The online computational load associated with Nonlinear Model predictive control (NMPC) is a serious barrier for its application to systems with fast dynamics. This paper addresses numerical approaches for fast and re...
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ISBN:
(纸本)9781479944088
The online computational load associated with Nonlinear Model predictive control (NMPC) is a serious barrier for its application to systems with fast dynamics. This paper addresses numerical approaches for fast and real-time feasible NMPC. The applicability of these methods to fast systems with sampling times in order of milliseconds is investigated through a nonlinear teleoperation system. The main ideas of the “real-time iteration” (RTI) approach for real-time optimization are addressed. Condensing approach to efficient structure exploitation of the described RTI scheme is then presented. The described method ensures a considerable reduction of the computational effort as required in real-time implementations. The NMPC algorithms based on these methods can therefore be carried out at higher sampling rates. The results show promising performance of the nonlinear teleoperation system using fast NMPC controller.
We present two event-triggered MPC laws that do not require to solve a quadratic program (QP) in every time step but only upon certain events. We prove one of the control laws results in exactly the same closed-loop b...
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ISBN:
(纸本)9781467371605
We present two event-triggered MPC laws that do not require to solve a quadratic program (QP) in every time step but only upon certain events. We prove one of the control laws results in exactly the same closed-loop behavior as classical MPC. The second control law requires even fewer QPs per time. It is suboptimal w.r.t. the MPC cost function, but still results in asymptotically stable closed-loop behavior. We illustrate the event-triggered MPC laws with two examples.
Fuzzy supervisory predictive control based on genetic algorithm optimization is proposed. For the nonlinear model, through a general objective function dynamically optimized to determine the optimal set-point for a gi...
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Fuzzy supervisory predictive control based on genetic algorithm optimization is proposed. For the nonlinear model, through a general objective function dynamically optimized to determine the optimal set-point for a given regulatory level, by using genetic algorithm in order to solve the nonlinear optimization problem for the setpoint, and compared with the supervisory predictive control based on linear model and nonlinear model. Simulation results show the proposed algorithm has better control performance.
Here, the predictive control algorithm has planned for a type-2 large-scale process based on hierarchical scheme. The useful large-scale systems are mostly a type of nonlinear systems, which are large in scope and it ...
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ISBN:
(数字)9781728172965
ISBN:
(纸本)9781728172972
Here, the predictive control algorithm has planned for a type-2 large-scale process based on hierarchical scheme. The useful large-scale systems are mostly a type of nonlinear systems, which are large in scope and it is usually hard task to obtain the dynamic of the system. Since years ago, one way to deal with such systems has been modeling them in the form of fuzzy and decomposing into some subsystem to control them in decentralized. The interval type-2 (IT2) fuzzy Takagi-Sugeno (T-S) is chosen to modeling the dynamic of the large-scale system because of the fact that it is robust to uncertainties of modeling. One of the most prevalent and handy controller is model predictive control (MPC), which has been applied since years ago. Resorting to the fact that model predictive control stems from a cost function, thereby, the hierarchical scheme has used for optimization problem. Finally, an instance studied to illustrate the proposed algorithm.
In this paper, a nonlinear model predictive control (NMPC) based on the Hammerstein model with piecewise linear function is presented. By introducing binary variables, the approach turns NMPC problem into a mixed inte...
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In this paper, a nonlinear model predictive control (NMPC) based on the Hammerstein model with piecewise linear function is presented. By introducing binary variables, the approach turns NMPC problem into a mixed integer programming problem. An example shows the possibility of application of this control scheme to chemical process.
Occupancy in an office can significantly influence Indoor Air Quality parameters. A reliable control of Indoor Air Quality represents a crucial requirement in the Heating, Ventilation and Air Conditioning field. For t...
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ISBN:
(数字)9798350348637
ISBN:
(纸本)9798350348644
Occupancy in an office can significantly influence Indoor Air Quality parameters. A reliable control of Indoor Air Quality represents a crucial requirement in the Heating, Ventilation and Air Conditioning field. For this purpose, specific modelization and control techniques can be used in order to guarantee the desired level of the involved parameters. A nonlinear Model predictive control strategy is used in the present paper in order to manage carbon dioxide level in an office through natural ventilation. This controller is located on an upper level with respect to other controllers, e.g., temperature ones. Occupancy and wind speed are taken into account in the control problem formulation as measured input disturbance variables. The impact of occupancy previewing mismatch on carbon dioxide control is assessed through tailored simulations, which considered exact knowledge, underestimation and overestimation of the occupancy.
Robust model predictive control of discrete nonlinear systems with bounded time-varying delay and persistent disturbances is investigated in this paper. The T-S fuzzy systems are utilized to represent nonlinear system...
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ISBN:
(纸本)9781509035502
Robust model predictive control of discrete nonlinear systems with bounded time-varying delay and persistent disturbances is investigated in this paper. The T-S fuzzy systems are utilized to represent nonlinear systems. A Razumikhin-type Lyapunov function is adopted for time-delay systems due to its advantage in reducing the complexity especially for systems with large delays and disturbances. The robust positive invariance set theory for systems subjected to time-varying delay and disturbances is analyzed. In addition, the input-to-state stability is realized due to persistent disturbances. The controller synthesis conditions are derived by solving a sequence of matrix inequalities. Simulation on a continuous stirred-tank reactor (CSTR) is illustrated to verify the effectiveness of the proposed method.
This paper provides a tutorial on the continuation/ GMRES method (C/GMRES), which is a real-time optimization algorithm tailored for nonlinear model predictive control (NMPC) and a Maple version of AutoGenU, which is ...
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This paper provides a tutorial on the continuation/ GMRES method (C/GMRES), which is a real-time optimization algorithm tailored for nonlinear model predictive control (NMPC) and a Maple version of AutoGenU, which is an automatic code generation system utilizing a symbolic computation language to simulate NMPC with C/GMRES. Once such settings of NMPC as the state equation and performance index are specified in a Maple worksheet, a problem-dependent C source file is automatically generated by symbolic computation, compiled, and executed. The worksheet also loads data files of simulation results and plots their time histories. This paper describes the usage and settings of AutoGenU for Maple.
This work describes the application of a Min-Max predictive controller to a control laboratory plant. Min-max formulations of Model Based predictive control (MBPC) are one of the possible approaches in the literature ...
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This work describes the application of a Min-Max predictive controller to a control laboratory plant. Min-max formulations of Model Based predictive control (MBPC) are one of the possible approaches in the literature to deal with the control of plants subject to bounded uncertainties. One of the drawbacks of Min-Max MBPC is the amount of calculation required to find a control sequence. The controller used in this paper is relatively efficient numerically: the control sequence is calculated solving a linear programming problem with a reasonable number of constraints. This allows the calculation time to be small enough to apply the controller to reasonably fast real systems. The Min-Max MBPC was tested on-line in a two-tank laboratory plant under different working situations to check the robustness, stability and general performance of the designed controller. The results are quite encouraging.
Model predictive control (MPC) has the ability to cope with hard constraints on control and state. It has, therefore, been widely applied in most industries specially, petrochemical industries. Dynamic safety margin (...
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Model predictive control (MPC) has the ability to cope with hard constraints on control and state. It has, therefore, been widely applied in most industries specially, petrochemical industries. Dynamic safety margin (DSM) is a performance index used to measure the distance between a predefined safety boundary, described by a set of inequality constraints, in state space and system trajectory as it evolves. Designing MPC based on DSM is especially important for safety critical system to maintain a predefined margin of safety during transient and steady state. In this work, MPC based on DSM is used in fault tolerant control (FTC) design. The proposed method of FTC is suitable for single and multi-model system according to the fault type and fault information. It can compensate missed information about the fault and uncertainties in the faulty model
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