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Description Reviews More Details. Description This book presents a framework for a new hybrid scheduling strategy for heterogeneous, asymmetric telecommunication environments. It discusses comparative advantages and disadvantages of push, pull, and hybrid transmission strategies, together with practical consideration and mathematical reasoning.

Free Returns We hope you are delighted with everything you buy from us. However, if you are not, we will refund or replace your order up to 30 days after purchase. While convergence of such DPSA for convex problems has been studied, the presence of the integrality constraint makes it inapplicable. Consequently, convergence of the proposed DPSA and properties at the converging point under certain assumptions is presented.

Numerical results on maximum clique problem are provided to sustain the effectiveness of the proposed algorithm. Keywords: Distributed control , Power systems , Predictive control for linear systems Abstract: This paper proposes a centralized and a distributed sub-optimal control strategy to maintain in safe regions the real-time transient frequencies of a given collection of buses, and simultaneously preserve asymptotic stability of the entire network.

In a receding horizon fashion, the centralized control input is obtained by iteratively solving an open-loop optimization aiming to minimize the aggregate control effort over controllers regulated on individual buses with transient frequency and stability constraints. Due to the non-convexity of the optimization, we propose a convexification technique by identifying a reference control input trajectory. We then extend the centralized control to a distributed scheme, where each subcontroller can only access the state information within a local region.

Simulations on a IEEE network illustrate our results. Keywords: Distributed control , Agents-based systems , Adaptive control Abstract: The problem of time-constrained multi-agent task scheduling and control synthesis is addressed.

Product | Data Scheduling and Transmission Strategies in Asymmetric Telecommunication Environments

We assume the existence of a high level plan which consists of a sequence of cooperative tasks, each of which is associated with a deadline and several Quality-of-Service levels. By taking into account the reward and cost of satisfying each task, a novel scheduling problem is formulated and a path synthesis algorithm is proposed.

Based on the obtained plan, a distributed hybrid control law is further designed for each agent. Under the condition that only a subset of the agents are aware of the high level plan, it is shown that the proposed controller guarantees the satisfaction of time constraints for each task.

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A simulation example is given to verify the theoretical results. Keywords: Chemical process control , Process Control , Optimization Abstract: The efficiency of irrigation systems is critically important for reducing water consumption in agricultural production process, especially with water scarcity nowadays being more and more severe all over the world. Empirical irrigation that often leads to over-watering and results in low yield and water waste should be prevented and substituted by advanced automatic irrigation systems. In this work, we focus on the data-driven real-time irrigation control and propose a model predictive control MPC -based approach to achieve desired plant root-zone deficit level given variable precipitation and evapotranspiration as disturbance.

To take future weather into irrigation decision making, specialized local weather prediction is realized for local irrigation spots where regional weather forecast is less reliable, and the formulation of a dynamic uncertainty set is introduced to account for prediction errors and used in robust MPC design. The proposed approach is evaluated through a real-world case study in which we demonstrate that the implementation of the data-driven real time irrigation control system effectively facilitates the control of plant root-zone deficit level for local irrigation spots.

Keywords: Chemical process control , Process Control , Optimization Abstract: Model predictive control MPC under chance constraints has been a promising solution to complicated control problems subject to uncertain disturbance. However, traditional approaches either require exact knowledge of probabilistic distributions, or rely on massive multi-scenarios that are generated to represent uncertainties.

In this paper, a novel approach is proposed based on actively learning a compact high-density region from available data in form of a polytope. This is achieved by adopting the support vector clustering, which has been recently utilized in data-driven robust optimization. A new strategy is developed to calibrate the size of the polytope, which provides appropriate probabilistic guarantee. Finally the optimal control problem is cast as a robust optimization problem, which can be efficiently handled by existing numerical solvers. The proposed method commonly requires less data samples than traditional approaches, and can help reducing the conservatism, thereby enhancing the practicability of model predictive control.

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The efficacy of the proposed method is verified based on a simulated example. In a laboratory continuous stirred tank reactor CSTR ran the neutralization reaction of acetic acid and sodium hydroxide. The controlled output was pH value of the reaction mixture and the manipulated variable was the volumetric flow rate of acid. The integral action was designed to remove a steady-state error in set-point tracking.

An uncertain mathematical model of the controlled process was identified from data measured in multiple step responses. Extensive laboratory experimental analysis was performed to tune the weighting matrices of an objective function to optimize the control performance of robust MPC for a laboratory plant. Control performance of the reactor was evaluated using analytical quality criteria. Keywords: Control applications , Adaptive control , Process Control Abstract: The complicated physical and chemical reactions in the smelting process and the blast furnace BF internal complex operating environment have led to the difficulty of establishing the model-based controllers.

Therefore, model free control methods meet the actual needs of the engineering projects. However, due to the sparse characteristic of the molten iron quality MIQ data in BF ironmaking, traditional model free adaptive control based MIQ control methods cannot control such a complex industrial system with strong nonlinear time-varying dynamics.

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Two groups of verified experiments are performed to evaluate the performance of the controller. The results show that the proposed method not only has better control performance than the compared traditional CFDL based model free adaptive control method and data-driven model predictive control MPC method, but also can guarantee the bounded-input bounded- output stability of the MIQ output control system for BF ironmaking process.

Keywords: Distributed parameter systems , Estimation , Process Control Abstract: In this paper, we present a model of reservoir pressure dynamics in view of estimating influx during drilling. The distributed nature of the model is shown to have an important impact on the transient behaviour of pressure and flow rate when a liquid influx is present. Then, two observers, designed using a backstepping approach, are used to estimate the distributed reservoir pressure as well as wellbore states. The relevance of the approach is illustrated in industry-relevant simulations. Keywords: Manufacturing systems and automation , Process Control , Machine learning Abstract: In the manufacturing industry, it is crucial to identify process variables that strongly affect product quality so that high product quality is maintained. Conventional methods based on variable importance have not necessarily shown good results. In the present work, we propose a new method to estimate variable importance.

  • Honggang Wang;
  • 1. Introduction.
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First, we construct a regression model for predicting product quality from process variables by using support vector regression or gaussian process regression, then we compute variable importance from the sensitivity of the model. It is demonstrated through a numerical example and an industrial case study that the proposed method outperforms conventional methods such as partial least squares and random forest. Keywords: Predictive control for nonlinear systems , Large-scale systems , Numerical algorithms Abstract: Large-scale nonlinear model predictive control NMPC often relies on real-time solution of optimization problems that are constrained by partial differential equations PDEs.

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  • 1. Introduction?

However, the size and complexity of the underlying PDEs present significant computational challenges. In this regard, the development of fast, efficient and scalable PDE-constrained optimization solvers remains central to large-scale NMPC. As a contribution in this direction, this paper proposes a new efficient preconditioned iterative scheme for optimal control of large-scale time-dependent diffusion-reaction problems with nonlinear reaction kinetics.

The scheme combines a custom-made high-order spectral Petrov-Galerkin SPG method with a new preconditioner tailored for the linear-quadratic control problems that underly Sequential Quadratic Programming SQP methods. The preconditioner is matrix-free and amenable to parallelization.

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In the absence of control, such processes lead to unstable systems that naturally exhibit finite-time blow-up phenomena. Open-loop simulations demonstrate the ability of the SPG scheme to efficiently control SFI processes, independently of the problem size and the model parameters. We develop a new proof of convexity for the problem that allows the nonlinear dynamics to be modelled as a linear system, then demonstrate the performance of ADMM in comparison with Dynamic Programming DP through simulation.

The results demonstrate up to two orders of magnitude improvement in solution time for comparable accuracy against DP. Keywords: Predictive control for nonlinear systems , Robust control Abstract: Within this paper we consider time optimal robust model predictive control of a robot arm that carries a glass plate. To this end, we propose a respective model and constraints on the strains in the extremal fibers of the glass plate based on the section modulus and tensile strength to avoid breakages.

In order to synthesize a control strategy, we propose to use a tailored ellipsoidal tube based model predictive control scheme, which can deal with the highly nonlinear constraints of the glass plate. The necessity of modeling the strains in the fibers as well as the properties of the proposed robust control method are illustrated in a realistic case study for a KUKA youBot model, which is simulated in the presence of process noise.