Journal of Dynamic Systems, Measurement, and Control
ISSN / EISSN : 0022-0434 / 1528-9028
Published by: ASME International (10.1115)
Total articles ≅ 5,439
Latest articles in this journal
Published: 10 September 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052396
This paper focuses on the empirical derivation of regret bounds for mobile systems that can optimize their locations in real time within a spatiotemporally varying renewable energy resource. The case studies in this paper focus specifically on an airborne wind energy system, where the replacement of towers with tethers and a lifting body allows the system to adjust its altitude continuously, with the goal of operating at the altitude that maximizes net power production. While prior publications have proposed control strategies for this problem, often with favorable results based on simulations that use real wind data, they lack any theoretical or statistical performance guarantees. In the present work, we make use of a very large synthetic data set, identified through parameters from real wind data, to derive probabilistic bounds on the difference between optimal and actual performance, termed regret. The results are presented for a variety of control strategies, including maximum probability of improvement, upper confidence bound, greedy, and constant altitude approaches. In addition, we use dimensional analysis to generalize the aforementioned results to other spatiotemporally varying environments, making the results applicable to a wider variety of renewably powered mobile systems. Finally, to deal with more general environmental mean models, we introduce a novel approach to modify calculable regret bounds to accommodate any mean model through what we term an "effective spatial domain."
Published: 10 September 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052394
This paper presents an averaged state model and the design of nonlinear observers for an on/off pneumatic actuator. The actuator is composed of two chambers and four on/off solenoid valves. The elaborated averaged state model has the advantage of using only one continuous input instead of four binary inputs. Based on this new model, a high gain observer and a sliding mode observer are designed using the piston position and the pressure measurements in one of the chambers. Finally, their closed-loop performances are verified and compared on an experimental benchmark.
Published: 10 September 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052395
In this paper, we propose a global self-optimizing control (SOC) approach, where nonlinear dynamic model is obtained from historical data of plant operation via the framework of sparse identification for nonlinear dynamics (SINDy) combined with regularized regression. With the nonlinear static input-output map obtained by forcing steady-state operation, the globally optimal solutions of controlled variables can be found by tracking the necessary conditions of optimality (NCO) in an analytical fashion. After validation with a numerical example, the proposed method is evaluated using a Modelica-based dynamic model of a chilled water plant. The economic objective for chiller plant operation is to minimize the total power of compressor, condenser water pump and cooling tower fan, while the cooling tower fan speed and condenser water mass flow rate are used as manipulated inputs. The operating data are generated based on realistic ambient and load conditions and a best-practice rule-based strategy for chiller operation. The control structure with the SOC method yields a total power consumption close to the global optimum and substantially smaller than that of a best-practice rule-based chiller plant control strategy. The proposed method promises a global SOC solution using dynamic operation data, for cost-effective and adaptive control structure optimization.
Published: 2 September 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052312
This paper concerns the design and rigorous in silico evaluation of a closed-loop hemorrhage resuscitation algorithm with blood pressure (BP) as controlled variable. A lumped-parameter control design model relating volume resuscitation input to blood volume (BV) and BP responses was developed and experimentally validated. Then, three alternative adaptive control algorithms were developed using the control design model: (i) model reference adaptive control with BP feedback, (ii) composite adaptive control with BP feedback, and (iii) composite adaptive control with BV and BP feedback. To the best of our knowledge, this is the first work to demonstrate model-based control design for hemorrhage resuscitation with readily available BP as feedback. The efficacy of these closed-loop control algorithms was comparatively evaluated as well as compared with an empiric expert knowledge-based algorithm based on 100 realistic virtual patients created using a well-established physiological model of cardiovascular hemodynamics. The in silico evaluation results suggested that the adaptive control algorithms outperformed the knowledge-based algorithm in terms of both accuracy and robustness in BP set point tracking: the average median performance error and median absolute performance error were significantly smaller by >99% and >91%, and as well, their inter-individual variability was significantly smaller by >88% and >94%. Pending in vivo evaluation, model-based control design may advance the medical autonomy in closed-loop hemorrhage resuscitation.
Published: 31 August 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052272
This paper investigates temporal correlations in human driving behavior using real-world driving for improving the accuracy of speed forecasting. These correlations can point to a measurement weighting function with two parameters: a forgetting factor for past speed measurements that the vehicle itself drove with, and a discounting factor for the speeds of vehicles ahead based on information from vehicle-to-vehicle (V2V) communication. The developed weighting approach is applied to a vehicle speed predictor using polynomial regression, a prediction method well-known in the literature. The performance of the developed approach is then assessed in both real-world and simulated traffic scenarios for accuracy and robustness. The new weighting method is applied to an ecological adaptive cruise control (eco-ACC), and its influence is analyzed on the prediction accuracy and the performance of the eco-ACC in an electric vehicle model. The results show that the new prediction method improves energy saving from the eco-driving by up to 4 % compared to a baseline least-square-based polynomial regression. This is a 10 % improvement over the constant speed/acceleration model, a conventional speed predictor.
Published: 31 August 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052273
This paper investigates the lateral dynamics stabilization problem for autonomous electric vehicles (AEVs) through the active front steering (AFS) system. A fault-estimation-observer-based robust fuzzy fault tolerant controller is proposed to tackle actuator faults, time delay, modeling nonlinearities and external disturbances. Firstly, to establish a more accurate dynamics model, the Takagi-Sugeno fuzzy modeling strategy is utilized to handle velocity change and parameter uncertainties. Secondly, to further improve the lateral stability and driving active safety of the AEV, an integrated actuator fault model comprising efficiency loss fault and additional bias fault is proposed. Meanwhile, in order to diagnose actuator additional bias fault, a fuzzy fault estimation observer (FFEO) is designed to acquire fault information online. Thirdly, to eliminate the influence caused by integrated fault and actuator time delay, a fuzzy fault tolerant controller (FFTC) is constructed to improve the handling performance and driving active safety of the AEV. Finally, the effectiveness of the proposed control scheme is demonstrated via a full-car model based on the joint simulation of Carsim and MATLAB/Simulink.
Published: 25 August 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052245
Laser powder bed fusion (L-PBF) additive manufacturing (AM) is one type of metal-based AM process that is capable of producing high-value complex components with a fine geometric resolution. As melt-pool characteristics such as melt-pool size and dimensions are highly correlated with porosity and defects in the fabricated parts, it is crucial to predict how process parameters would affect the melt-pool size and dimensions during the build process to ensure the build quality. This paper presents a two-level machine learning (ML) model to predict the melt-pool size during the scanning of a multi-track build. To account for the effect of thermal history on melt-pool size, a so-called (pre-scan) initial temperature is predicted at the lower-level of the modeling architecture, and then used as a physics-informed input feature at the upper-level for the prediction of melt-pool size. Simulated data sets generated from the Autodesk's Netfabb Simulation are used for model training and validation. Through numerical simulations, the proposed two-level ML model has demonstrated a high prediction performance and its prediction accuracy improves significantly compared to a naive one-level ML without using the initial temperature as an input feature.
Published: 23 August 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052230
This research proposes an Iterative Dynamic Programming (IDP) algorithm that generates an optimal supervisory control policy for Hybrid Electric Vehicles (HEVs) considering transient powertrain dynamics. The proposed algorithm tries to solve the 'curse of dimensionality and the 'curse of modeling' of conventional Dynamic Programming (DP). The proposed IDP algorithm iteratively updates the DP formulation using a machine learning (ML) based powertrain model. The ML model is recursively trained using the outputs from the driving cycle simulation with a high-fidelity model. Once the reduced model converges to the high-fidelity model accuracy, the resulting control policy yields a 9.1% Fuel Economy (FE) improvement compared to the baseline non-predictive rule-based control for the UDDS driving cycle. A conventional DP control strategy based on a quasi-static powertrain model and a perfect preview of future power demand yields 14.2% FE improvement. However, the FE improvement reduces to 5.7% when the policy is validated with the high-fidelity model. It is concluded that capturing the transient powertrain dynamics is critical to generating a realistic fuel economy prediction and relevant powertrain control policy. The proposed IDP strategy employs targeted state-space exploration to leverage the improving state trajectory from previous iterations. Compared to conventional fixed state-space sampling methods, this method improves the accuracy of the DP policy against discretization error. It also significantly reduces the computational load of the relatively high number of states of the transient powertrain model.
Published: 23 August 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052229
Nanopositioning stages are widely used in high-precision positioning applications. However, they suffer from an intrinsic hysteretic behavior, which deteriorates their tracking performance. This study proposes an adaptive conditional servocompensator (ACS) to compensate the effect of the hysteresis when tracking periodic references. The nanopositioning system is modeled as a linear system cascaded with hysteresis at the input side. The hysteresis is modeled with a Modified Prandtl-Ishlinskii (MPI) operator. With an approximate inverse MPI operator placed before the system hysteresis operator, the resulting system takes a semi-affine form. The design of the adaptive conditional servocompensator consists of two stages: firstly, we design a continuously-implemented sliding mode control (SMC) law. The hysteresis inversion error is treated as a matched disturbance and an analytical bound on the inversion error is used to minimize the conservativeness of the SMC design. The second part of the controller is the adaptive conditional servocompensator. Under mild assumptions, we establish the well-posedness and periodic stability of the closed-loop system. In particular, the solution of the closed-loop error system will converge exponentially to a unique periodic solution in the neighborhood of zero. The efficacy of the proposed controller is verified experimentally on a commercial nanopositioning device under different types of periodic reference inputs, via comparison with multiple inversion-based and inversion-free approaches.
Published: 18 August 2021
Journal of Dynamic Systems, Measurement, and Control; https://doi.org/10.1115/1.4052173
A warm start method is developed for efficiently solving complex chance constrained optimal control problems. The warm start method addresses the computational challenges of solving chance constrained optimal control problems using biased kernel density estimators and Legendre-Gauss-Radau collocation with an $hp$ adaptive mesh refinement method. To address the computational challenges, the warm start method improves both the starting point for the chance constrained optimal control problem, as well as the efficiency of cycling through mesh refinement iterations. The improvement is accomplished by tuning a parameter of the kernel density estimator, as well as implementing a kernel switch as part of the solution process. Additionally, the number of samples for the biased kernel density estimator is set to incrementally increase through a series of mesh refinement iterations. Thus, the warm start method is a combination of tuning a parameter, a kernel switch, and an incremental increase in sample size. This warm start method is successfully applied to solve two challenging chance constrained optimal control problems in a computationally efficient manner using biased kernel density estimators and Legendre-Gauss-Radau collocation.