Real-Time Numerical Differentiation of Sampled Data Using Adaptive Input and State Estimation

Real-time numerical differentiation plays a crucial role in many digital control algorithms, such as PID control, which requires numerical differentiation to implement derivative action. This paper addresses the problem of numerical differentiation for real-time implementation with minimal prior information about the signal and noise using adaptive input and state estimation. Adaptive input estimation with adaptive state estimation (AIE/ASE) is based on retrospective cost input estimation, while adaptive state estimation is based on an adaptive Kalman filter in which the input-estimation error covariance and the measurement-noise covariance are updated online. The accuracy of AIE/ASE is compared numerically to several conventional numerical differentiation methods. Finally, AIE/ASE is applied to simulated vehicle position data generated from CarSim.Comment: This paper is under review at the International Journal of Contro

Kinematics-Based Sensor Fault Detection for Autonomous Vehicles Using Real-Time Numerical Differentiation

Sensor fault detection is of extreme importance for ensuring the safe operation of vehicles. This paper introduces a novel approach to detecting and identifying faulty sensors. For ground vehicles confined to the horizontal plane, this technique is based on six kinematics-based error metrics that are computed in real time by using onboard sensor data encompassing compass, radar, rate gyro, and accelerometer measurements as well as their derivatives. Real-time numerical differentiation is performed by applying the adaptive input and state estimation (AIE/ASE) algorithm. Numerical examples are provided to assess the efficacy of the proposed methodology

Robust Sampled-Data Adaptive Control of the Rohrs Counterexamples

Abstract-We revisit the Rohrs counterexamples within the context of sampled-data adaptive control. In particular, retrospective cost adaptive control (RCAC) is applied to the sampled continuous-time plant with unmodeled high-frequency dynamics, which involves nonminimum-phase (NMP) sampling zeros. It is shown that, without knowledge of these NMP zeros, RCAC stabilizes the uncertain plant and asymptotically follows the sinusoidal command

Personality, posttraumatic stress and trauma type: factors contributing to posttraumatic growth and its domains in a Turkish community sample

Background: Posttraumatic growth (PTG) is conceptualized as a positive transformation resulting from coping with and processing traumatic life events. This study examined the contributory roles of personality traits, posttraumatic stress (PTS) severity and their interactions on PTG and its domains, as assessed with the Posttraumatic Growth Inventory Turkish form (PTGI-T). The study also examined the differences in PTG domains between survivors of accidents, natural disasters and unexpected loss of a loved one. Methods: The Basic Personality Traits Inventory, Posttraumatic Diagnostic Scale, and PTGI-T were administered to a large stratified cluster community sample of 969 Turkish adults in their home settings. Results: The results showed that conscientiousness, agreeableness, and openness to experience significantly related to the total PTG and most of the domains. The effects of extraversion, neuroticism and openness to experience were moderated by the PTS severity for some domains. PTG in relating to others and appreciation of life domains was lower for the bereaved group. Conclusion: Further research should examine the mediating role of coping between personality and PTG using a longitudinal design

Aliasing Effects in Direct Digital Adaptive Control of Plants with High-Frequency Dynamics and Disturbances

Abstract-In this paper we consider sampled-data adaptive control in the presence of aliasing, due to either the highfrequency free response of the plant, or the high-frequency content in the disturbances. In particular, we present a numerical investigation of retrospective cost adaptive control (RCAC) applied to sampled-data command-following and disturbancerejection problems, and investigate the performance of RCAC in the presence of aliasing. It is shown that RCAC is able to stabilize the plant despite the high-frequency dynamics, unless the controllability of unstable modes is not lost due to sampling. However, the intersample command-following performance may be nonzero due to aliasing of disturbances

Retrospective Cost Adaptive Control for Systems with Unknown Nonminimum-Phase Zeros

We develop a multi-input, multi-output direct adaptive controller for discrete-time, possibly nonminimum-phase, systems with unknown nonminimum-phase zeros. The adaptive controller requires limited modeling information about the system, specifically, Markov parameters from the control input to the performance variables. Often, only a single Markov parameter is required, even in the nonminimum-phase case. We demonstrate the algorithm on command-following and disturbance-rejection problems, where the command and disturbance spectra are unknown. This controller is based on a retrospective performance objective, where the controller is updated using either batch or recursive least squares

FIR-based phase matching for robust Retrospective-Cost Adaptive Control

Abstract — In this paper we develop frequency-domain meth-ods for approximating IIR plants with FIR transfer functions. The underlying goal is to increase the performance and robust-ness of Retrospective-Cost Adaptive Control (RCAC), which is applicable to MIMO possibly nonminimum-phase (NMP) plants without the need to know the locations of the NMP zeros. The only required modeling information is an FIR approximation of the plant, which may be based on a limited number of Markov parameters, or possibly noisy frequency response data. In this paper we investigate the resulting phase mismatch between the true plant and the FIR approximation obtained through linear and nonlinear approximation methods. We consider degradation in the phase mismatch due to uncertainty in the frequency response data. I