This brief studies the causal consensus problem of singular (descriptor) multi-agent systems with networked communication delays and agents described by general singular systems. For the studied systems, only the info...
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This contribution is concerned with an emergency steering assist and its evaluation through subject testing in a driving simulator. In an emergency traffic situation where a rear end collision is imminent a swerving m...
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ISBN:
(纸本)9781479938414
This contribution is concerned with an emergency steering assist and its evaluation through subject testing in a driving simulator. In an emergency traffic situation where a rear end collision is imminent a swerving maneuver is often too difficult for most drivers. Therefore an assistance system is usefull that supports the driver by steering torque overlay. The paper presents the algorithm used for the assistance. The system was prototypically implemented in a driving simulator and testet with subjects to evaluate the benefits and challenges. The results show that the collision avoidance behaviour of the driver can be improved by the emergency steering assist.
This paper proposes a method for the construction of piecewise affine terminal cost functions for model predictive control (MPC). The terminal cost function is constructed on a predefined partition by solving a linear...
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ISBN:
(纸本)9781450327329
This paper proposes a method for the construction of piecewise affine terminal cost functions for model predictive control (MPC). The terminal cost function is constructed on a predefined partition by solving a linear program for a given piecewise affine system, a stabilizing piecewise affine controller, an invariant set and a piecewise convex stage cost function. The constructed terminal cost function satisfies the sufficient conditions for asymptotic stability if it is used in anMPC scheme. In general, the constructed terminal cost function will be nonconvex. However, optional additional constraints in the linear program ensure that the cost function is convex, reducing the computational effort involved with solving the MPC optimization problem. Multiple examples illustrate the approach. Copyright 2014 ACM.
This study suggests a moving horizon H8 control scheme for variable speed wind turbines above the rated wind speed to maintain the output power at the rated value for variable operating points. A constrainedH8 control...
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In this paper, we present a distributed estimation setup where local agents estimate their states from relative measurements received from their neighbours. In the case of heterogeneous multi-agent systems, where only...
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Underactuated mechanical systems are systems with less actuators than degrees of freedom. Therefore, it is complicated to measure all states, i.e. angular positions or angular velocities, of the mechanical system. Alt...
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In this paper, the task of finding an algorithm providing sufficiently excited data within the MPC framework is tackled. Such algorithm is expected to take action only when the re-identification is needed and it shall...
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In this paper, the task of finding an algorithm providing sufficiently excited data within the MPC framework is tackled. Such algorithm is expected to take action only when the re-identification is needed and it shall be used as the “least costly” closed loop identification experiment for MPC. The already existing approach based on maximization of the smallest eigenvalue of the information matrix increase is revised and an adaptation by introducing a semi-receding horizon principle is performed. Further, the optimization algorithm used for the maximization of the provided information is adapted such that the constraints on the maximal allowed control performance deterioration are handled more carefully and are incorporated directly into the process instead of using them just as a termination condition. The effect of the performed adaptations is inspected using a numerical example. The example shows that the employment of the semi-receding horizon brings major improvement of the identification properties of the obtained data and the proposed adaptive-search step algorithm used for the “informativeness” optimization brings further significant increase of the contained information while the aggravation of the economical and tracking aspects of the control are kept at acceptable level.
A method to ensure recursive feasibility and asymptotic stability for a distributed model predictive control scheme in case of temporary communication failure is presented. The considered networks are made up of dynam...
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A method to ensure recursive feasibility and asymptotic stability for a distributed model predictive control scheme in case of temporary communication failure is presented. The considered networks are made up of dynamically uncoupled nonlinear systems, coupled through constraints and objectives. The proposed method substitutes affected coupling constraints in such a way that the scheme can not become infeasible, while a decrease in the cost is guaranteed.
In this paper we address an inverse problem on populations described by probability distributions. From a theoretical point of view, this problem can be seen as a natural extension to the classical observability probl...
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ISBN:
(纸本)9781467360890
In this paper we address an inverse problem on populations described by probability distributions. From a theoretical point of view, this problem can be seen as a natural extension to the classical observability problem. We consider a population that is described by a classical linear finite-dimensional system in which the initial state is a random vector subject to a non-parametric probability distribution. The problem is to reconstruct this initial state distribution from the time-evolution of the probability distribution of the output. We reveal as a novel viewpoint, that, at its core, this problem is a tomography problem which is a well-known subject in the field of inverse problems. Furthermore we show how this tomography problem is inherently linked with the observability properties of the finite-dimensional system thereby establishing a beautiful link between a control theoretic question and tomography problems.
In this paper we propose a robust self-triggered model predictive control algorithm for linear systems with additive bounded disturbances and hard constraints on the inputs and state. In self-triggered control, at eve...
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In this paper we propose a robust self-triggered model predictive control algorithm for linear systems with additive bounded disturbances and hard constraints on the inputs and state. In self-triggered control, at every sampling instant the time until the next sampling instant is computed online based on the current state of the system. The goal is to achieve a low average sampling rate, thereby minimizing communication in the control system and possibly reducing the number of control updates as is required in sparse control applications. Naturally, and intentionally, our approach leads to long spans of time in which the plant is controlled in an open-loop fashion. Especially for unstable plants or large disturbances this necessitates taking into account the disturbance characteristics in the design of the control law in order to prevent constraint violation in the closed-loop system. We use constraint tightening methods as proposed in Tube Model Predictive control to guarantee robust constraint satisfaction. The self-triggered controller is shown to stabilize a robust invariant set in the state space for the closed-loop system.
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