The rendezvous problem between autonomous vehicles is formulated as an optimal cooperative control problem with terminal constraints. Optimal control problems are often solved by seeking solutions which satisfy the fi...
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ISBN:
(纸本)9783540743545
The rendezvous problem between autonomous vehicles is formulated as an optimal cooperative control problem with terminal constraints. Optimal control problems are often solved by seeking solutions which satisfy the first order necessary conditions for an optimum. Such an approach is based on a Hamiltonian formulation, which leads to a difficult two-point boundary-value problem. We propose a different approach in which the control history is found directly by a genetic algorithm search method. The main advantage of the method is that it does not require the development of a Hamiltonian formulation and consequently, it eliminates the need to deal with an adjoint problem, which leads to a difficult two-point boundary-value problem in nonlinear ordinary differential equations. This method has been applied to the solution of interception and rendezvous problems in an underwater environment, where the direction of the velocity vector is used as the control. We consider the effects of gravity, thrust and viscous drag and treat the rendezvous location as a terminal constraint. We then study cooperative rendezvous problems between spacecraft. We treat the case where the magnitude of the continuous low thrust vector is fixed and the direction of the thrust is used as the control. The spacecraft start from different points on an initial circular orbit and meet at a point on a circular orbit of larger radius, with the same final orbital velocity. The present genetic algorithm was developed to treat complex interception and rendezvous problems involving multiple vehicles.
A number of plants of technological interest include transport phenomena in which mass, or energy, or both, flow along one space dimension, with or without reactions taking place, but with neglected dispersion. This t...
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This paper focuses on the design of a nonlinear model predictive control (NMPC) scheme for a cement grinding circuit, i.e., a ball mill in closed loop with an air classifier. The multivariable controller uses two mass...
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ISBN:
(纸本)9783540726982
This paper focuses on the design of a nonlinear model predictive control (NMPC) scheme for a cement grinding circuit, i.e., a ball mill in closed loop with an air classifier. The multivariable controller uses two mass fractions as controlled variables, and the input flow rate and the classifier selectivity as manipulated variables. As the particle size distribution inside the mill is not directly measurable, a receding-horizon observer is designed, using measurements at the mill exit only. The performance of the control scheme in the face of measurement errors and plant-model mismatches is investigated in simulation.
A minimum-time optimal recharging control strategy for high pressure gas storage tank systems is described in this work. The goal of the nonlinear model-based controller is to refill the tank in minimum time with a tw...
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ISBN:
(纸本)9783540726982
A minimum-time optimal recharging control strategy for high pressure gas storage tank systems is described in this work. The goal of the nonlinear model-based controller is to refill the tank in minimum time with a two-component gas mixture of specified composition subject to hard constraints on the component flow rates, tank temperature, and tank pressure. The nonlinearity in this system arises from the nonideal behavior of the gas at high pressure. The singular minimum-time optimal control law can not be reliably implemented in the target application due to a lack of sensors. Minimum-time optimal control is therefore approximated by a nonlinear model-based constraint controller. In order to account for the uncertainty in the unmeasured state of the storage tank, the state sensitivities to the control and process measurements are propagated along with the state to obtain a state variance estimate. When the variance of the state exceeds a maximum threshold, the constraint control algorithm automatically degrades into a fail-safe operation.
The application of nonlinear model predictive control (NMPC) for the temperature control of an industrial batch polymerization reactor is illustrated. A real-time formulation of the NMPC that takes computational delay...
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ISBN:
(纸本)9783540726982
The application of nonlinear model predictive control (NMPC) for the temperature control of an industrial batch polymerization reactor is illustrated. A real-time formulation of the NMPC that takes computational delay into account and uses an efficient multiple shooting algorithm for on-line optimization problem is described. The control relevant model used in the NMPC is derived from the complex first-principles model and is fitted to the experimental data using maximum likelihood estimation. A parameter adaptive extended Kalman filter (PAEKF) is used for state estimation and on-line model adaptation. The performance of the NMPC implementation is assessed via simulation and experimental studies.
This paper presents a Receding Horizon control (RHC) algorithm to the problem of on-line flight path optimization for aircraft in a dynamic Free-Flight (FF) environment. The motivation to introduce the concept of RHC ...
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ISBN:
(纸本)9783540726982
This paper presents a Receding Horizon control (RHC) algorithm to the problem of on-line flight path optimization for aircraft in a dynamic Free-Flight (FF) environment. The motivation to introduce the concept of RHC is to improve the robust performance of solutions in a dynamic and uncertain environment, and also to satisfy the restrictive time limit in the real-time optimization of this complicated air traffic control problem. Compared with existing algorithms, the new algorithm proves more efficient and promising for practical applications.
In this work, two methods based on a nonlinear MPC scheme are proposed to solve close-loop stochastic dynamic optimization problems assuring both robustness and feasibility with respect to output constraints. The main...
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ISBN:
(纸本)9783540726982
In this work, two methods based on a nonlinear MPC scheme are proposed to solve close-loop stochastic dynamic optimization problems assuring both robustness and feasibility with respect to output constraints. The main concept lies in the consideration of unknown and unexpected disturbances in advance. The first one is a novel deterministic approach based on the wait-and-see strategy. The key idea is here to anticipate violation of output hard-constraints, which are strongly affected by instantaneous disturbances, by backing off of their bounds along the moving horizon. The second method is a new stochastic approach to solving nonlinear chance-constrained dynamic optimization problems under uncertainties. The key aspect is the explicit consideration of the stochastic properties of both exogenous and endogenous uncertainties in the problem formulation (here-and-now strategy. The approach considers a nonlinear relation between the uncertain input and the constrained output variables.
Unmanned aerial vehicles (UAVs) are excellent platforms for detecting and tracking objects of interest on or near the ground due to their vantage point and freedom of movement. This paper presents a cooperative vision...
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ISBN:
(纸本)9783540743545
Unmanned aerial vehicles (UAVs) are excellent platforms for detecting and tracking objects of interest on or near the ground due to their vantage point and freedom of movement. This paper presents a cooperative vision-based estimation and tracking system that can be used in such situations. The method is shown to give better results than could be achieved with a single UAV, while being robust to failures. In addition, this method can be used to detect, estimate and track the location and velocity of objects in three dimensions. This real-time, vision-based estimation and tracking algorithm is computationally efficient and can be naturally distributed among multiple UAVs. This chapter includes the derivation of this algorithm and presents flight results from several realtime estimation and tracking experiments conducted on MIT's Real-time indoor Autonomous Vehicle test ENvironment (RAVEN).
Model Predictive control (MPC) originated in the late seventies and has developed considerably since then. The term Model Predictive control does not designate a specific control strategy but rather an ample range of ...
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In this paper, an alternative approach to the computation of control invariant sets for piecewise affine systems is presented. Based on two approximation operators, two algorithms that provide outer and inner approxim...
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ISBN:
(纸本)9783540726982
In this paper, an alternative approach to the computation of control invariant sets for piecewise affine systems is presented. Based on two approximation operators, two algorithms that provide outer and inner approximations of the maximal robust control invariant set are presented. These algorithms can be used to obtain a robust control invariant set for the system. An illustrative example is presented.
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