Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinea...
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for the identification of nonlinear state-space models. However, if the resulting models are used directly for nonlinear model predictive control (NMPC), the resulting nonlinear optimization problem is often overly complex due to the size of the network, requires the use of high-order observers to track the states of the ANN model, and the overall control scheme does not exploit the available autograd tools for these models. In this paper, we propose an efficient approach to auto-convert ANN statespace models to linear parameter-varying (LPV) form and solve predictive control problems by successive solutions of linear model predictive problems, corresponding to quadratic programs (QPs). Furthermore, we show how existing deep-learning methods, such as SUBNET that uses a state encoder, enable efficient implementation of MPCs on identified ANN models. Performance of the proposed approach is demonstrated by a simulation study on an unbalanced disc system.
For various applications, information models described in standards are available to enable a digital data exchange. For modular process plants, where different domains are interacting in different phases of the plant...
For various applications, information models described in standards are available to enable a digital data exchange. For modular process plants, where different domains are interacting in different phases of the plant lifecycle, single, existing standards are not *** this work, we analyze the most relevant standards for process engineering, namely OntoCAPE, DEXPI, and IEC 61512, in regards to their application to support the lifecycle of modular process plants, which are described in the VDI 2776 and VDI/VDE/NAMUR 2658 standards. By challenging the standards with competency questions derived from a specific use case, we discuss which information models are most suitable for all individual domains in each lifecycle phase. The result is the specification of parts of these standards which are needed to create a fully digital support for modular process plants on the example of the introduced use case.
Ananthram and Borkar [1] showed that there exist strategies that are consistent with the requirements of a decentralized information structure but are unattainable through the use of common randomness. This opens the ...
Ananthram and Borkar [1] showed that there exist strategies that are consistent with the requirements of a decentralized information structure but are unattainable through the use of common randomness. This opens the question of discovering physically realisable mechanisms that provide access to this region of the strategic space. In our previous work we introduced a class of quantum strategies that allow such access in a two-agent setting. In this paper, we consider the problem of optimal allocation of a $k$ -partite quantum resource amongst $n$ agents, $k < n$ . We study the problem of decentralized estimation of a binary source by agents that are informed through independent binary symmetric channels, and face a cost that is homogeneous in their actions. We show a $k$ -partite quantum resource produces the maximum advantage over classical strategies when allocated to the agents with the $k$ most reliable channels.
Given a partially observable Markov decision process (POMDP) with finite state, input and measurement spaces, and costly measurements and control, we consider the problem of when to sample and actuate. Both sampling a...
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ISBN:
(数字)9781665467612
ISBN:
(纸本)9781665467629
Given a partially observable Markov decision process (POMDP) with finite state, input and measurement spaces, and costly measurements and control, we consider the problem of when to sample and actuate. Both sampling and actuation are modeled as control actions in a framework encompassing estimation and intervention problems. The process evolves freely between two consecutive control action times. control actions are assumed to reset the conditional distribution of the state given the measurements to one of a finite number of distributions. We tackle the problem of deciding when control actions should occur in order to minimize an average cost that penalizes states and the rate of control actions. The problem is first shown to boil down to a stopping time problem. While the latter can be solved optimally, the complexity of the optimal policy is intractable. Thus, we propose two approximate methods. The first is inspired by relaxed dynamic programming, and it is within an additive cost factor of the optimal policy. The second is inspired by consistent event-triggered control and ensures that the cost is smaller than that of periodic control for the same control rate. We conclude that the latter policy can deal with large dimensional problems, as demonstrated in the context of precision agriculture.
In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimensi...
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We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control problem in terms of a finite number of conv...
ISBN:
(数字)9783907144077
ISBN:
(纸本)9781665497336
We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control problem in terms of a finite number of convex sub-problems. The reformulation is facilitated by exact (input-state) linearization, which is shown to provide beneficial properties for the treated class of systems. We characterize possible types of the resulting convex subproblems and illustrate our approach with three numerical examples.
We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control problem in terms of a finite number of conv...
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In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimensi...
In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques. This new technique will reduce the dimension of the noise disturbance and will allow any controller designed for the reduced model to be refined into a controller for the original stochastic system, while preserving any specification on the output. Although initially the reduced model will be time-varying, a method will be provided with which the reduced model can become time-invariant if it satisfies some minor technical conditions. We present our theoretical findings with an example that supports the proposed framework and illustrates how model reduction and controller refinement of stochastic systems can be achieved. We finish the paper by considering specific examples to analyze both completeness with respect to controller synthesis and model order reduction with respect to the state.
The tracking accuracy in motion controlsystems like the moving stages in lithography machines, e.g., wafer scanners or metrology inspection tools, is partly determined by how the frequency content of its reference tr...
The tracking accuracy in motion controlsystems like the moving stages in lithography machines, e.g., wafer scanners or metrology inspection tools, is partly determined by how the frequency content of its reference trajectories is transferred to the closed-loop tracking error. In this regard, fourth-order reference trajectories for point-to-point motion will be studied from a frequency-domain perspective. By appropriately pairing the maximum snap and maximum jerk values, weakly-damped modes in the closed-loop response can be robustly dealt with without introducing a penalty on throughput.
We show that the explicit realization of data-driven predictive control (DPC) for linear deterministic systems is more tractable than previously thought. To this end, we compare the optimal control problems (OCP) corr...
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