Model Predictive Control for Vehicle Stabilization at the Limits of Handling

Recent developments in vehicle steering systems offer new opportunities to measure the steering torque and reliably estimate the vehicle sideslip and the tire-road friction coefficient. This paper presents an approach to vehicle stabilization that leverages these estimates to define state boundaries that exclude unstable vehicle dynamics and utilizes a model predictive envelope controller to bound the vehicle motion within this stable region of the state space. This approach provides a large operating region accessible by the driver and smooth interventions at the stability boundaries. Experimental results obtained with a steer-by-wire vehicle and a proof of envelope invariance demonstrate the efficacy of the envelope controller in controlling the vehicle at the limits of handling.

'Security Here is Not Safe': Violence, Punishment, & Space in the Contemporary U.S. Penitentiary

The US penitentiary at Lewisburg, Pennsylvania, was retrofitted in 2008 to offer the country’s first federal Special Management Unit (SMU) program of its kind. This model SMU is designed for federal inmates from around the country identified as the most intractably troublesome, and features double-celling of inmates in tiny spaces, in 23-hour or 24-hour a day lockdown, requiring them to pass through a two-year program of readjustment. These spatial tactics, and the philosophy of punishment underlying them, contrast with the modern reform ideals upon which the prison was designed and built in 1932. The SMU represents the latest punitive phase in American penology, one that neither simply eliminates men as in the premodern spectacle, nor creates the docile, rehabilitated bodies of the modern panopticon; rather, it is a late-modern structure that produces only fear, terror, violence, and death. This SMU represents the latest of the late-modern prisons, similar to other supermax facilities in the US but offering its own unique system of punishment as well. While the prison exists within the system of American law and jurisprudence, it also manifests features of Agamben’s lawless, camp-like space that emerges during a state of exception, exempt from outside scrutiny with inmate treatment typically beyond the scope of the law.

"Security Here is Not Safe": Violence, Punishment, and Space in the Contemporary US Penitentiary

The US penitentiary at Lewisburg, Pennsylvania, was retrofitted in 2008 to offer the country's first federal Special Management Unit (SMU) program of its kind. This model SMU is designed for federal inmates from around the country identified as the most intractably troublesome, and features double-celling of inmates in tiny spaces, in 23-hour or 24-hour a day lockdown, requiring them to pass through a two-year program of readjustment. These spatial tactics, and the philosophy of punishment underlying them, contrast with the modern reform ideals upon which the prison was designed and built in 1932. The SMU represents the latest punitive phase in American penology, one that neither simply eliminates men as in the premodern spectacle, nor creates the docile, rehabilitated bodies of the modern panopticon; rather, it is a late-modern structure that produces only fear, terror, violence, and death. This SMU represents the latest of the late-modern prisons, similar to other supermax facilities in the US but offering its own unique system of punishment as well. While the prison exists within the system of American law and jurisprudence, it also manifests features of Agamben's lawless, camp-like space that emerges during a state of exception, exempt from outside scrutiny with inmate treatment typically beyond the scope of the law

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.