nonlinearmodelpredictivecontrol (NMPC) is an advanced control technique which finds increasing interest in industry. One obstacle to its more widespread use is the computational effort due to the resulting nonlinea...
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ISBN:
(纸本)9781509025916
nonlinearmodelpredictivecontrol (NMPC) is an advanced control technique which finds increasing interest in industry. One obstacle to its more widespread use is the computational effort due to the resulting nonlinear dynamic programming problems. Efficient online optimization methods are required to overcome this problem, in particular for processes that require fast sampling times. A promising approach to online optimization is the Multi-Level Iteration (MLI) scheme that was proposed in [1]. The MLI scheme is based on Sequential Quadratic Programming (SQP) and consists of four solution modes, which differ in the performance and computation speed due to the amount of information that is used when solving the quadratic programming (QP) subproblems. It has been successfully tested on theoretical case studies. In this work, the MLI scheme is for the first time investigated experimentally for solving the optimal state feedback control of a nonlinear fed-batch process where the thermal system is real hardware and the chemistry is considered by inserting the resulting heat of reaction via a heating device. The performance of the MLI scheme is studied for different combinations of modes and different frequencies of using each of them, and the resulting control performance is compared.
nonlinear model-predictive control (NMPC) and dynamic real-time optimization (DRTO) lead to a substantial improvement of the operation of complex nonlinear processes. Whereas the focus in NMPC is primarily on control ...
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nonlinear model-predictive control (NMPC) and dynamic real-time optimization (DRTO) lead to a substantial improvement of the operation of complex nonlinear processes. Whereas the focus in NMPC is primarily on control performance by minimizing the deviation from a given set-point, the objective in DRTO is to achieve a profitable and flexible operation adapted to changing market conditions and process uncertainties by employing an economic optimization criterion. A method for solving dynamic optimization problems based on neigh boring-extremal updates suitable for applications in NMPC and DRTO is presented in this paper. If process operation is affected by small perturbations, efficient techniques for updating the nominal trajectories based on parametric sensitivities can be applied [J. Kadam, W. Marquardt, Sensitivity-based solution updates in closed-loop dynamic optimization, in: Proceedings of the DYC-OPS 7 Conference, Cambridge, USA, 20041;these updates do not require the solution of the rigorous optimization problem but rely on first and second-order sensitivities computed by composite adjoints. However for larger perturbations and strong nonlinearities, the fast updates obtained by the neighboring-extremal solutions are not sufficiently accurate, and the solution of the nonlinear optimization problem requires further iterations with updated sensitivities to give a feasible and optimal solution. The application of the method to a simulated semi-batch reactor demonstrates its capabilities. The presented method is discussed in comparison to other methods in the literature. (C) 2009 Elsevier Ltd. All rights reserved.
Abstract This paper investigates the formulation of nonlinear model-predictive control problems with economic objectives on an infinite horizon. The proposed formulation guarantees nominal stability for closed-loop op...
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Abstract This paper investigates the formulation of nonlinear model-predictive control problems with economic objectives on an infinite horizon. The proposed formulation guarantees nominal stability for closed-loop operation. Furthermore, a novel solution method of the infinite horizon method through a transformation of the independent time variable is employed. The closed-loop optimization with infinite horizon is compared to a finite-horizon formulation. A small case study is presented for illustration purposes.
In this note, a solution method is presented for nonlinear model-predictive control of open-loop stable systems on an infinite horizon. The proposed method first reformulates the infinite-horizon continuous-time probl...
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In this note, a solution method is presented for nonlinear model-predictive control of open-loop stable systems on an infinite horizon. The proposed method first reformulates the infinite-horizon continuous-time problem by a time-coordinate transformation as a finite horizon problem and computes the solution after discretization of the control variables. This method aims to ensure stability without imposing a terminal constraint set. The adaptive discretization algorithm allows an efficient and accurate solution of the infinite-horizon problem with a moderate number of discrete decision variables. The time transformation function is adapted such that the important dynamics of the system can be captured and the control variables can be discretized appropriately. An illustrative case study is presented.
model-based online applications such as soft-sensing, fault detection or modelpredictivecontrol require representative online models. Basing models on physics has the advantage of naturally describing nonlinear proc...
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model-based online applications such as soft-sensing, fault detection or modelpredictivecontrol require representative online models. Basing models on physics has the advantage of naturally describing nonlinear processes and potentially describing a wide range of operating conditions. Implementing adaptivity is essential for online use to avoid model performance degradation over time and to compensate for model imperfection. Requirements for identifiability and observability, numerical robustness and computational speed place an upper limit on model complexity. These considerations motivate the design of balanced-complexity physical models with adaptivity for online use. Techniques used in the design of balanced-complexity models are given with examples from offshore oil and gas production. Despite potential benefits, the effort required to implement balanced-complexity models, particularly at large scales, may deter their use. This paper presents a modelica-based approach to reduce implementation effort by interfacing exported modelica models with application code by means of a generic interface. The suggested approach is demonstrated by parameter estimation for a subsea well-manifold-pipeline system.
A two-layer architecture for dynamic real-time optimization (or nonlinearmodelpredictivecontrol (NMPC) with an economic objective) is presented, where the solution of the dynamic optimization problem is computed on ...
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A two-layer architecture for dynamic real-time optimization (or nonlinearmodelpredictivecontrol (NMPC) with an economic objective) is presented, where the solution of the dynamic optimization problem is computed on two time-scales. On the upper layer, a rigorous optimization problem is solved with an economic objective function at a slow time-scale, which captures slow trends in process uncertainties. On the lower layer, a fast neighboring-extremal controller is tracking the trajectory in order to deal with fast disturbances acting on the process. Compared to a single-layer architecture, the two-layer architecture is able to address control systems with complex models leading to high computational load, since the rigorous optimization problem can be solved at a slower rate than the process sampling time. Furthermore, solving a new rigorous optimization problem is not necessary at each sampling time if the process has rather slow dynamics compared to the disturbance dynamics. The two-layer control strategy is illustrated with a simulated case study of an industrial polymerization process. (C) 2010 Elsevier Ltd. All rights reserved.
A combination of multiple neural networks (NNs) is selected and used to modelnonlinear multi-input multi-output (MIMO) processes with time delays. An optimisation procedure for a nonlinear model-predictive control (M...
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A combination of multiple neural networks (NNs) is selected and used to modelnonlinear multi-input multi-output (MIMO) processes with time delays. An optimisation procedure for a nonlinear model-predictive control (MPC) algorithm based on this model is then developed. The proposed scheme has been applied and evaluated for two example problems, including the MPC of a multi-component distillation column.
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