This paper presents a constraint aggregation approach for nonlinear model predictive control (NMPC). Constraint aggregation functions provide an approximation of the feasible region with a reduced number of nonlinear ...
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This paper presents a constraint aggregation approach for nonlinear model predictive control (NMPC). Constraint aggregation functions provide an approximation of the feasible region with a reduced number of nonlinear constraints. The effect of the aggregation on the closed-loop system performance and stability is studied using tools from sensitivity analysis. Numerical results for the control of a 20th order flexible aircraft model show that significant computational savings can be achieved. The proposed method can facilitate the implementation of NMPC solutions for large-scale systems.(c) 2022 Elsevier Ltd. All rights reserved.
The oscillating water column (OWC) wave energy converter (WEC) together with a self-rectifying air turbine and generator which convert alternating airflow induced by the water motion into kinetic energy and then into ...
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The oscillating water column (OWC) wave energy converter (WEC) together with a self-rectifying air turbine and generator which convert alternating airflow induced by the water motion into kinetic energy and then into electric energy is a promising device for the advancement of marine renewable energy. As researchers overcome the modeling challenges of OWCs, such as the nonlinearities due to air compressibility and power take-off (PTO) dynamics, we can integrate control algorithms to improve the efficiency of the OWC. Herein, we present a nonlinear model predictive controller (NMPC) that maximizes the generated electro-mechanical power while maintaining the efficiency of a self-rectifying turbine attached to an OWC WEC. To achieve this goal, a good trade-off between maximizing generated power and turbine efficiency is found by adjusting the importance of both objectives in the computation of the control signal. Furthermore, we apply the proposed NMPC to an array of three WECs that takes into account the hydrodynamic interactions between the devices in the computation of optimal generator torque control signal.
In the pursuit of integrated scheduling and control frameworks for chemical processes, it is important to develop accurate integrated models and computational strategies such that optimal decisions can be made in a dy...
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In the pursuit of integrated scheduling and control frameworks for chemical processes, it is important to develop accurate integrated models and computational strategies such that optimal decisions can be made in a dynamic environment. In this study, a recently developed switched system formulation that integrates scheduling and control decisions is extended to closed-loop operation embedded with nonlinear model predictive control (NMPC). The resulting framework is a nested online scheduling and control loop that allows to obtain fast and accurate solutions as no model reduction is needed and no integer variables are involved in the formulations. In the outer loop, the integrated model is solved to calculate an optimal product switching sequence such that the process economics is optimized, whereas in the inner loop, an NMPC implements the scheduling decisions. The proposed scheme was tested on two multi-product continuous systems. Unexpected large disturbances and rush orders were handled effectively.
The motion cueing algorithm (MCA) is the main algorithm in motion simulators in charge of generating vehicle motions within the platform's constraints. The classical washout filter is one of the popular types of M...
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The motion cueing algorithm (MCA) is the main algorithm in motion simulators in charge of generating vehicle motions within the platform's constraints. The classical washout filter is one of the popular types of MCA, which is used in air and land vehicle motion simulators. The fixed home position of the simulator platform is always cogitated in the MCA to washout the motion simulator after generating each motion. Unfortunately, considering the fixed home position reduces the efficient consumption of the workspace in the linear directions. The linear motion of the motion simulator is due to the production of the high-pass frequency part of the motion scenarios. Prepositioning is used to tackle this assumption by varying the home position rather than the fixed position. The linear motion limitations of the motion simulator can virtually be enlarged using the prepositioning method. The efficient regeneration of the high-pass motion cues using a new propositioning technique is the main goal of this study to increase the motion realism of the simulator and remove any false motion cues due to the platform limitations. The proposed model utilised the recurrent neural network (RNN) to estimate the motion scenario along the prediction horizon. The nonlinear model predictive control (MPC) uses the estimated motion signals to extract the best optimal off-centre position of the motion simulator platform. The newly developed prepositioning technique is developed in the simulation environment of MATLAB to validate the proposed technique in terms of efficiency and applicability. The outcomes prove the capability of the proposed technique against the recently developed prepositioning technique using fuzzy logic and RNN.
nonlinear model predictive control (NMPC) has been used to control a variety of bioprocesses. As NMPC requires full state information, a nonlinear moving horizon estimator (NMHE) is used to reconstruct the states from...
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nonlinear model predictive control (NMPC) has been used to control a variety of bioprocesses. As NMPC requires full state information, a nonlinear moving horizon estimator (NMHE) is used to reconstruct the states from the available measurements. In context of bioprocesses, both NMPC and NMHE typically use macroscopic mass balance type models. These models ignore all intracellular information that is available via, e.g., Genome-Scale Metabolic networks. However, the use of these models adds significantly to the computational complexity of the controller. In this paper, we aim to assess whether incorporating metabolic networks in an NMPC framework leads to improvement in the tracking performance of a continuous bioreactor. To avoid complex bilevel optimisation problems, our NMPC scheme utilises the dynamic Metabolic Flux Analysis framework. In this framework, the intracellular fluxes are estimated from the measurements of extracellular metabolites. This is achieved by an NMHE scheme. The tracking performance of this metabolic network-based NMPC-NMHE is compared to a simple macroscopic model based NMPC in which all the states, i.e., the extracellular metabolites are assumed to be measured. In our case study, the tracking performance of the metabolic network-based NMPC is marginally better than the performance of the macroscopic model based NMPC. The added advantage of metabolic network based NMPC comes from the insight they offer from the estimated fluxes. Moreover, the performance and the insights obtained depend on the complexity of the metabolic network used.
Active Debris removal using robotic manipulators onboard satellites is a promising way of cleaning up the space junk. However, complexities and non-linearities associated with the control of such coupled space-based s...
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Active Debris removal using robotic manipulators onboard satellites is a promising way of cleaning up the space junk. However, complexities and non-linearities associated with the control of such coupled space-based systems present difficulties in their feasible implementation. Lack of a fixed base arises serious problems in controlling the space manipulators for precision tasks like capture of an orbiting space debris. This paper presents systematic modelling and control approaches for Rotation floating space robots in order to draw a comparison between them while tracking a moving target representing autonomous debris capture. We propose a nonlinear model predictive controller (NMPC) for the space robot in order to design an optimal path that the end-effector can follow while being controlled to capture the target. To the best of the knowledge of the authors, such a controller has not been tested for a Rotation floating space robot before. Further, the current work implements and reviews one of the most commonly used Transpose Jacobian Cartesian (TJC) controller for Rotation floating space robots through the use of Generalized Jacobian Matrix (GJM). The results provide sufficient evidence of the superior performance of the nonlinear model predictive controller over the TJC controller. Finally, the current work also implements the same nonlinear model predictive controller on a more popular state of the art Free floating space robot and compares it with the Rotation floating space robot.
In this paper, the derivative-free optimization algorithm MADS (mesh adaptive direct search) is adapted for implementation on field programmable gate array (FPGA) with fixed-point data representation. MADS is then exp...
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ISBN:
(纸本)9781665451093
In this paper, the derivative-free optimization algorithm MADS (mesh adaptive direct search) is adapted for implementation on field programmable gate array (FPGA) with fixed-point data representation. MADS is then exploited to solve constrained nonlinear optimization problems arising from nonlinear model predictive control. The application on two examples taken from the literature shows the advantages of the proposed circuit architecture over the existing work, in terms of latency and resource occupation.
Motorsport has historically driven automobile innovation by challenging the world's best car manufacturers to design, and develop vehicles that push limits of contemporary technology, and compete at physical vehic...
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Motorsport has historically driven automobile innovation by challenging the world's best car manufacturers to design, and develop vehicles that push limits of contemporary technology, and compete at physical vehicle limits. Autonomous driving is a rapidly evolving field that garners interest in industry, government, and research due to substantial improvements in road safety, and traffic flow. Autonomous racing is a byproduct of advancements in autonomous driving, and guarantees innovation in the field through the design of state-of-the-art perception, motion planning, and control algorithms developed to perform in fast-paced, multi-object environments at high speeds, operating at a vehicle's acceleration, and tire limits. We propose a high-level nonlinear model predictive control (NMPC) strategy incorporating a Pacejka tire model, and nonlinear vehicle dynamics in the global coordinate system with constraints based on track boundaries, and vehicle input limits for optimal motion planning to minimize lap time. The NMPC motion planner is evaluated in three real-world racetracks, Circuit of the Americas, Circuit de Spa-Francorchamps, and Autodromo Nazionale Monza for three race car classes, Formula 1 (F1), Le Mans Prototype (LMP1), and Grand Touring Endurance (GTE). The proposed NMPC strategy is shown to generate time-optimal trajectories for each vehicle class in the evaluated tracks, conforming to optimal racing lines demonstrated by professional racing drivers.
A large amount of energy requirement for solvent regeneration is a major barrier to the widespread adoption of amine-based post-combustion CO 2 capture (PCC). Flexible operation is one of the ways to lower the energy ...
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A large amount of energy requirement for solvent regeneration is a major barrier to the widespread adoption of amine-based post-combustion CO 2 capture (PCC). Flexible operation is one of the ways to lower the energy penalty by responding to changes in economic factors like the energy price. However, for effective implementation of flexible operation strategies, it is necessary to identify the most economic operating condition under various potential scenarios and to establish an appropriate control strategy to operate the process. As flexible operation will inherently involve a large operating envelope, we investigate the use of nonlinear model predictive control (NMPC) technology. To circumvent the problem of solving a large-scale nonlinear programming problem online, a simpler NARX model is identified and used. With the NARX model, an offset-free NMPC is designed and simulated under various dynamic scenarios. The developed NARX-based NMPC shows satisfactory control performance, stabilizing the CO 2 capture rate faster than LMPC by 60-100 min.
The purpose of the greenhouse system is to build a suitable microclimate environment for crop growth in northeastern China. The precise control of environmental factors is the key factor to ensure the high quality of ...
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ISBN:
(纸本)9798350334722
The purpose of the greenhouse system is to build a suitable microclimate environment for crop growth in northeastern China. The precise control of environmental factors is the key factor to ensure the high quality of crop growth. However, the actual greenhouse system is described as a complex dynamic characteristic with strong nonlinear, strong coupling, multiple disturbances. Therefore, the traditional PID control method is difficult to solve the above problems. In order to solve the above problems, this paper develops a microclimate dynamic model suitable for the northeastern greenhouse. Subsequently, the nonlinear model predictive control (NMPC) algorithm combines with GA algorithm is applied to the northeastern greenhouse. This scheme is applied to the control of temperature, humidity and carbon dioxide concentration in the greenhouse compared with the traditional PID control. The simulation results show that the nonlinear model predictive control based on GA algorithm has better control performance and stability. Furthermore, the proposed method can meet the actual demand.
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