Bourhane
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    A Fuzzy Flight Controller Combining Linguistic and Model-based Fuzzy Control2004In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 146, no 3, p. 313-347Article in journal (Refereed)
    Abstract [en]

    In this paper we address the design of a fuzzy flight controller that achieves stable and robust -aggressive- manoeuvrability for an unmanned helicopter. The fuzzy flight controller proposed consists of a combination of a fuzzy gain scheduler and linguistic (Mamdani-type) controller. The fuzzy gain scheduler is used for stable and robust altitude, roll, pitch, and yaw control. The linguistic controller is used to compute the inputs to the fuzzy gain scheduler, i.e., desired values for roll, pitch, and yaw at given desired altitude and horizontal velocities. The flight controller is obtained and tested in simulation using a realistic nonlinear MIMO model of a real unmanned helicopter platform, the APID-MK

  • 11.
    Kadmiry, Bourhane
    et al.
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Driankov, Dimiter
    Örebro University.
    Autonomous Helicopter Control using Linguistic and Model-Based Fuzzy Control2001In: Proceedings of the IEEE International Symposium on Intelligent Control (CCA/ISIC), IEEE , 2001, p. 348-352Conference paper (Refereed)
    Abstract [en]

    The paper presents the design of a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. The controller is able of regulating high horizontal velocities via stabilization of the attitude angles within much larger ranges than currently available. We use a novel approach to the design consisting of two steps: 1) a Mamdani-type of a fuzzy rules are used to compute for each desired horizontal velocity the corresponding desired values for the attitude angles and the main rotor collective pitch; and 2) using a nonlinear model of the altitude and attitude dynamics, a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. According to our knowledge this is the first time when a combination of linguistic and model-based fuzzy control is used for the control of a complicated plant such as an autonomous helicopter. The performance of the combined linguistic/model-based controllers is evaluated in simulation and shows that the proposed design method achieves its intended purpose

  • 12.
    Kadmiry, Bourhane
    et al.
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Driankov, Dimiter
    Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group . Linköping University, The Institute of Technology.
    Fuzzy Control of an Autonomous Helicopter2001In: Proceedings of the 9th IEEE International Fuzzy Systems Association (IFSA) World Congress, IEEE Computer Society , 2001, p. 2797-2802Conference paper (Refereed)
    Abstract [en]

    This work presents a horizontal velocity controller for the unmanned helicopter APID MK-III developed by Scandicraft AB in Sweden. We use a novel approach to the design consisting of two steps: 1) Mamdani-type of fuzzy rules to compute each of the desired horizontal velocity corresponding to the desired values for the attitude angles and the main rotor collective pitch; and 2) a Takagi-Sugeno controller is used to regulate the attitude angles so that the helicopter achieves its desired horizontal velocities at a desired altitude. The performance of the combined linguistic/model-based controller is evaluated in simulation and shows that the proposed design method achieves its intended purpose

  • 13.
    Kadmiry, Bourhane
    et al.
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Takagi-Sugeno Fuzzy Gain Scheduling with Sampling-Time Uncertainties2004In: IEEE International Conference on Fuzzy Systems Fuzz-IEEE 2004,2004, 2004, p. 239-Conference paper (Refereed)
    Abstract [en]

    This paper addresses the robust fuzzy control problem for discrete-time nonlinear systems in the presence of sampling time uncertainties. The case of the discrete T-S fuzzy system with sampling-time uncertainty is considered and a robust controller design method is proposed. The sufficient conditions and the design procedure are formulated in the form of linear matrix inequalities (LMI). The effectiveness of the proposed controller design methodology is demonstrated of a visual-servoing control problem

  • 14.
    Palm, R.
    et al.
    Siemens AG Corporate Technology, Otto-Hahn-Ring 6, 81739, Munich, Germany.
    Driankov, Dimiter
    Linköping University, The Institute of Technology. Linköping University, Department of Computer and Information Science, AUTTEK - Autonomous Unmanned Aerial Vehicle Research Group .
    Design of a fuzzy gain scheduler using sliding mode control principles2001In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 121, no 1, p. 13-23Article in journal (Refereed)
    Abstract [en]

    Fuzzy gain schedulers are designed on the basis of a conventional modeling of the nonlinear controlled system and the division of the state space into a finite number of fuzzy regions. Linearization of the nonlinear system at the center of each fuzzy region leads to the design of a set of linear control laws that locally stabilize the linearized system, and consequently the original nonlinear system at the corresponding operating point. Gain scheduling control of the original nonlinear system can be therefore realized along an a priori unknown, but slowly time varying desired trajectory. In this paper we analyze the stability and robustness of the gain-scheduled closed-loop system by adopting ideas from sliding mode control. It is shown that gain scheduling control of the original nonlinear system can be realized along an a priori unknown, but slowly time-varying desired trajectory. It is shown how the advantages of the sliding mode types of analysis of a fuzzy gain scheduler can also be used for its design. © 2001 Elsevier Science B.V.

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