Haut PDF Model following control for nonlinear systems

Model following control for nonlinear systems

Model following control for nonlinear systems

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An improved algorithm for the control synthesis of nonlinear sampled switched systems

An improved algorithm for the control synthesis of nonlinear sampled switched systems

Received: date / Accepted: date Abstract A novel algorithm for the control synthesis for nonlinear switched sys- tems is presented in this paper. Based on an existing procedure of state- space bisection and made available for nonlinear systems with the help of guaranteed integration, the algorithm has been improved to be able to con- sider longer patterns of modes with a better pruning approach. Moreover, the use of guaranteed integration also permits to take bounded perturba- tions and varying parameters into account. It is particularly interesting for safety critical applications, such as in aeronautical, military or med- ical fields. The whole approach is entirely guaranteed and the induced controllers are correct-by-design. Some experimentations are performed to show the important gain of the new algorithm.
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Model Predictive Control of Building Systems for Energy Flexibility

Model Predictive Control of Building Systems for Energy Flexibility

2.5 Discussions This chapter introduced the case study building in the thesis, the CCHT houses. They were three- storey twin houses, selected to represent the typical single-family dwelling in Canada. The detailed whole building model was built in TRNSYS and the main TRNSYS modules such as the basement and HVAC system were presented. The model calibration was conducted with historic measured data; the calibration method and the analysis of the calibration results were also illustrated. The uncertainty on some key parameters and the lack of detailed (sub-hourly) measurements made this calibration study difficult; the results, however, showed a good agreement with measured values. They were well below the targets suggested by ASHRAE Guideline 14 for calibrated simulations for monthly values, and fairly close for hourly values. Fine-tuning parameters of the model to reduce the energy use gap between simulation and measurements was not pursued in this study, because the initial goal was not only calibrating the energy use, but also the space temperatures. The dynamic results showed that the calibrated model could capture the temperature variations in the space quite well. This is particularly important for the study because the model will be used for control purpose in the following chapters.
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Model-free control for VSC-based HVDC systems

Model-free control for VSC-based HVDC systems

Modern power grids contain more and more complex dynamic elements, most of the times based on power electronics. This is the case of the HVDC links. They are also active elements which have an impact on the neighborhood of the AC network in which they are inserted. Particularly, HVDCs controls may affect the transient stability of the near zone (see, e.g., [1]). This impact was optimized in [2] and [3] by using an extended control model. However, as this model captures several grid dynamics in addition to the ones of the HVDC link, its order may be high. This situation is encountered in many other power systems applications. For example, analysis and damping of inter-area modes of a grid needs a model of the whole synchronous zone which results in high-order mathematical objects. Another typical example is the so-called secondary regulations (voltage and frequency) for which a global vision of the system is also necessary.
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A Reflective Model for Architecting Feedback Control Systems

A Reflective Model for Architecting Feedback Control Systems

Keywords-Autonomic Computing, Model-Driven Engineer- ing, Software Architecture I. I NTRODUCTION The 24/7 deployment of distributed systems is dramati- cally increasing the complexity and maintenance costs of software systems. The ability to adapt then becomes crucial for such systems. Autonomic computing is a promising way to organize self-adaptive systems. This approach aims at realizing computing applications that can dynamically adjust themselves to accommodate changing environments and user needs with minimal or no human intervention [1]. The engineering of such systems in the large is rather a challenging task. It is hard to find an appropriate model that controls the adaptation itself [2]. Its implementation being generally integrated in a feedback control loop, developing several adaptive behaviors necessitates to reason on them, at least to obtain an appropriate coordination between them. Moreover, engineering in the large obviously needs some reuse capabilities on loops or more likely, on elements composing the loops [3].
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Dual Particle Output Feedback Control based on Lyapunov drifts for nonlinear systems

Dual Particle Output Feedback Control based on Lyapunov drifts for nonlinear systems

Dual Particle Output Feedback Control based on Lyapunov drifts for nonlinear systems Emilien Flayac, Karim Dahia, Bruno H´eriss´e, and Fr´ed´eric Jean Abstract— This paper presents a dual receding horizon out- put feedback controller for a general non linear stochastic sys- tem with imperfect information. The novelty of this controller is that stabilization is treated, inside the optimization problem, as a negative drift constraint on the control that is taken from the theory of stability of Markov chains. The dual effect is then created by maximizing information over the stabilizing controls which makes the global algorithm easier to tune than our previous algorithm. We use a particle filter for state estimation to handle nonlinearities and multimodality. The performance of this method is demonstrated on the challenging problem of terrain aided navigation.
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Approximate Prediction-Based Control Method for Nonlinear Oscillatory Systems with Applications to Chaotic Systems

Approximate Prediction-Based Control Method for Nonlinear Oscillatory Systems with Applications to Chaotic Systems

In fact, PBC has a practical limitation for continuous-time systems if it is applied as proposed for discrete-time systems [21], the need of future state values in real-time. In [28] we proposed the approxi- mate Prediction-Based Control (aPBC) with a methodology based on the implicit Runge-Kutta method and state estimation applied to predict future states for the free system in real-time based on system model. The authors claim that aPBC is the continuous-time applicable version of PBC because it uses prediction and stabilizes free system UPOs, ideally, vanishing steady state control effort. In counter- part, predicting future states has some drawbacks because the proposed future state prediction scheme requires an increase in the closed-loop system order and consequently computational power for numerical integration. Moreover, it is also subjected to prediction model and real system mismatch and integration method precision for application.
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Predictive control based on nonlinear observer for muscular force and fatigue model

Predictive control based on nonlinear observer for muscular force and fatigue model

Predictive Control Based on Nonlinear Observer for Muscular Force and Fatigue Model T.Bakir 1 , B. Bonnard 2 and S. Othman 3 Abstract— The Functional Electrical Stimulation (FES) is used in the case of neurological disorders (paralyzed muscles) or the muscle reinforcement (sportsmen). A recent model was proposed coupling a force model and a fatigue model. Based on the specific structure of the model, we present a predictive control scheme using online estimation of the fatigue variables with a nonlinear observer. It is numerically tested in a preliminary study.
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Adaptive robust model predictive control for nonlinear systems

Adaptive robust model predictive control for nonlinear systems

This thesis presented a tube Model Predictive Control framework for nonlinear systems that have uncertain dynamics and are subject to unknown external disturbances. [r]

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A Polynomial Approach for Analysis and Optimal Control of Switched Nonlinear Systems

A Polynomial Approach for Analysis and Optimal Control of Switched Nonlinear Systems

First, this class of functions uniformly approximate any continuous nonlinear function defined over a compact domain R n (see [42]). Moreover, the canonical expression in- troduced in [42] uses the minimum and exact number of parameters, and it is the first PWL expression able to represent PWL mappings in arbitrary dimensional domains. As a consequence of this, an efficient characterization is obtained from the viewpoint of memory storage and numerical evaluation [26]. Second, the approximation can be used in real implementations; the points taken from the nonlinear model may be replaced by points taken from sensors or data directly from the process. Thus, it addresses the problem of finding a PWL approximation of system where a reasonable number of measure samples of the vector field is available (regression set) [98]. Third, this alternative approach deals with an approximation which is easier to handle than the nonlinear model. In fact, it might use many tools developed for hybrid systems –e.g., the MLD model based approach [16]– since algorithms for translating MLD systems into PWL systems are available [15], [102]. Finally, this CPWL is used in a model based control, termed probing control in [68], being a first step development a hybrid probing control. The task of the controller is to determine, at every instant, the best control action (the best feed substrate) based on the compilation of the sensor’s on-line information (or for the nonlinear model). The fact that the probing strategy for feedback control requires a minimum of process knowledge is exploited in [7]. This work refers to a probing control as it is presented in [7] for E. coli. Short pulses to the feed rate are added, and taking into account the system response, the pulse is increased or decreased according with the tuning rule. The probing control strategy avoids acetate accumulation while maintaining a high growth rate [7], [101]. The main contribution of Chapter 4 is a hybrid dynamical model using orthonor-
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Near-optimal strategies for nonlinear and uncertain networked control systems

Near-optimal strategies for nonlinear and uncertain networked control systems

Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems Lucian Bus¸oniu Romain Postoyan Jamal Daafouz Abstract—We consider problems where a controller commu- nicates with a general nonlinear plant via a network, and must optimize a performance index. The system is modeled in discrete time and may be affected by a class of stochastic uncertainties that can take finitely many values. Admissible inputs are constrained to belong to a finite set. Exploiting some optimistic planning algorithms from the artificial intelligence field, we propose two control strategies that take into account the communication constraints induced by the use of the network. Both strategies send in a single packet long-horizon solutions, such as sequences of inputs. Our analysis characterizes the relationship between computation, near-optimality, and trans- mission intervals. In particular, the first strategy imposes at each transmission a desired near-optimality, which we show is related to an imposed transmission period; for this setting, we analyze the required computation. The second strategy has a fixed computation budget, and within this constraint it adapts the next transmission instant to the last state measurement, leading to a self-triggered policy. For this case, we guarantee long transmission intervals. Examples and simulation experiments are provided throughout the paper.
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Repetitive Control Design Based on Forwarding for Nonlinear Minimum-Phase Systems

Repetitive Control Design Based on Forwarding for Nonlinear Minimum-Phase Systems

Despite the large progress in practical applications (see, among them, [11–14]), when considering the problem of a RC-design in the continuous-time domain, many questions are still open. In particular, it is not clear whether an exact (i.e., infinite-dimensional) RC-scheme can be used (from a theoretical point of view) to achieve asymptotic convergence of the desired regulated output. In fact, one of the main limitations of existing continuous-time schemes is that only “boundary information” (namely, the values of the delayed signal at instants t and t + T ) is used for control purposes. Such a constraint, however, strongly re- stricts the class of systems to which a RC-scheme can be applied, that is nonlinear systems which are strictly input passive (in other words, with a direct feedthrough term). We refer to [1] for a proof for linear systems where it is shown that exponential stability of a (continuous-time) lin- ear system incorporating a pure delay in the RC-scheme can be achieved only for systems having zero-relative de- gree between the input and the regulated output; alterna- tively, see [15, 16] for a proof in the context of nonlinear systems based on dissipativity operators.
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Anti-windup Compensation for Nonlinear Systems via Gradient Projection: Application to Adaptive Control

Anti-windup Compensation for Nonlinear Systems via Gradient Projection: Application to Adaptive Control

Anti-windup Compensation for Nonlinear Systems via Gradient Projection: Application to Adaptive Control Justin Teo and Jonathan P. How Abstract— Control saturation is an important limitation in practical control systems and it is well known that performance degradation or instability may result if this limitation is not effectively addressed. Using ideas from the gradient projection method in nonlinear programming, we propose a new anti- windup scheme for multi-input, multi-output nonlinear dy- namic controllers. The key idea is to project the controller state update law onto the tangent plane of the active saturation constraints. To do this, we first extend the gradient projection method to the continuous-time case that can accommodate multiple nonlinear constraints. This is then used for anti-windup compensation, resulting in a hybrid controller that switches its state update law over arbitrary combinations of saturating controls. Simulations on a nonlinear two-link robot driven by an adaptive sliding mode controller illustrates its effectiveness and limitations.
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2020 — A nonlinear neural network-based model predictive control for industrial gas turbine

2020 — A nonlinear neural network-based model predictive control for industrial gas turbine

ABSTRACT Gas turbines are now extensively used in aviation, oil and gas applications and power gen- eration. With this increasing use in a diverse range of applications, gas turbine engines are designed to operate in a wide operating envelope. Typically, the ambient temperature can vary substantially from a hot summer day to a cold winter night. In addition, different fuel types may be used. Furthermore, the performance of a turbine engine deteriorates with use because of component degradation caused by erosion and corrosion. These requirements for guaran- teed high performance levels while maintaining stability and safe operation with minimum overall cost impose severe challenges on control system design. In this dissertation, new ap- proaches for gas turbine engine modelling and multivariable advanced controller design are investigated. A nonlinear model predictive control (NMPC) approach based on an ensemble of recurrent neural networks (NN) is utilized to achieve the control objectives for a Siemens SGT-A65 three spool aeroderivative gas turbine engine used for power generation. A novel ensemble method is proposed, which results in an adaptive NN model. The simulation results show improvement in accuracy and robustness by using the proposed modelling approach. Also, another important gain is the very rapid execution time (40,5 μs), which can support many real time applications that require model-based control design.
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On the use of nonlinear model predictive control without parameter adaptation for batch processes

On the use of nonlinear model predictive control without parameter adaptation for batch processes

Received: 12 May 2016; Accepted: 22 August 2016; Published: 29 August 2016 Abstract: Optimization techniques are typically used to improve economic performance of batch processes, while meeting product and environmental specifications and safety constraints. Offline methods suffer from the parameters of the model being inaccurate, while re-identification of the parameters may not be possible due to the absence of persistency of excitation. Thus, a practical solution is the Nonlinear Model Predictive Control (NMPC) without parameter adaptation, where the measured states serve as new initial conditions for the re-optimization problem with a diminishing horizon. In such schemes, it is clear that the optimum cannot be reached due to plant-model mismatch. However, this paper goes one step further in showing that such re-optimization could in certain cases, especially with an economic cost, lead to results worse than the offline optimal input. On the other hand, in absence of process noise, for small parametric variations, if the cost function corresponds to tracking a feasible trajectory, re-optimization always improves performance. This shows inherent robustness associated with the tracking cost. A batch reactor example presents and analyzes the different cases. Re-optimizing led to worse results in some cases with an economical cost function, while no such problem occurred while working with a tracking cost.
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Model Predictive Control for Demand Response Management Systems in Smart Buildings

Model Predictive Control for Demand Response Management Systems in Smart Buildings

Control design for buildings becomes increasingly challenging as many components, such as weather predictions, occupancy levels, energy costs, etc., have to be considered while develop- ing new algorithms. A building is a complex system that consists of a set of subsystems with different dynamic behaviors. Therefore, it may not be feasible to deal with such a system with a single dynamic model. In recent years, a rich set of conventional and modern control schemes have been developed and implemented for the control of building systems in the context of the Smart Grid, among which model redictive control (MPC) is one of the most- frequently adopted techniques. The popularity of MPC is mainly due to its ability to handle multiple constraints, time varying processes, delays, uncertainties, as well as disturbances. This PhD research project aims at developing solutions for demand response (DR) man- agement in smart buildings using the MPC. The proposed MPC control techniques are im- plemented for energy management of HVAC systems to reduce the power consumption and meet the occupant’s comfort while taking into account such restrictions as quality of service and operational constraints. In the framework of MPC, different power capacity constraints can be imposed to test the schemes’ robustness to meet the design specifications over the operation time. The considered HVAC systems are built on an architecture with a layered structure that reduces the system complexity, thereby facilitating modifications and adap- tation. This layered structure also supports the coordination between all the components. As thermal appliances in buildings consume the largest portion of the power at more than one-third of the total energy usage, the emphasis of the research is put in the first stage on the control of this type of devices. In addition, the slow dynamic property, the flexibility in operation, and the elasticity in performance requirement of thermal appliances make them good candidates for DR management in smart buildings.
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Periodic event-triggered control for nonlinear networked control systems

Periodic event-triggered control for nonlinear networked control systems

Periodic event-triggered control for nonlinear networked control systems W. Wang, R. Postoyan, D. Neˇsi´c and W.P.M.H. Heemels Abstract—Periodic event-triggered control (PETC) is an ap- pealing paradigm for the implementation of controllers on plat- forms with limited communication resources, a typical example being networked control systems. In PETC, transmissions over the communication channel are triggered by an event generator, which depends solely on the available plant and controller data, and is only evaluated at given sampling instants to enable its digital implementation. In this paper, we consider the general scenario where the controller communicates with the plant via multiple decoupled networks. Each network may contain multiple nodes, in which case a dedicated protocol is used to schedule transmissions among these nodes. The transmission instants over the networks are asynchronous and generated by local event generators. At given sampling instants, the local event generator evaluates a rule, which only involves the measurements and the control inputs available locally, to decide whether a transmission is needed over the considered network. Following the emulation approach, we show how to design the local triggering generators to ensure input-to-state stability and L p -stability for the overall
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Model Checking as Control: Feedback Control for Statistical Model Checking of Cyber-Physical Systems

Model Checking as Control: Feedback Control for Statistical Model Checking of Cyber-Physical Systems

ples of feedback-control for the analysis of cyber-physical systems (CPS). FC-SSC uses stochastic system identification to learn a CPS model, im- portance sampling to estimate the CPS state, and importance splitting to control the CPS so that the probability that the CPS satisfies a given property can be efficiently inferred. We illustrate the utility of FC-SSC on two example applications, each of which is simple enough to be easily understood, yet complex enough to exhibit all of FC-SCC’s features. To the best of our knowledge, FC-SSC is the first statistical system checker to efficiently estimate the probability of rare events in realistic CPS ap- plications or in any complex probabilistic program whose model is either not available, or is infeasible to derive through static-analysis techniques.
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Comparative experimental study of control theoretic and connectionist controllers for nonlinear systems

Comparative experimental study of control theoretic and connectionist controllers for nonlinear systems

The adaptive controller has the capability to quickly learn and adapt to parameter uncertainties. Like the sliding control, the explicit model of the nonlinear structure i[r]

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Application of multiple model control to space structure systems with nonlinear inertial properties

Application of multiple model control to space structure systems with nonlinear inertial properties

The SISO designs for the MACE test article in Chapter 3 imply that the MM technique effectively enhances robust stability and performance of compensators for two [r]

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