On reliable control system designs

A mathematical model for use in the design of reliable multivariable control systems is discussed with special emphasis on actuator failures and necessary actuator redundancy levels. The model consists of a linear time invariant discrete time dynamical system. Configuration changes in the system dynamics are governed by a Markov chain that includes transition probabilities from one configuration state to another. The performance index is a standard quadratic cost functional, over an infinite time interval. The actual system configuration can be deduced with a one step delay. The calculation of the optimal control law requires the solution of a set of highly coupled Riccati-like matrix difference equations. Results can be used for off-line studies relating the open loop dynamics, required performance, actuator mean time to failure, and functional or identical actuator redundancy, with and without feedback gain reconfiguration strategies

On reliable control system designs

Bibliography: p. [37].Supported by the Fannie and John Hertz Foundation. NASA Ames grant NGL-22-009-124 AFOSR grant 77-3281J. Douglas Birdwell, D.A. Castanon, M. Athans

On reliable control system designs

"December, 1985." Caption title.Bibliography: p. [35].NASA Ames grant NGL-22-009-124 AFOSR grant 77-3281J. Douglas Birdwell, D.A. Castanon, M. Athans

On reliable control system designs with and without feedback reconfigurations

Bibliography: leaf 8.Caption title. Supported in part by the Fannie and John Hertz Foundation.NASA Ames Grant NGL-22-099-124 AFOSR Grant 77-3281J.D. Birdwell, D.A. Castanon and M. Athans

Algorithms for computing the multivariable stability margin

Stability margin for multiloop flight control systems has become a critical issue, especially in highly maneuverable aircraft designs where there are inherent strong cross-couplings between the various feedback control loops. To cope with this issue, we have developed computer algorithms based on non-differentiable optimization theory. These algorithms have been developed for computing the Multivariable Stability Margin (MSM). The MSM of a dynamical system is the size of the smallest structured perturbation in component dynamics that will destabilize the system. These algorithms have been coded and appear to be reliable. As illustrated by examples, they provide the basis for evaluating the robustness and performance of flight control systems

A field programmable gate array based modular motion control platform

The expectations from motion control systems have been rising day by day. As the systems become more complex, conventional motion control systems can not achieve to meet all the specifications with optimized results. This creates the necessity of fundamental changes in the infrastructure of the system. Field programmable gate array (FPGA) technology enables the reconfiguration of the digital hardware, thus dissolving the necessity of infrastructural changes for minor manipulations in the hardware even if the system is deployed. An FPGA based hardware system shrinks the size of the hardware hence the cost. FPGAs also provide better power ratings for the systems as well as a more reliable system with improved performance. As a trade off, the development is rather more difficult than software based systems, which also affects the research and development time of the overall system. In this paper a level of abstraction is introduced in order to diminish the requirement of advanced hardware description language (HDL) knowledge for implementing motion control systems thoroughly on an FPGA. The intellectual property library consists of synthesizable hardware modules specifically implemented for motion control purposes. Other parts of a motion control system, like user interface and trajectory generation, are implemented as software functions in order to protect the modularity of the system. There are also several external hardware designs for interfacing and driving various types of actuators

Learning Critical Scenarios in Feedback Control Systems for Automated Driving

Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough for its acceptance on the market. Recently, much research has focused on scenario-based methods. However, the number of possible driving scenarios to test is in principle infinite. In this paper, we formalize a learning-based optimization framework to generate corner test-cases, where we take into account the operational design domain. We examine the approach on the case of a feedback control system for automated driving, for which we suggest the design of the objective function expressing the criticality of scenarios. Numerical tests on two logical scenarios of the case study demonstrate that the approach can identify critical scenarios within a limited number of closed-loop experiments