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Intelligent Control (ISIC), 2013 IEEE International Symposium on

Date 28-30 Aug. 2013

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Displaying Results 1 - 14 of 14
  • Table of contents

    Page(s): 1 - 67
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    Freely Available from IEEE
  • Convergence conditions of iterative learning control revisited: A unified viewpoint to continuous-time and discrete-time cases

    Page(s): 31 - 34
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (115 KB) |  | HTML iconHTML  

    This paper deals with convergence conditions of iterative learning control (ILC) for linear time-varying plants from a unified viewpoint to continuous-time and discrete-time cases. For a continuous-time plant, the corresponding discrete-time plant with a sampling period is obtained via delta operator. Then, a necessary and sufficient condition is given under which the tracking error of the discrete-time ILC converges to zero as the number of iterations tends to infinity. A candidate of ILC convergence condition for the original continuous-time plant is readily obtained by considering the case that the sampling period tends to zero. It is in fact a sufficient condition of convergence, which is shown with a rigorous proof. The condition is based on the supremum of the set of the spectral radius of a time-varying matrix related to the feedthrough term of the plant to its differential output. It is better than any other existing conditions based on induced norm. View full abstract»

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  • Neural network-based adaptive event-triggered control of nonlinear continuous-time systems

    Page(s): 35 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (187 KB) |  | HTML iconHTML  

    In this paper, a neural network (NN) based adaptive event-triggered control is developed for a single input and single output (SISO) uncertain nonlinear continuous time system. An explicit design of the event-triggered controller using NN approximation and feedback linearization is presented. The controller dynamics are approximated by using two single layer NNs. In addition, novel weight update laws are derived for the NNs in the context of event-triggered transmission, i.e., weights are updated only at the triggering instants, hence, aperiodic in nature. The closed loop stability analysis using Lyapunov approach for impulsive dynamical system is carried out to show the uniform ultimate boundedness (UUB) of the NN weight estimation errors as well as system states. Numerical results are included for validating the design. View full abstract»

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  • Finite-horizon optimal adaptive neural network control of uncertain nonlinear discrete-time systems

    Page(s): 41 - 46
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (700 KB) |  | HTML iconHTML  

    In this paper, finite-horizon optimal control design for affine nonlinear discrete-time systems with totally unknown system dynamics is presented. First, a novel neural network (NN)-based identifier is utilized to learn the control coefficient matrix. This identifier is used together with the action-critic-based scheme to learn the time-varying solution, or referred to as value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. To handle the time varying nature of the value function, NNs with constant weights and time-varying activation functions are considered. To satisfy the terminal constraint, an additional term is added to the novel updating law. The uniformly ultimately boundedness of the closed-loop system is demonstrated by using standard Lyapunov theory. The effectiveness of the proposed method is verified by simulation results. View full abstract»

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  • Dynamically re-optimized SNAC controller for robust wing rock suppression

    Page(s): 47 - 52
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (210 KB) |  | HTML iconHTML  

    Following the philosophy of adaptive optimal control, a new technique is presented in this paper for robust optimal regulation of a class of nonlinear systems. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesized offline for optimal regulation of the nominal system. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which done by synthesizing yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilizing the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function so that both the unmodelled part of the dynamics as well as its partial derivatives with respect to the states are captured. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as `Dynamically re-optimized single network adaptive critic (DR-SNAC)'. To demonstrate its effectiveness, the DR-SNAC technique is applied to suppress the wing rock phenomenon of slender delta wings in high angle of attack in presence of significant unmodelled dynamics and the results are quite promising. View full abstract»

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  • Stable neural PID anti-swing control for an overhead crane

    Page(s): 53 - 58
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (346 KB) |  | HTML iconHTML  

    PD with compensation or PID are the most popular algorithms for the overhead crane control. To minimize steady-state error with respect to uncertaintie, PID control needs a big integral gain and the PD with compensator requires a large derivative gain. Both of them deteriorate transient performances of the crane control. In this paper, we propose a novel anti-swing control strategy which combines PID control with neural compensation. The main theory contributions of this paper are semiglobal asymptotic stability of the neural PID for the anti-swing control is proven with standard weights training algorithms. The conditions give explicit selection methods for the gains of the linear PID control. A experimental study on an overhead crane with this neural PID control is addressed. View full abstract»

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  • Certifying non-existence of undesired locally stable equilibria in formation shape control problems

    Page(s): 200 - 205
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (216 KB) |  | HTML iconHTML  

    A fundamental control problem for autonomous vehicle formations is formation shape control, in which the agents must maintain a prescribed formation shape using only information measured or communicated from neighboring agents. While a large and growing literature has recently emerged on distance-based formation shape control, global stability properties remain a significant open problem. Even in four-agent formations, the basic question of whether or not there can exist locally stable incorrect equilibrium shapes remains open. This paper shows how this question can be answered for any size formation in principle using semidefinite programming techniques for semialgebraic problems, involving solutions sets of polynomial equations, inequations, and inequalities. View full abstract»

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  • A swarm model for planar formations of multiple autonomous unmanned aerial vehicles

    Page(s): 206 - 211
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (463 KB) |  | HTML iconHTML  

    A swarm model that induces emergent collective behaviors from the individuals in the swarm is proposed for the planar formations of multiple autonomous unmanned aerial vehicles (UAVs). The stability of the formations, or cohesiveness of the swarm, is guaranteed via the the Direct Method of Lyapunov which is used to construct the instantaneous velocity of each individual. A Lyapunov-like function, from which the velocity controllers are constructed, expresses the three well-known Reynolds' flocking rules as artificial potential fields for inter-individual attraction and avoidance. Basic patterns of planar formation which are similar to emergent behaviors distinctive of swarming in nature are demonstrated via computer simulations. Different emergent patterns are obtained with variations in the system parameters. View full abstract»

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  • Zonotope-based set-membership estimation for Multi-Output uncertain systems

    Page(s): 212 - 217
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (253 KB) |  | HTML iconHTML  

    This paper presents an improved technique for guaranteed zonotopic state estimation of Multi-Output discrete-time linear-time invariant systems subject to unknown but bounded disturbances and measurement noises, in the presence of interval uncertainties. The estimation procedure is based on the minimization of the P-radius of the zonotopic state estimation domain, which guarantees the non-increasing property of the guaranteed zonotopic state estimation at each time instant. This paper proposes an original result on the zonotopic approximation of the intersection between a zonotope (state prediction domain) and a polytope (obtained when considering the measurements of all the system outputs at the same time). View full abstract»

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  • Demonstration of self-sensing in Shape Memory Alloy actuated gripper

    Page(s): 218 - 222
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1033 KB) |  | HTML iconHTML  

    This paper demonstrates the self-sensing behaviour of Shape Memory Alloys (SMAs) in controlling an SMA wire actuated gripper. High power-to-weight ratio, large recovery strain, low driving voltages and direct drive capability of SMA attracted considerable research attention. To make SMA more applicable to small scale robotic manipulations, its motion control using accurate self-sensing is a best alternative. The self-sensing algorithm uses sliding mode control (SMC) in this work. A self-sensing model is built by measuring the electrical resistance of SMA wire. The polyfit model facilitates to replace additional sensor and electronics for position feedback. Demonstration of the advantages gained from using self-sensing polyfit model is evident through stationary trajectory tracking control experiment. The method is accurate and precise in tracking, providing a scope for sensorless control by simplifying the control system. With the merits shown, we expect this technique can be utilized for SMA actuators in meso to micro scale applications. View full abstract»

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  • A novel visual odometry correction using sensor fusion technique on multiple cameras

    Page(s): 352 - 357
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (239 KB) |  | HTML iconHTML  

    Visual odometry estimates the position and orientation of the mobile robot using vision information. A good estimate is always the key for a robotic task implementation. The proposed technique is based on finding the translation component of the robot's movement with low level feature extraction using multiple cameras. The change in successive images from a camera gives an estimate of displacement of the robot while it is moving. This change is found by extracting features using the edge detection and correlation on small portion of two successive images. The proposed faster and computationally less expensive method of feature extraction is not very accurate as compared to existing feature extraction. But, proposed system with multiple cameras does not alter the speed of operation and improves accuracy using simple sensor fusion technique. Proposed method is also merged with odometry using encoder technique and results show further improvement in accuracy. Experiments reported in this paper validate that odometry calculation with multiple cameras using simple direct sensor fusion technique reduces error by atleast 60%. The results demonstrate that the visual odometry is possible with low level feature extraction. View full abstract»

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  • Policy iteration based near-optimal control scheme for robotic manipulator with model uncertainties

    Page(s): 358 - 363
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (348 KB) |  | HTML iconHTML  

    This paper addresses a single network adaptive critic(SNAC) based continuous time near-optimal control strategy for robotic manipulator with partially known dynamics. The optimal control of the robot manipulator is generalized to the control problem that of a continuous time nonlinear input affine system and the solution is obtained through adaptive critic based approach. Such generalization facilitates to achieve near-optimal solution with SNAC, which results in a computationally efficient control scheme. The discussed policy iteration scheme reaches optimality through learning even in the presence of unmodelled dynamics. The validation of the proposed algorithm is done through simulation on a robotic manipulator model. The results show that the near optimal performance is achieved while controlling the manipulator with the proposed SNAC based strategy. View full abstract»

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  • Kernel machines for uncalibrated visual servoing of robots

    Page(s): 364 - 369
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1283 KB) |  | HTML iconHTML  

    A new modelling method of image Jacobian estimation is presented for uncalibrated visual servoing of robots, in which a kernel recursive least squares (KRLS) technique is used for non-linear mapping between target image features and robot joint angles, and an image Jacobian expression is derived from the KRLS algorithm with gaussian kernel. The simulations of robot visual servoing with eye-in-hand camera configuration are conducted using the KRLS Jacobian estimator and the same are compared with SVR and LS-SVM Jacobian estimators. The simulation results have shown that the robot visual servoing converges at the desired goal and KRLS is proved to be a better choice. View full abstract»

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  • Probabilistic analysis of sampling based path planning algorithms

    Page(s): 370 - 375
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (183 KB) |  | HTML iconHTML  

    In this paper we investigate probabilistic completeness and asymptotic optimality of various existing randomized sampling based algorithms such as, probabilistic roadmap methods (PRM) and its many variants. We give new alternate proofs to many such existing theorems regarding probabilistic completeness and asymptotic optimality, in both incremental and independent random problem model framework. View full abstract»

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