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14 篇 会议

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

Proceedings of the 1998 IEE Colloquium on Optimization in Co...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

The proceedings contains 10 papers from the iee colloquium on optimization in control: methods and applications: Topics discussed include: evolutionary methods for engineering systems optimization;optimization using population-based incremental learning;adaptive simulated annealing for controller design;neural network identification by gradient based methods;second variation method and optimal control;boundary layer transition control;missile guidance algorithms generation;neuromorphic remotely operated vehicle propulsion control;nonlinear stabilization and regulation via optimal gain schedule;and genetic algorithms for active controls optimization in railway vehicles.

关键词： optimization

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Evolutionary methods for the optimization of engineering systems

Evolutionary methods for the optimization of engineering sys...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Bureerat, S. Cooper, J.E. Univ of Manchester Manchester United Kingdom

The use of Evolutionary methods for the optimization of engineering systems is reviewed and a number of the most common methods are compared in terms of their philosophical basis and implementation. The use of these approaches, including Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies, Simulated Annealing and Population Based Incremental Learning, are illustrated using the shape optimization problem of a steel plate with buckling and stress constraints. The solutions obtained using the various methods are compared.

关键词： optimization

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Neural network identification: A survey of gradient based methods

Neural network identification: A survey of gradient based me...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： McLoone, Sean Queen's Univ Belfast Belfast United Kingdom

System identification can be viewed as an optimization problem in which a performance index is minimized with respect to the parameters being identified. From this perspective, neural modeling structures can be classified as linear, nonlinear or mixed linear-nonlinear (hybrid) in the parameters. Neural network identification can then be achieved by linear, nonlinear or hybrid optimization, also called gradient based optimization.

关键词： Neural networks

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Adaptive simulated annealing for designing finite-precision PID controller structures

Adaptive simulated annealing for designing finite-precision ...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Chen, S. Istepanian, R.H. Whidborne, J.F. Wu, J. Univ of Portsmouth Portsmouth United Kingdom

Many control applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations. The adaptive simulated annealing (ASA) offers a viable optimization tool for tackling these difficult constrained optimization problems. We demonstrate the effectiveness of the ASA using an application, designing finite-precision PID controller structures with maximum stability robustness measure. The sparseness issue of controller structures is also discussed, and it is shown that the ASA can cope with this kind of constraints easily.

关键词： control system synthesis

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optimization using Population Based Incremental Learning (PBIL)

Optimization using Population Based Incremental Learning (PB...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Hughes, Evan J. Cranfield Univ Shrivenham United Kingdom

The population based incremental learning (PBIL) algorithm is a simple stochastic optimization technique that can be applied quickly to a wide range of problems. A practical approach to applying the PBIL algorithm to optimization problems is described. First, the operation of the algorithm is described and then guidelines for tuning the algorithm are presented. An example implementation is given and a method for reducing the processing burden of the algorithm is detailed. An example MATLAB routine demonstrate its simplicity.

关键词： optimization

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optimization based control of boundary layer transition - theory and experimentation

Optimization based control of boundary layer transition - th...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： McCormack, W. Tutty, O.R. Rogers, E. Nelson, P.A. Univ of Southampton Southampton United Kingdom

Distributed suction can be applied to automatically control boundary layer transition to reduce drag on engine nacelles and hybrid laminar flow control aerofoils and wings, thereby reducing the operating costs of civil aircrafts. To achieve a specified transition position, the suction distribution which achieves the minimum energy cost must be sought. Also, it is highly desirable to maximize the energy profit. Given these requirements, the route to design is obviously via an appropriate formulated nonlinear constrained optimization problem. A number of methods have been used to solve such problems, namely gradient descent methods, genetic algorithms, and simulated annealing. These methods, along with relative performance comparisons, are discussed.

关键词： Flow control

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Non-linear stabilization and regulation via an optimal gain schedule

Non-linear stabilization and regulation via an optimal gain ...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Harrison, Robert F. Univ of Sheffield Sheffield United Kingdom

A nonlinear design method based on linear quadratic optimal control theory that applies to a wide class of nonlinear system is presented. The method is easy to apply and results in an asymptotically optimal solution that can be implemented in real-time, as the solution is causal. Key feature to the method is the introduction of state-dependence in the wright matrices of the usual linear quadratic cost function, leading to a nonlinear control policy. The method is demonstrated using a simple nonlinear vehicle suspension model with a cubic damping force.

关键词： Nonlinear control systems

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Genetic algorithms for optimizing active controls in railway vehicles

Genetic algorithms for optimizing active controls in railway...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Mei, T.X. Foo, T.H.E. Goodall, R.M. Loughborough Univ Loughborough United Kingdom

This paper presents optimizations of active control designs for railway vehicle suspensions using Genetic Algorithms. Genetic Algorithm (GA in short) is a stochastic process aimed at providing global optimization solutions for a wide range of applications. In this paper, two active suspension controls are designed for the railway vehicles with the aid of GA. The paper first studies the controls for actively steering wheelsets of railway vehicles. The basic aim of a controller is to stabilize the potentially unstable vehicles and to improve the ride quality without interfering with the natural curving action of the solid axle wheelsets. The Genetic Algorithm is used to assist the design of an optimal controller by choosing the weighting factors in order to achieve the best performance with the minimum interference to curving. In the second study, `classical controllers' controlling the front and rear ideal actuators of a flexible vehicle body are investigated. The aim is to minimize the flexible effect of the railway vehicle thereby improving the ride quality. GA is used to fine-tune the gains of the controllers in order to obtain the best overall ride quality of the entire vehicle body.

关键词： Optimal control systems

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Neuromorphic ROV propulsion control based upon model-reference non-linear optimization

Neuromorphic ROV propulsion control based upon model-referen...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Suttiwaree, K. Lucas, J. Univ of Liverpool Liverpool United Kingdom

Thruster Propulsion is a significant component for making accurate motion control possible as well as achieving superior navigational performance of Remotely Operated Vehicles (ROVs), both for tethered and untethered classes. As a result, the contents of this paper mainly focus upon thruster functioning enhancement by forming an effective real-time closed-loop compensation. An electric-driven thruster, instrumented with drive and embedded electronics, is employed as a test-bed. Its electrical and mechanical properties are briefly described and the inherent existence of stiction (static friction) will be highlighted since such non-linearity directly degrades the dynamic behaviour of the thruster. Therefore, a non-linear optimization technique, serving as a 1-step-ahead predictive controller, has been structured not only to eliminate this unwanted stiction effect, but also to have the following additional attributes: 1) to maintain commanded rotational direction, 2) to regulate angular velocity with respect to set-point, and 3) to produce a robust system with better noise immunity. The intelligent predictive algorithm is based upon a Multi-Layer Perceptron (MLP) neural network configured as a state-estimator, whereas a model-reference gives the desired response characteristic. The outcome of such combined modules will clearly outperform the classical methodologies as indicated by the comparison with: i) an ordinary predictive strategy, and ii) an industrial three-term PID regulation.

关键词： Motion control

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Second variation method and optimal control in the presence of model-reality differences

Second variation method and optimal control in the presence ...

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proceedings of the 1998 iee colloquium on optimization in control: methods and applications

作者： Farid, Mohammad Roberts, Peter D. City Univ London United Kingdom

This article concerns the finite horizon optimal control of continuous-time deterministic processes in the presence of model-reality differences. The latter occurs either due to the complexity of the real physical process and hence inability in accurate and exact modelling or the desire of the control engineers in employing simplified models, e.g. linear time varying models. A typical optimal control algorithm in the presence of model-reality differences may be as follows. A nominal control signal is applied to the process and all the states in the process are measured for the duration of time horizon (let us assume this is possible). Using the state measurements, one can construct a linear time-varying model for the small variations in state and control around the nominal trajectories, i.e. a small signal model. Now by solving a minimization problem based on the linear time-varying model, one can calculate a new control signal which reduces the objective function compared to the previous step. The new control signal is applied to the process and all the calculations are repeated again. These calculations continue iteratively until the control signal converges to its optimal value. In this algorithm instead of using the exact physical model of the process, a linear time-varying model is used which is updated after each iteration. This makes the calculation of optimal control in each iteration much easier.

关键词： Optimal control systems

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