| 1. |
- Abdalmoaty, Mohamed, 1986-, et al.
(författare)
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On Re-Weighting, Regularization Selection, and Transient in Nuclear Norm Based Identification
- 2015
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Ingår i: IFAC-PapersOnLine. - : Elsevier. - 2405-8963. ; 48:28, s. 92-97
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Tidskriftsartikel (refereegranskat)abstract
- In this contribution, we consider the classical problem of estimating an Output Error model given a set of input-output measurements. First, we develop a regularization method based on the re-weighted nuclear norm heuristic. We show that the re-weighting improves the estimate in terms of better fit. Second, we suggest an implementation method that helps in eliminating the regularization parameters from the problem by introducing a constant based on a validation criterion. Finally, we develop a method for considering the effect of the transient when the initial conditions are unknown. A simple numerical example is used to demonstrate the proposed method in comparison to classical and another recent method based on the nuclear norm heuristic.
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| 2. |
- Chistiakova, Tatiana, et al.
(författare)
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Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process
- 2017
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Ingår i: IFAC-PapersOnLine. - : Elsevier B.V.. - 2405-8963. ; 50:1, s. 3917-3922
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Tidskriftsartikel (refereegranskat)abstract
- Aeration of biological reactors in wastewater treatment plants is important to obtain a high removal of soluble organic matter as well as for nitrification but requires a significant use of energy. It is hence of importance to control the aeration rate, for example, by ammonium feedback control. The goal of this paper is to model the dynamics from the set point of an existing dissolved oxygen controller to effluent ammonia using two types of system identification methods for a Hammerstein model, including a newly developed recursive variant. The models are estimated and evaluated using noise corrupted data from a complex mechanistic model (Activated Sludge Model no.1). The performance of the estimated nonlinear models are compared with an estimated linear model and it is shown that the nonlinear models give a significantly better fit to the data. The resulting models may be used for adaptive control (using the recursive Hammerstein variant), gain-scheduling control, L2 stability analysis, and model based fault detection.
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| 5. |
- Ekberg, Kristoffer, 1990-, et al.
(författare)
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A Comparison of Optimal Gear Shifts for Stiff and Flexible Driveshafts During Accelerations
- 2020
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Konferensbidrag (refereegranskat)abstract
- Reducing the fuel consumption is important and much development work is on engine optimization for best stationary fuel consumption. Here, a solution is developed for the transient operation to get fuel optimal accelerations, considering the actuation of fuel injection, wastegate control and gear utilization. The transient acceleration scenario studied is; a truck is approaching a red light at slow rolling speed, the light turns green and the truck shall be accelerated to 50 km/h with minimum fuel. Optimal control is used to find the fuel optimal control strategies. By using a dynamic engine model, taking the turbocharger dynamics into consideration, the engine air fuel ratio is taken into account. The differences and similarities between a stiff and flexible driveline model, are analyzed. The results show that the most dominating effect is the turbocharger dynamics of the engine. The two drivelines have similar gear changing strategies while the finer details differ due to the additional degrees of freedom that are present in the flexible driveline.
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| 7. |
- Karlsson, Jesper, et al.
(författare)
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Sampling-based Motion Planning with Temporal Logic Missions and Spatial Preferences
- 2020
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Konferensbidrag (refereegranskat)abstract
- While motion planning under temporal logic specifications has been addressed in several state-of-the-art works, spatial aspects have been so far largely neglected. In this work, we enrich the semantics of robot motion specifications by including preferences on spatial relations between its trajectory and various elements in its environment. The spatial preferences are given in a fragment of Signal Temporal Logic (STL) on top of complex missions in syntactically co-safe Linear Temporal Logic (scLTL). We propose a cost function with user-specified parameters, which determines the compromise between efficiency and spatial robustness of a trajectory. The proposed modification of the incremental sampling-based RRT$^\star$ driven by this cost function guarantees that the motion plan (if found) simultaneously satisfies the mission and asymptotically minimize the cost. The paper includes several case studies showcasing the effects of the user-adjustable parameters on the resulting trajectories.
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| 8. |
- Malmström, Magnus, 1994-, et al.
(författare)
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Asymptotic Prediction Error Variance for Feedforward Neural Networks
- 2020
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Konferensbidrag (refereegranskat)abstract
- The prediction uncertainty of a neural network is considered from a classical system identification point of view. To know this uncertainty is extremely important when using a network in decision and feedback applications. The asymptotic covariance of the internal parameters in the network due to noise in the observed dependent variables (output) and model class mismatch, i.e., the true system cannot be exactly described by the model class, is first surveyed. This is then applied to the prediction step of the network to get a closed form expression for the asymptotic, in training data information, prediction variance. Another interpretation of this expression is as the non-asymptotic Cramér-Rao Lower Bound. To approximate this expression, only the gradients and residuals, already computed in the gradient descent algorithms commonly used to train neural networks, are needed. Using a toy example, it is illustrated how the uncertainty in the output of a neural network can be estimated.
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| 9. |
- Olsson, Fredrik, et al.
(författare)
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Tremor Severity Rating by Markov Chains
- 2018
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Konferensbidrag (refereegranskat)abstract
- The paper deals with mathematical modeling tools for tremor quantification, a problem arising in e.g. clinical applications and sports. Tremor is an involuntary repetitive movement of extremities, head, or trunk that occurs in disease but also in health, due to e.g. strain or fatigue. Quantification of tremor is traditionally performed by ocular observation, while numerous technologies based on wearable accelerometer data exist and have been tested in medical practice. The currently available approaches rely on spectral analysis that reduces a fundamentally nonlinear and non-stationary phenomenon to a linear combination of harmonic components. The classical nonlinear identification methods are as well of limited use because the underlying system is essentially autonomous and produces sustained oscillations without exogenous excitation. An alternative view on tremor is therefore adopted that treats the problem from a severity rating perspective aligned with clinical practices. The tremor amplitude is modelled by a Markov chain, where the states represent the predefined intervals of severity. A comparison with a previously developed event-based method of tremor quantification is provided on data collected using a smart phone in a patient diagnosed with Parkinson disease and undergoing Deep Brain Stimulation therapy. The experimental procedure is unobtrusive and can be implemented in a way that is transparent to the patient.
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