Nonlinear model predictive controllers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a...
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ISBN:
(纸本)9783540726982
Nonlinear model predictive controllers (NLMPC) using fundamental dynamic models and online nonlinear optimization have been in service in ExxonMobil Chemical since 1994. The NLMPC algorithm used in this work employs a state space formulation, a finite prediction horizon, a performance specification in terms of desired closed loop response characteristics for the outputs, and costs on incremental manipulated variable action. The controller can utilize fundamental or empirical models. The simulation and optimization problems are solved simultaneously using sequential quadratic programming (SQP). In the paper, we present results illustrating regulatory and grade transition (servo) control by NLMPC on several industrial polymerization processes. The paper outlines the NLMPC technology employed, describes the current status in industry for extending linear model predictive control to nonlinear processes or applying NLMPC directly, and identifies several needs for improvements to components of NLMPC.
In this paper, we present an online optimization approach for coordinating large-scale robot teams in both convex and non-convex polygonal environments. In the former, we investigate the problem of moving a team of m ...
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ISBN:
(纸本)9783540743545
In this paper, we present an online optimization approach for coordinating large-scale robot teams in both convex and non-convex polygonal environments. In the former, we investigate the problem of moving a team of m robots from an initial shape to an objective shape while minimizing the total distance the team must travel within the specified workspace. Employing SOCP techniques, we establish a theoretical complexity of O(k(1.5)m(1.5)) for this problem with O(km) performance in practice - where k denotes the number of linear inequalities used to model the workspace. Regarding the latter, we present a multi-phase hybrid optimization approach. In Phase I, an optimal path is generated over an appropriate tessellation of the workspace. In Phase II, model predictive control techniques are used to identify optimal formation trajectories over said path while guaranteeing against collisions with obstacles and workspace boundaries. Once again employing SOCP, we establish complementary complexity measures of O(l(3.5)m(1.5)) and O(l(1.5)m(3.5)) for this problem with O(l(3)m) and O(lm(3)) performance in practice - where l denotes the length of the optimization horizon.
This chapter presents a structural analysis approach for the design of fault tolerant estimation algorithms. The general fault tolerance problem setting is first given, and structural analysis is presented in the comp...
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ISBN:
(纸本)9783540707004
This chapter presents a structural analysis approach for the design of fault tolerant estimation algorithms. The general fault tolerance problem setting is first given, and structural analysis is presented in the component based modeling frame. An original condition for structural observability is developed, which is constructive, since it allows to identify those Data Flow Diagrams by which unknown variables can be estimated, both in healthy and in faulty conditions. The link with two basic dependability concepts, namely critical faults and reliability is shown.
This chapter describes a principled, yet computationally efficient way for a team of UAVs with Received Signal Strength Indicator (RSSI) sensors to locate radio frequency emitting ground vehicles in a large environmen...
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ISBN:
(纸本)9783540743545
This chapter describes a principled, yet computationally efficient way for a team of UAVs with Received Signal Strength Indicator (RSSI) sensors to locate radio frequency emitting ground vehicles in a large environment. Such a capability has a range of both civilian and military applications. RSSI sensor readings are noisy and multiple emitters will cause ambiguous, overlapping signals to be received by the sensor. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. To build a coherent distributed picture given communication limitations, the UAVs share only those sensor readings that induce the largest changes in their local filter. Each UAV translates its probability distribution into a map of information entropy and then plans a path that will maximize the reduction in entropy (or conversely provides the highest information gain.) Planned paths are shared with a subset of other UAVs to minimize overlapping search. Experiments in a medium fidelity simulation environment show the approach to be lightweight and effective. Live flight results with lightweight Class I UAVs validate our approach.
The study of unmanned aerial systems (UAS) has been an active research topic in recent years due to the rapid growth of UAS real-world applications driven by the Global War on Terrorism (GWOT). UAS are defined as a co...
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ISBN:
(纸本)9783540743545
The study of unmanned aerial systems (UAS) has been an active research topic in recent years due to the rapid growth of UAS real-world applications driven by the Global War on Terrorism (GWOT). UAS are defined as a complete unmanned system including control station, data links, and vehicle. Unmanned aerial vehicle (UAV) refers to the vehicle element of the UAS. Currently UAS operate standalone, independent of neighboring UAS and used primarily for reconnaissance. However UAS roles are expanding to the point where UAV swarms will operate as cooperative autonomous units. The reason is that cooperatively controlled multiple UAS have the potential to complete mission critical complicated tasks with the higher efficiency and failure tolerance, such as coordinated navigation to a target, coordinated terrain exploration and search and rescue operations. This chapter presents study results associated with real-time trajectory planning and cooperative formation flying algorithms for use in performing multi-UAV cooperative operations. Closed form analytical and simulation results were used along with a UAS simulation test bed for evaluating and verifying these algorithms in multi-UAV cooperative scenarios. The full kinematics constraints of the UAV model is explicitly used, ensuring the planned trajectories and formations are feasible. Two operational modes are implemented for every UAV, one corresponding to the search phase, the other corresponding to the cooperative flying phase. Each phase is executed upon receiving commands. Finally this chapter discusses the use of this simulation environment for multi-UAV cooperative operator training.
In this chapter we consider the problem of robust visual tracking of multiple targets using several, not necessarily registered, cameras. The key idea is to exploit the high spatial and temporal correlations between f...
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ISBN:
(纸本)9783540743545
In this chapter we consider the problem of robust visual tracking of multiple targets using several, not necessarily registered, cameras. The key idea is to exploit the high spatial and temporal correlations between frames and across views by (i) associating to each viewpoint a set of intrinsic coordinates on a low dimensional manifold, and (ii) finding an operator that maps the dynamic evolution of points over manifolds corresponding to different viewpoints. Once this operator has been identified, correspondences are found by simply running a sequence of frames observed from one view through the operator to predict the corresponding current frame in the other view. As we show in the chapter, this approach substantially increases robustness not only against occlusion and clutter, but also against appearance changes. In addition, it provides a scalable mechanism for sensors to share information under bandwidth constraints. These results are illustrated with several examples.
The experimental study of genetic regulatory networks has made tremendous progress in recent years resulting in a huge amount of data on the molecular interactions in model organisms. It is therefore not possible anym...
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ISBN:
(纸本)9783540719878
The experimental study of genetic regulatory networks has made tremendous progress in recent years resulting in a huge amount of data on the molecular interactions in model organisms. It is therefore not possible anymore to intuitively understand how the genes and interactions together influence the behavior of the system. In order to answer such questions, a rigorous modeling and analysis approach is necessary. In this chapter, we present a family of such models and analysis methods enabling us to better understand the dynamics of genetic regulatory networks. We apply such methods to the network that underlies the nutritional stress response of the bacterium E. coli.
A formulation of continuous-time nonlinear MPC is proposed in which input trajectories are described by general time-varying parameterizations. The approach entails a limiting case of suboptimal single-shooting, in wh...
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ISBN:
(纸本)9783540726982
A formulation of continuous-time nonlinear MPC is proposed in which input trajectories are described by general time-varying parameterizations. The approach entails a limiting case of suboptimal single-shooting, in which the dynamics of the associated NLP are allowed to evolve within the same timescale as the process dynamics, resulting in a unique type of continuous-time dynamic state feedback which is proven to preserve stability and feasibility.
A novel robust controller, chance constrained nonlinear MPC, is presented. Time-dependent uncertain variables are considered and described with piecewise stochastic variables over the prediction horizon. Restrictions ...
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ISBN:
(纸本)9783540726982
A novel robust controller, chance constrained nonlinear MPC, is presented. Time-dependent uncertain variables are considered and described with piecewise stochastic variables over the prediction horizon. Restrictions are satisfied with a user-defined probability level. To compute the probability and its derivatives of satisfying process restrictions, the inverse mapping approach is extended to dynamic chance constrained optimization cases. A step of probability maximization is used to address the feasibility problem. A mixing process with both an uncertain inflow rate and an uncertain feed concentration is investigated to demonstrate the effectiveness of the proposed control strategy.
Mathematical modeling is used for individual patients to help for an early diagnosis of the evolution of the infection. The feasibility of the method is depicted on some patients who start a HAART (Highly Active AntiR...
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ISBN:
(纸本)9783540719878
Mathematical modeling is used for individual patients to help for an early diagnosis of the evolution of the infection. The feasibility of the method is depicted on some patients who start a HAART (Highly Active AntiRetroviral Therapy). It is shown how this mathematical study can be used in the early diagnosis of the immunological failure for HIV patients.
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