| 1. |
- Ali, Mohammad, 1982, et al.
(författare)
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Model-Based Threat Assessment for Lane Guidance Systems
- 2011
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Ingår i: American Control Conference. - 0743-1619. - 9781457700804 ; , s. 4586-4591
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Konferensbidrag (refereegranskat)abstract
- This paper considers a threat assessment problem in a lane guidance application for semi-autonomous vehicles. In particular, in order to issue an autonomous assisting intervention, we assess the vehicle’s ability to safely travel along a path subject to limitations arising from the vehicle’s dynamics and the driver’s ability.We first introduce a set of constraints describing “safedriving”. For the specific lane guidance application considered in this paper, the constraints are set by the lane boundaries and the vehicle’s stability limits. We then formulate the threat assessment problem as a constraints satisfaction problem over a finite time horizon, solved by resorting to reachabililty analysisand invariant set theory.Validation with experimental data demonstrates the capability of the proposed threat assessment method of predicting vehicle instability or crossing of the lane boundaries.
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| 2. |
- Andersson, Alina, et al.
(författare)
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Analytic Parameterization of Stabilizing Controllers for the Surge Subsystem of the Moore-Greitzer Compressor Model
- 2013
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Ingår i: Proc. 2013 American Control Conference (ACC2013). - 0743-1619. - 9781479901777 ; , s. 5257-5262
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Konferensbidrag (refereegranskat)abstract
- This paper is based on a new procedure for dynamic output feedback design for systems with nonlinearities satisfying quadratic constraints. The new procedure is motivated by the challenges of output feedback control design for the 3-state Moore-Greitzer compressor model. First, we use conditions for stability of a transformed system and the associated matching conditions to find the data of the stabilizing controllers for the surge subsystem. Second, using the set of stabilizing controllers satisfying the given constraints for the closed-loop system with the dynamic output feedback controller we made optimization over the parameter set. We present the data of the stabilizing controllers and the new constraints for the corresponding parameters. The contributions in this paper are simplified conditions for the synthesis and optimization over the control parameter set.
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| 3. |
- Berntorp, Karl, et al.
(författare)
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Models and Methodology for Optimal Vehicle Maneuvers Applied to a Hairpin Turn
- 2013
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Ingår i: 2013 American Control Conference. - 0743-1619. - 9781479901777 ; , s. 2139-2146
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Konferensbidrag (refereegranskat)abstract
- There is currently a strongly growing interest in obtaining optimal control solutions for vehicle maneuvers, both in order to understand optimal vehicle behavior and to devise improved safety systems, either by direct deployment of the solutions or by including mimicked driving techniques of professional drivers. However, it is nontrivial to find the right mix of models, formulations, and optimization tools to get useful results for the above purposes. Here, a platform is developed based on a state-of-the-art optimization tool together with adoption of existing vehicle models, where especially the tire models are in focus. A minimum-time formulation is chosen to the purpose of gaining insight in at-the-limit maneuvers, with the overall aim of possibly finding improved principles for future active safety systems. We present optimal maneuvers for different tire models with a common vehicle motion model, and the results are analyzed and discussed. Our main result is that a few-state single-track model combined with different tire models is able to replicate the behavior of experienced drivers. Further, we show that the different tire models give quantitatively different behavior in the optimal control of the vehicle in the maneuver.
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| 4. |
- Berntorp, Karl, et al.
(författare)
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Path Tracking with Obstacle Avoidance for Pseudo-Omnidirectional Mobile Robots Using Convex Optimization
- 2014
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Ingår i: [Host publication title missing]. - 0743-1619. ; , s. 517-524
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Konferensbidrag (refereegranskat)abstract
- We consider the problem of trajectory generation for time-optimal path tracking for the class of pseudo-omnidirectional mobile robots. An Euler-Lagrange model of the robot dynamics is derived, and by writing it on special form a convex reformulation of the path-tracking problem can be utilized. This enables the use and regeneration of time-optimal trajectories during runtime. The proposed approach also incorporates avoidance of moving obstacles, which are unknown a priori. Using sensor data, objects along the desired path are detected. Subsequently, a new path is planned and the corresponding time-optimal trajectory is found. The robustness of the method and its sensitivity to model errors are analyzed and discussed with extensive simulation results. Moreover, we verify the approach by successful execution on a physical setup.
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| 5. |
- Chasparis, Georgios, et al.
(författare)
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Distributed Management of CPU Resources for Time-Sensitive Applications
- 2013
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Ingår i: [Host publication title missing]. - 0743-1619. ; , s. 5305-5312
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Konferensbidrag (refereegranskat)abstract
- The number of applications sharing the same embedded device is increasing dramatically. Very efficient mechanisms (resource managers) for assigning the CPU time to all demanding applications are needed. Unfortunately, existing optimization-based resource managers consume too much resource themselves. In this paper, we address the problem of distributed convergence to fair allocation of CPU resources for time-sensitive applications. We propose a novel resource management framework where both applications and the resource manager act independently trying to maximize their own performance measure according to a utility-based adjustment process. Contrary to prior work on centralized optimization schemes, the proposed framework exhibits adaptivity and robustness to changes both in the number and nature of applications, while it assumes minimum information available to both applications and the resource manager. It is shown analytically that fair resource allocation can be achieved through the proposed adjustment process when the CPU is overloaded. Experiments using the TrueTime Matlab toolbox show the validity of the proposed approach.
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| 6. |
- Giselsson, Pontus
(författare)
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A generalized distributed accelerated gradient method for distributed model predictive control with iteration complexity bounds
- 2013
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Ingår i: [Host publication title missing]. - 0743-1619. ; , s. 327-333
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Konferensbidrag (refereegranskat)abstract
- Most distributed optimization methods used for distributed model predictive control (DMPC) are gradient based. Gradient based optimization algorithms are known to have iterations of low complexity. However, the number of iterations needed to achieve satisfactory accuracy might be significant. This is not a desirable characteristic for distributed optimization in distributed model predictive control. Rather, the number of iterations should be kept low to reduce communication requirements, while the complexity within an iteration can be significant. By incorporating Hessian information in a distributed accelerated gradient method in a well-defined manner, we are able to significantly reduce the number of iterations needed to achieve satisfactory accuracy in the solutions, compared to distributed methods that are strictly gradient-based. Further, we provide convergence rate results and iteration complexity bounds for the developed algorithm.
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| 7. |
- Giselsson, Pontus
(författare)
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Optimal preconditioning and iteration complexity bounds for gradient-based optimization in model predictive control
- 2013
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Ingår i: [Host publication title missing]. - 0743-1619. ; , s. 358-364
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Konferensbidrag (refereegranskat)abstract
- In this paper, optimization problems arising in model predictive control (MPC) and in distributed MPC aresolved by applying a fast gradient method to the dual of the MPC optimization problem. Although the development of fast gradient methods has improved the convergence rate of gradient-based methods considerably, they are still sensitive to ill-conditioning of the problem data. Since similar optimization problems are solved several times in the MPC controller, the optimization data can be preconditioned offline to improve the convergence rate of the fast gradient method online. A natural approach to precondition the dual problem is to minimize the condition number of the Hessian matrix. However, in MPC the Hessian matrix usually becomes positive semi-definite only, i.e., the condition number is infinite and cannot be minimized. In this paper, we show how to optimally precondition the optimization data by solving a semidefinite program, where optimally refers to the preconditioning that minimizes an explicit iteration complexity bound. Although the iteration bounds can be crude, numerical examples show that the preconditioning can significantly reduce the number of iterations needed to achieve a prespecified accuracy of the solution.
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| 8. |
- Giselsson, Pontus
(författare)
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Output feedback distributed model predictive control with inherent robustness properties
- 2013
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Ingår i: [Host publication title missing]. - 0743-1619. ; , s. 1691-1696
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Konferensbidrag (refereegranskat)abstract
- We consider robust output feedback distributed model predictive control (DMPC). The proposed controller is based on the results in [8] in which nominal stability and feasibility was proven for a DMPC-formulation without terminal constraint set or terminal cost in the optimization. We extend these results to show robust stability under state feedback as well as output feedback when dynamics and measurements are affected by bounded noise. The provided numerical example suggests that the region of attraction without terminal constraint set may be significantly larger than if a terminal constraint set is used.
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| 9. |
- Hedegärd, Marcus, 1979, et al.
(författare)
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Convex identification of models for asymmetric hysteresis
- 2014
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Ingår i: American Control Conference. - 0743-1619. - 9781479932726 ; , s. 4753-4758
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Konferensbidrag (refereegranskat)abstract
- The generalized Prandtl-Ishlinskii model (GPI) of hysteresis has a wide applicability, partly because of its capability of modelling highly asymmetric hysteresis. A disadvantage of the GPI models compared to, for example the Modified Prandtl-Ishlinskii models, has been that they have had to be identified using parametric non convex methods. Recently though, a method for non-parametric convex identification for an extended and more general GPI model was described, giving all model functions. Here, the method, which was based on input discrete equations, is briefly presented in terms of the corresponding input continuous equations. This extended model corresponds to a Preisach model and an explicit expression for this relation is derived. The method is directly applicable to data consisting of first order reversal curves, but in an application to an electrical substation equipment, it is shown that other kinds of data can also be used. The method gives significantly closer fit to this data than previous studies, and it demonstrates that non-equal left and right envelope functions are optimal.
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| 10. |
- Henningsson, Maria, et al.
(författare)
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Dynamic Mapping of Diesel Engine through System Identification
- 2010
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Ingår i: Proceedings of the 2010 American Control Conference. - 2378-5861 .- 0743-1619. - 9781424474264 - 9781424474271 ; , s. 3015-3020
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Konferensbidrag (refereegranskat)abstract
- From a control design point of view, modern diesel engines are dynamic, nonlinear, MIMO systems. This paper presents a method to find low-complexity black-box dynamic models suitable for model predictive control (MPC) of NOx and soot emissions based on on-line emissions measurements. A four-input-five-output representation of the engine is considered, with fuel injection timing, fuel injection duration, exhaust gas recirculation (EGR) and variable geometry turbo (VGT) valve positions as inputs, and indicated mean effective pressure, combustion phasing, peak pressure derivative, NOx emissions, and soot emissions as outputs. Experimental data were collected on a six-cylinder heavy-duty engine at 30 operating points. The identification procedure starts by identifying local linear models at each operating point. To reduce the number of dynamic models necessary to describe the engine dynamics, Wiener models are introduced and a clustering algorithm is proposed. A resulting set of two to five dynamic models is shown to be able to predict all outputs at all operating points with good accuracy.
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