Application of robust open-loop and closed-loop model predictive control to processes with safety constraints

This paper presents a new approach for robust open-loop and closed-loop control of nonlinear processes with parameter uncertainties and a comparison with classical concepts. The approach leads to trajectories that show small variations if uncertain parameters and uncertain initial conditions are present. The algorithm utilizes the Unscented Transformation. It allows a 2nd order approximation of the first two statistical moments of the system's output as a function of the stochastic system's state and uncertain model parameters. Because the numerical burden is low, it can be used for optimization based online closed-loop process control as well.

A new approach for robust model predictive control of biological production processes

Lower-limb exoskeletons are widely researched to improve walking performance and mobility. Low-level sensor-less exoskeleton motor control is attractive for consumer applications due to reduced device complexity and cost, but complex and variable transmission system configurations make the development of effective open-loop motor controllers that are responsive to user input challenging. The objective of this study was to develop and validate an open-loop motor control framework resulting in similar or greater performance vs. closed-loop torque control. We used generalized linear regression to develop two open-loop controllers by modeling motor current during exoskeleton-assisted walking; a "complex" model used desired torque and estimated ankle angular velocity as inputs, while a "simple" model used desired torque alone. Five participants walked at 1.0-1.3 m/s on a treadmill with closed-loop and both open-loop controllers providing ankle exoskeleton assistance. Both open-loop current controllers had similar root-mean-squared torque tracking error (p=0.23) compared to the closed-loop torque-feedback controller. Both open-loop controllers had improved relative average torque production (p<0.001 complex, p=0.022 simple), lower power consumption (p<0.001 for both), and reduced operating noise (p=0.002 complex, p<0.001 simple) over the closed-loop controller. New control models developed for a different ankle exoskeleton configuration showed similar improvements (lower torque error, greater average and peak torque production, lower power consumption) over closed-loop control during over-ground walking. These results demonstrate that our framework can produce open-loop motor controllers that match closed-loop control performance during exoskeleton operation.

A normal vector approach for integrated process and control design with uncertain model parameters and disturbances

Layer-to-layer closed-loop feedback control application for inter-layer temperature stabilization in laser powder bed fusion

We consider a finite-horizon continuous-time optimal control problem with nonlinear dynamics, an integral cost, control constraints and a time-varying parameter which represents perturbations or uncertainty. After time-discretization of the problem we employ a Model Predictive Control (MPC) algorithm with a shrinking horizon, which uses a "prediction"/forecast for the uncertain parameter and (possibly inexact) measurements of the state vector, and generates a piecewise constant control signal by solving auxiliary open-loop control problems. In our main result we derive an estimate of the difference between the MPC-generated control and the optimal feedback control, both obtained for the same value of the perturbation parameter, in terms of the step-size of the discretization and the measurement error. We also give an estimate for a norm of the difference between the MPC-generated control and the optimal open-loop control in the problem with the "true" value of the uncertain parameter, depending on the prediction error.

Groundwater is a crucial resource for water supply and irrigation in many parts of the world, especially in the Middle East. The Eocene aquifer, located in the northern part of the West Bank, Palestine, is threatened by unsustainable groundwater abstractions and on-ground pollution. Analysis and management of this aquifer are challenging because of limited data availability. This research contributes to the long-term sustainability of the aquifer by model-based design of future abstraction strategies considered within an uncertainty analysis framework. The methodology employed started with development of a single-layer steady-state MODFLOW groundwater model of the area, followed by uncertainty analysis of model parameters using Monte Carlo simulations. The same model was afterwards coupled with a Successive Linear Programming (SLP) optimization algorithm, implemented in the Groundwater Management tool (GWM) of the United States Geological Survey (USGS). The purpose of optimization was deriving five optimal abstraction strategies, each aiming to maximize groundwater abstraction, subject to different constraints regarding groundwater depletion. Given the uncertainty of model parameters, the sensitivity and reliability of these optimal strategies were then tested. Sensitivity was checked for two optimal strategies by performing re-optimization with different values of uncertain model parameters (one at a time). Reliability of the five strategies was tested by analyzing the extent of constraints’ violation for each strategy when varying the uncertain parameters using Monte Carlo simulations. Finally, the model was used for determining capture zones of wells for the five optimal abstraction strategies, land-use in these capture zones, and the associated estimates of on-ground nitrogen loading. The developed strategies were then deployed in a web-based decision support application (named Groundwater Decision Support System—GDSS), together with other relevant information. Users can analyze results of different optimal strategies in terms of groundwater level variations and total water balance results, and test consequences of uncertain parameters. Capture zones of wells for different abstraction strategies, together with land-use and on-ground nitrogen loading in these capture zones, are also presented. Results show that critical uncertain parameters are recharge, hydraulic conductivity, and conductance at key boundary condition locations. Optimal abstraction strategies results indicate that an increase in total abstractions could be between 5% and 20% from the current level (estimated at about 56 × 106 m3/year, which is about 74% of estimated annual recharge). The uncertain parameters, however, are impacting the sensitivity and the reliability of the optimal strategies to variable degrees. Recharge and hydraulic conductivity are the most critical uncertain parameters regarding sensitivity of the optimal strategies, while reliability is also impacted by the level of abstraction proposed in a given strategy (number, locations, and abstraction rates of new wells). The main novelty and contribution of this research is in combining modelling, uncertainty analysis, and optimization techniques in a comprehensive decision support system for the area of the Eocene aquifer, characterized with limited data availability.

Step motors are widely used in motion control systems. The step motor controller must perform high-precision positioning and smooth movement operation. In motion systems applying velocity profile control, the velocity of the motor gradually increases or decreases, to avoid the negative effect caused by the sudden change in velocity. Previous research proposes several step motor controller designs, that contain several modules, including a velocity profile generator, step motor driver, and feedback counter. These controllers are able to perform precise positioning and provide smooth motion operation. However, because these controllers operate in an open-loop control so the synchronous condition can be lost when disturbances in load torque exist. This paper presents the application of closed-loop control in the step motor controller. The control algorithm is based on two mode operations: open-loop and closed-loop. The controller operates in open-loop mode when it remains synchronized, and switches to closed-loop mode when it loses synchronization. This paper presents an algorithm for generating various velocity profiles and closed-loop control algorithms. The experiment is performed on a 16-b DSP, to verify the performance of the design. The experimental results show a velocity profile of unsymmetrical shape and demonstrate effective closed-loop control in an actual step motor system

The trajectory tracking method for an unmanned surface vehicle (USV) using integral terminal sliding mode integral backstepping adaptive control (ITSMIBAC) scheme is presented in this paper, which takes into account external disturbances and uncertain model parameters. Furthermore, finite-time convergence and excellent track tracking effect are guaranteed by the differential equations that is made up of the integral terminal sliding mode function. In particular, a backstepping method adding integral term is used to ensure the globally asymptotic stability of system and make the differential equations to converge to zero. The upper bound of the disturbances including external disturbances and uncertain model parameters is estimated by an adaptive control law. Finally, the effectiveness and the feasibility of the proposed controller are demonstrated by numerical example and comparison result through tracking circular orbit.

In this paper, a centralized model predictive control (MPC) is applied on a group of interconnected microgrids (MGs) with the main grid. The objective is to maximize the benefits for all the elements constituting the MGs in addition to the benefits of the main grid. The application of MPC in our study needs a forecasting information about energy prices, production power, and loads. The algorithm is tested on five interconnected MGs connected to the main grid. Results have shown the performance of the proposed algorithm, especially for the benefits of MG owners, the coordination between MGs while respecting of the constraints related to each one of them.

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.

Model Predictive Control In Solar Trough Plants: A Review

Active Unsteady Flow Control experiments were performed on a Natural Laminar Flow, NLF 0414 airfoil at Rec = 1.0 × 10 6 . The goal of this study was to control boundary-layer separation across the trailing-edge region of the airfoil in off-design conditions. Both openloop and closed-loop control approaches were used. Active control of separation was achieved using a series of blowing slots at the x/c = 0.75 location. Open loop control parameters were varied across a parametric range of jet amplitudes, actuation frequencies and duty cycles and performance measurements were acquired to identify the effectiveness of these actuation schemes. PIV measurements were acquired across a horizontal plane near the trailing edge region of the model to understand the effects of actuation on flow separation and the vortex dynamics associated with unsteady actuation. A closed loop controller was developed to vary the actuation parameters in-situ using sensory feedback from the unsteady surface pressure measurements and adaptive modal decomposition methods. This closed-loop system was able to automatically control the extent of separation such that a desired value of Cl was obtained.

Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter‐comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state‐of‐the‐art climate models, due to computational constraints facing the high dimensionality and non‐linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non‐linearities of the parameter perturbations are well captured, such that only a limited number of 20–50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert‐tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional climate model. Objective calibration of parameterizations with regional models could also serve as a strategy toward improving parameterization packages of global climate models.

In this paper, the influence of uncertain model parameters on vehicle lateral dynamics steady states is studied. These steady states are widely used in driver assistant systems and enable highly automated driving functions. A methology for propagating random variables through the single-track vehicle model is presented. The random variables are described using continuous probability density functions. It is shown, that the influence of parameter uncertanties scales differently to different steady states. The effect on the path curvature and the lateral acceleration is highlighted. An example for a fault detection scheme is given and analysed in context of the given uncertainty. The faults that can be detected without violating a given true-positive rate and a given false-positive rate are calculated. The method can be used to quickly identify possible problems arising from uncertain parameters.