This work is concerned with the stereo matching problem for real-time obstacle detection. The correspondence problem is viewed as an optimization task where the objective is to find a solution for which the matches ar...
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This work is concerned with the stereo matching problem for real-time obstacle detection. The correspondence problem is viewed as an optimization task where the objective is to find a solution for which the matches are as compatible as possible with respect to specific constraints. The optimization process is performed by means of a genetic algorithm with a new encoding scheme. For an effective exploitation of the genetic algorithm for real-time obstacle detection, a multilevel searching strategy is proposed in order to speed-up the stereo matching process. The multilevel searching strategy consists of matching the edges at different levels by considering their gradient magnitudes. The performance of the proposed multilevel genetic stereo matching procedure is evaluated for real-time obstacle detection in front of a moving vehicle using linear stereo vision.
This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to z...
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This paper revisits the Arimoto-algorithm in the discrete-time case. It is shown that if a plant satisfies a positivity condition, there always exists a learning gain so that the algorithm converges monotonically to zero tracking error. If the plant does not satisfy the positivity condition, a linear LQ tracker can be used to condition the plant so that it satisfies the positivity condition. The overall structure results in a novel combination of Arimoto ILC and LQ optimal control, that drives the tracking error monotonically to zero for an arbitrary discrete-time LTI plant. This is a very strong property for any ILC algorithm.
The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a g...
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The main objective of this paper is to show how one can benefit from using Iterative Learning control instead of conventional feedback control. As a main result it is shown that even if the nominal plant satisfies a given uncertainty condition, there always exists ILC algorithms that can drive the tracking error monotonically to zero. This same result cannot be achieved with conventional feedback control, or by inverting a nominal model of the plant. Hence ILC offers an unique tool to invert dynamical systems with uncertainty.
This paper investigates the robustness of dual-rate MPC systems with a proposed inferential control strategy. It shows that for some scenarios where a high-frequency model plant mismatch is presented, such dual-rate i...
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This paper investigates the robustness of dual-rate MPC systems with a proposed inferential control strategy. It shows that for some scenarios where a high-frequency model plant mismatch is presented, such dual-rate inferential MPC systems may be more robust than fast single rate MPC systems.
A unified and general framework is presented for H/sub /spl infin// control of mixed continuous-time and discrete-time time-varying (periodic) systems. Using the delta operator, a close relationship is shown between t...
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A unified and general framework is presented for H/sub /spl infin// control of mixed continuous-time and discrete-time time-varying (periodic) systems. Using the delta operator, a close relationship is shown between the continuous- and discrete-time solutions. No assumptions are made on certain system matrices being zero or normalized, which makes the approach general and easy to apply. A combined continuous/discrete-time lifting procedure is shown to be useful, especially for ill-conditioned systems. This procedure together with the delta formalism results in a numerically robust design method concerning both short and long sampling periods for systems with W-conditioned dynamics, including widely spread eigenvalues.
This paper presents the design and experimental results of a Micro Power Generator (MPG) which harvests mechanical energy from its environment and converts this energy into useful electrical power. The energy transduc...
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This paper presents the design and experimental results of a Micro Power Generator (MPG) which harvests mechanical energy from its environment and converts this energy into useful electrical power. The energy transduction component is mainly a magnet and a resonating spring made using SU-8 molding and MEMS electroplating technologies. We have shown that when the MPG is packaged into an AA battery size container along with a power-management circuit that consists of rectifiers and a capacitor, it is capable of producing ~1.6 V DC when charged for less than 1 min. Our goal is to realize a MPG to function with low input mechanical frequencies while producing enough power for low-power wireless applications.
Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al. , 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptot...
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Recently, a novel optimality based Repetitive control algorithm was proposed in (Hätönen et al. , 2003). According to the convergence analysis carried out in that paper, the algorithm will result in asymptotic convergence for an arbitrary discrete-time LTI plant and a T -periodic reference. However, the performance of the algorithm was tested only using simulation studies. In order to rigorously test how the algorithm performs with real systems, in this paper the algorithm is implemented on a non-minimum phase spring-mass-damper system. The results are very satisfactory, because the algorithm results in near perfect tracking with this rather demanding plant type.
A procedure for H/sub /spl infin// optimization of low order controllers for discrete-time and sampled-data systems is presented in this paper. Generally, low order H/sub /spl infin// controllers may be achieved by so...
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A procedure for H/sub /spl infin// optimization of low order controllers for discrete-time and sampled-data systems is presented in this paper. Generally, low order H/sub /spl infin// controllers may be achieved by solving bilinear matrix inequalities (BMIs). In this paper an iterative alternation between two LMIs gives a suboptimal solution. To avoid local minima in this search the initial controller is obtained by a frequency weighted controller reduction scheme, where the closed loop properties of a full order controller is taken into account. A minimal number of parameters in the state space realization of the controller also reduces the complexity and improves numerical robustness. The complete presentation is based on delta operator models, which shows a close relationship between the continuous- and discrete-time solutions. The sensitivity of the ordinary discrete-time shift operator LMI formulation to small sampling periods is also analyzed.
In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a ...
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In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a balance between convergence speed and robustness. Results show that the algorithm is capable of learning the required trajectory in very few iterations. However at this convergence rate the lack of robustness is a major issue. Appropriate use of the tuning parameter is shown to greatly increase the algorithm robustness as demonstrated by tests which successfully reach 600 iterations.
The global and local stability of process systems in generalized Lotka-Volterra form is studied in this paper using entropy-like and quadratic Lyapunov function candidates. The global stability check for LV models is ...
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The global and local stability of process systems in generalized Lotka-Volterra form is studied in this paper using entropy-like and quadratic Lyapunov function candidates. The global stability check for LV models is performed by solving an LMI for a diagonal positive semi-definite matrix using singular perturbation technique. It is shown that a quadratic Lyapunov function can also be determined by solving linear matrix inequalities (LMIs). In addition, the quadratic stability neighborhood is convex in the space of the quasi-monomials and can be estimated by computing its corner points using LMIs. Furthermore, it is proved that quadratic stability with a diagonal weighting matrix enables to construct a dissipative-Hamiltonian description of the system. The developed methods are illustrated on the model of a continuously stirred tank reactor with a nonlinear reaction system.
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