This paper discusses the implementation of nonlinear model predictive control on continuous industrial polymer manufacturing processes. Two examples of such processes serve to highlight many of the practical issues fa...
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ISBN:
(纸本)9783540726982
This paper discusses the implementation of nonlinear model predictive control on continuous industrial polymer manufacturing processes. Two examples of such processes serve to highlight many of the practical issues faced and the technological solutions that have been adopted. An outline is given of the various phases of deploying such a solution, and this serves as a framework for describing the relevant modeling choices, controller structures, controller tuning, and other practical issues.
We will in this paper highlight our experience with NMPC. In our context NMPC shall mean the use of a nonlinear mechanistic model, state estimation, and the solution of an online constrained nonlinear optimisation pro...
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ISBN:
(纸本)9783540726982
We will in this paper highlight our experience with NMPC. In our context NMPC shall mean the use of a nonlinear mechanistic model, state estimation, and the solution of an online constrained nonlinear optimisation problem. Our reference base is a number of applications of NMPC in a variety of processes. We discuss the use of mechanistic models in NMPC applications and in particular the merits and drawbacks of applying such models in online applications. Further, we focus on state estimation, and the use of Kalman filters and moving horizon estimation. Finally, we consider the design of the optimization problem itself and implementation issues.
The topic of this paper is a new model predictive control (MPC) approach for the sampled-data implementation of continuous-time stabilizing feedback laws. The given continuous-time feedback controller is used to gener...
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ISBN:
(纸本)9783540726982
The topic of this paper is a new model predictive control (MPC) approach for the sampled-data implementation of continuous-time stabilizing feedback laws. The given continuous-time feedback controller is used to generate a reference trajectory which we track numerically using a sampled-data controller via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution and the trajectory under control. We summarize the necessary theoretical results, discuss several aspects of the numerical implemenation and illustrate the algorithm by an example.
The robustness of asymptotic stability with respect to measurement noise for discrete-time feedback control systems is discussed. It is observed that, when attempting to achieve obstacle avoidance or regulation to a d...
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ISBN:
(纸本)9783540726982
The robustness of asymptotic stability with respect to measurement noise for discrete-time feedback control systems is discussed. It is observed that, when attempting to achieve obstacle avoidance or regulation to a disconnected set of points for a continuous-time system using sample and hold state feedback, the noise robustness margin necessarily vanishes with the sampling period. With this in mind, we propose two modifications to standard model predictive control (MPC) to enhance robustness to measurement noise. The modifications involve the addition of dynamical states that make large jumps. Thus, they have a hybrid flavor. The proposed algorithms are well suited for the situation where one wants to use a control algorithm that responds quickly to large changes in operating conditions and is not easily confused by moderately large measurement noise and similar disturbances.
We introduce two conceptual models for wireless sensing and control with power-limited sensors and controllers. The limited battery power of the wireless device is captured in the models by imposing hard constraints o...
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ISBN:
(纸本)9783540707004
We introduce two conceptual models for wireless sensing and control with power-limited sensors and controllers. The limited battery power of the wireless device is captured in the models by imposing hard constraints on either the number of available transmissions the device can make, or on the number of cycles it can stay awake. Such hard constraints can be viewed as a measurement budget, under which estimation or control policies will have to be developed over a given decision horizon. Among the two representative models studied here, the first one is one of optimal scheduling of a finite measurement budget for a Gauss-Markov process over an observation horizon. The second one is an optimal estimation problem where the number of transmissions the wireless sensor can make is limited to a number, M, which is less than the observation horizon, N. It is shown that both problems can be solved by employing dynamic-programming type arguments, and their solutions have a threshold characterization.
The abundance of batch processes and continuous processes with wide operating ranges has motivated the development of nonlinear MPC (NMPC) techniques, which employ nonlinear models for prediction. The prediction model...
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This paper presents a review of recent contributions that unite predictive control approaches with Lyapunov-based control approaches at the implementation level (Hybrid predictive control) and at the design level (Lya...
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ISBN:
(纸本)9783540726982
This paper presents a review of recent contributions that unite predictive control approaches with Lyapunov-based control approaches at the implementation level (Hybrid predictive control) and at the design level (Lyapunov-based predictive control) in a way that allows for an explicit characterization of the set of initial conditions starting from where closed-loop stability is guaranteed in the presence of constraints.
The extension of application domains of robotics from factories to human environments leads to implementing proper strategies for close interaction between people and robots. In order to avoid dangerous collision, for...
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ISBN:
(纸本)9783540707004
The extension of application domains of robotics from factories to human environments leads to implementing proper strategies for close interaction between people and robots. In order to avoid dangerous collision, force and vision based control can be used, while tracking human motion during such interaction.
In this paper, we develop a direct adaptive control framework for uncertain linear nonnegative and compartmental dynamical systems with unknown time delay. The specific focus of the paper is on compartmental pharmacok...
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ISBN:
(纸本)9783540719878
In this paper, we develop a direct adaptive control framework for uncertain linear nonnegative and compartmental dynamical systems with unknown time delay. The specific focus of the paper is on compartmental pharmacokinetic models and their applications to drug delivery systems. In particular, we develop a Lyapunov-Krasovskii-based direct adaptive control framework for guaranteeing set-point regulation of the closed-loop system in the nonnegative orthant in the presence of unknown system time delay. The framework additionally guarantees nonnegativity of the control signal. Finally, we demonstrate the framework on a drug delivery model for general anesthesia involving system time delays.
This paper provides an expository introduction to monotone and near-monotone biochemical network structures. Monotone systems respond in a predictable fashion to perturbations, and have very robust dynamical character...
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ISBN:
(纸本)9783540719878
This paper provides an expository introduction to monotone and near-monotone biochemical network structures. Monotone systems respond in a predictable fashion to perturbations, and have very robust dynamical characteristics. This makes them reliable components of more complex networks, and suggests that natural biological systems may have evolved to be, if not monotone, at least close to monotone. In addition, interconnections of monotone systems may be fruitfully analyzed using tools from control theory.
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