I. Saboori

Publications (5)1.21 Total impact

• Article: Decentralized Adaptive Control of Large-Scale Affine and Nonaffine Nonlinear Systems

  B. Karimi, M.B. Menhaj, M. Karimi-Ghartemani, I. Saboori
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  ABSTRACT: This paper presents a decentralized adaptive control design for a class of large-scale nonlinear systems with unknown subsystems. When the subsystems are modeled by affine equations, a direct adaptive controller is devised based on the Lyapunov theory, so that the stability of the closed-loop system is guaranteed by introducing a suitably driven adaptive rule. A neuro-based structure is proposed when the subsystems are nonaffine, and the stability analysis is also performed based on the Lyapunov theory. Moreover, the unknown interactions among the subsystems are considered as having a nonlinear function against the simple form considered for the affine case. The proposed controllers are employed in an inverted two-pendulum system, and their promising performances are illustrated.
  IEEE Transactions on Instrumentation and Measurement 09/2009; · 1.21 Impact Factor
• Conference Proceeding: A real-time nonlinear robust controller for magnetic levitation systems

  I. Saboori, M.B. Menhaj, B. Karimi, E. Saboori
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  ABSTRACT: This paper first introduces a nonlinear robust controller for maglev systems. This controller which employs mainly sliding mode concepts ensures global stability of the closed-loop system. A theorem has been established in the paper to prove the overall system stability. To show how good the proposed controller is we apply it to the maglev system. Furthermore, it has been shown that the proposed controller is robust against plant parameter uncertainties. The Experimental result clearly approves the effectiveness of the designed controller.
  Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on; 07/2008
• Conference Proceeding: A Decentralized Direct Adaptive Controller for a Class of Large-Scale Interconnected Nonlinear Systems

  B. Karimi, M.B. Menhaj, A. Afshar, I. Saboori
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  ABSTRACT: This paper presents a decentralized adaptive controller for a class of large-scale nonlinear systems with unknown subsystems. A direct adaptive controller is devised based on Lyapunov stability analysis so that the stability of the closed loop system is guaranteed by introducing a suitably driven adaptive rule. To show the effectiveness of the proposed decentralized adaptive controller, a nonlinear system is chosen as a case study. Simulation results are very promising.
  Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on; 11/2007
• Conference Proceeding: Optimal Robot Path Planning Based on Fuzzy Model of Obstacles

  I. Saboori, M.B. Menhaj, B. Karimi
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  ABSTRACT: The Dijkstra method is one of the well-known algorithms in graph theory for determining an optimum path. Some researchers have utilized this algorithm and represented some modified solutions for optimum robot path planning problems. They used a mass-point technique for robot modeling and further the dimensions and shape of robots have not been taken into account. These assumptions make their methods less efficient particularly in places where robot dimensions are comparable with those of the obstacles. This paper presents a more realistic robot modeling in which the dimensions and shape of robots have been taken into consideration. In addition, obstacles have been modeled based on fuzzy concepts. This insures that the robot smoothly tracks a specific optimal path while collision with obstacles is avoided, and further the sensitivity of optimal path to both precise locations and shape of obstacles is relaxed. In order to show the efficiency of the proposed method, we developed a software in Visual Basic 6 environment to simulate the robot path planning problem. Some test cases have been performed. The results of simulations easily approve the outperformance of the proposed method
  IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on; 12/2006
• Conference Proceeding: Robust Adaptive Control of Nonaffine Nonlinear Systems Using Radial Basis Function Neural Networks

  B. Karimi, M.B. Menhaj, I. Saboori
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  ABSTRACT: In this paper, the problem of noise rejection for a class of nonaffine nonlinear systems with parameter uncertainty is considered. We develop a neuro adaptive controller with guaranteed stability by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed controller, the nonlinear Van der Pol oscillator has been chosen as a case study. Simulation results are very promising
  IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on; 12/2006