This note considers the problem of determining optimal switching times at which mode transitions should occur in multimodal, hybrid systems. It derives a simple formula for the gradient of the cost functional with res...
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This note considers the problem of determining optimal switching times at which mode transitions should occur in multimodal, hybrid systems. It derives a simple formula for the gradient of the cost functional with respect to the switching times, and uses it in a gradient-descent algorithm. Much of the analysis is carried out in the setting of optimization problems involving fixed switching-mode sequences, but a possible extension is pointed out for the case where the switching-mode sequence is a part of the variable. Numerical examples testify to the viability of the proposed approach.
The detection of atmospheric methane on Mars implies an active methane source. This introduces the possibility of a biotic source with the implied need to determine whether the methane is indeed biotic in nature or ge...
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The detection of atmospheric methane on Mars implies an active methane source. This introduces the possibility of a biotic source with the implied need to determine whether the methane is indeed biotic in nature or geologically generated. There is a clear need for robotic algorithms which are capable of manoeuvring a rover through a methane plume on Mars to locate its source. We explore aspects of Mars methane plume modelling to reveal complex dynamics characterized by advection and diffusion. A statistical analysis of the plume model has been performed and compared to analyses of terrestrial plume models. Finally, we consider a robotic search strategy to find a methane plume source. We find that gradient-based techniques are ineffective, but that more sophisticated model-based search strategies are unlikely to be available in near-term rover missions.
Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the recei...
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ISBN:
(纸本)9781509042289
Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.
This paper proposes an algorithmic framework for optimal mode switches in hybrid dynamical systems. The problem is cast in the setting of optimal control, whose variable parameter consists of the switching times, and ...
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This paper proposes an algorithmic framework for optimal mode switches in hybrid dynamical systems. The problem is cast in the setting of optimal control, whose variable parameter consists of the switching times, and whose associated cost criterion is a functional of the state trajectory. The number of switching times (and hence of switching modes) is also a variable which may be unbounded, and therefore the optimization problem is not defined on a single metric space. Rather, it is defined on a sequence of spaces of possibly increasing dimensions. The paper characterizes optimality in terms of sequences of optimality functions and proposes an algorithm that is demonstrably convergent in this context.
This paper concerns the problem of optimal switching control in voltage converter circuits, where the objective is to minimize a cost-performance function comprised of the sum of a tracking-related measure and the swi...
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This paper concerns the problem of optimal switching control in voltage converter circuits, where the objective is to minimize a cost-performance function comprised of the sum of a tracking-related measure and the switching energy. Most of the existing approaches to optimal switching are based on continuous-parameter optimization and optimal control techniques, which are mostly suitable to continuous-parameter functions such as tracking-related performance metrics. On the other hand, the switching-energy cost performance is inherently a discontinuous function dependent on the number of switchings, and hence its inclusion in the problem often is done in ad-hoc ways. This paper explores a systematic approach to optimizing performance - energy tradeoffs by extending an algorithm for optimizing tracking, developed by the authors, to include the energy performance via an averaging technique. The problem is posed in the setting of Pulse Width Modulation, and the controlled variables are the cycle time and duty ratios at each cycle. Extensive simulation results suggest the potential generality of the proposed approach.
The simple PID controller can't get the satisfied degree, especially for the time-varying objects and non-linear systems, the traditional PID controllers can do nothing for them. to non-linear systems, the NN PID ...
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ISBN:
(纸本)9781467355322;9781467355339
The simple PID controller can't get the satisfied degree, especially for the time-varying objects and non-linear systems, the traditional PID controllers can do nothing for them. to non-linear systems, the NN PID controller has a good controller effect in the non-line premature turning and optimizing. The NN PID controller can make both neural network and PID control into an organic whole , which has the merit of any PID controller for its Simple construction and definite physical meaning of parameters, and also has the self learning and adaptive functions of a neural network. Radial basis function neural network(RBFNN) is a kind of three-layer feed forward neural network with single hidden layer, there is Great difference between it's structure and learning algorithms with BP neural network 's. so, in the Paper , the NN PID is used to achieve PID parameters self adjustments on RBF NN identification. an improved single neural adaptive PID controller is presented and PID control based on BPNN is studied in detail. A new self-adaptive learning model of RBF neural net work as established successfully.
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