This papers presents a nonlinear model predictive controller (NMPC) for temperature control in solar collector fields. The proposed NMPC uses feedback linearization for handling the systems nonlinearities in a mixed i...
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This papers presents a nonlinear model predictive controller (NMPC) for temperature control in solar collector fields. The proposed NMPC uses feedback linearization for handling the systems nonlinearities in a mixed integer quadratic programming (MIQP) formulation that makes the constraints convex in the new coordinates, making the controller solution an optimal one. Several simulations are shown with real data and a validated model of solar field to illustrate the advantages of the proposed strategy over other general-purpose NMPC methods, showing great improvement in constraint satisfaction.
This paper presents a method for control of formations of Unmanned Aerial Vehicles (UAVs) in urban environments with several obstacles. Therefore the trajectories for each UAV are planned using mixedintegerquadratic...
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This paper presents a method for control of formations of Unmanned Aerial Vehicles (UAVs) in urban environments with several obstacles. Therefore the trajectories for each UAV are planned using mixed integer quadratic programming (MIQP) to describe a minimization problem. The result of this minimization problem then characterizes a collision free trajectory for each UAV using the commanded formations to fulfil the missions. The description of the UAVs, the inter UAV collision avoidance, the collision avoidance with obstacles as well as the description of formations will be shown in detail together with some simulation results in this paper. In addition the introduction explains the fields of interest in such formations of UAVs and what kind of advantage they can bring in comparison to today's solutions. The novelty in the approach in this paper is the description of formations of UAVs used in combination with MIQP to change formations, to add additional UAVs into an existing formation and to split formations, simply by changing some parameters in the description of the formation.
In order to facilitate the optimal operation in the presence of process disturbances, the optimal selection of controlled variables plays a vital role. In this paper, we present a mixed integer quadratic programming m...
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In order to facilitate the optimal operation in the presence of process disturbances, the optimal selection of controlled variables plays a vital role. In this paper, we present a mixed integer quadratic programming methodology to select controlled variables c = Hy as the optimal combinations of fewer/all measurements of the process. The proposed method is evaluated on a toy test problem and on a binary distillation column case study with 41 trays.
The mixed Logical Dynamical (MLD) formalism has proved to be an efficient modelling framework for hybrid systems described by dynamics, logic and constraints. Furthermore, it allows formulating and solving practical p...
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The mixed Logical Dynamical (MLD) formalism has proved to be an efficient modelling framework for hybrid systems described by dynamics, logic and constraints. Furthermore, it allows formulating and solving practical problems such as control, using for example predictive strategies. However, its main drawback remains the computation load due to the complexity ofthe MIQPs to be solved. To overcome this problem, this paper presents a multi-MLD model, considering split state space regions inside which restricted simpler size MLD models take into account only variables variations that may occur. This approach enables to considerably reduce the computation time, to become more convenient for real time implementation even with small sampling time. This strategy is applied in simulation to the control of a three tanks benchmark.
A new approach to the design of Model Predictive Controller (MPC) that simultaneously addresses the actuator saturation and backlash is proposed in this paper. The discrete characteristics of the actuator backlash all...
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A new approach to the design of Model Predictive Controller (MPC) that simultaneously addresses the actuator saturation and backlash is proposed in this paper. The discrete characteristics of the actuator backlash allows one to reformulate the input constraints as a set of mixedinteger linear inequalities. As a result, the MPC is designed by solving a mixed-integerquadraticprogramming problem. Simulation results are presented to show how this new approach performs as compared to the existing techniques of the backlash compensation when they are applied to an industrial case study
Abstract Optimal control structure selection is vital to operate the process plants optimally in the presence of disturbances. In this paper we review the controlled variable selection, c = Hy, where y includes all th...
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Abstract Optimal control structure selection is vital to operate the process plants optimally in the presence of disturbances. In this paper we review the controlled variable selection, c = Hy, where y includes all the measurements. The objective is to find the matrix H such that steady-state operation is optimized while controlled variables c ' s are kept constant using inputs, when there are disturbances. Several cases are studied such as the optimal individual measurements, the optimal combinations of fewer/all measurements and the optimal combinations with structural constraints. The proposed methods are evaluated on a distillation column case study with 41 trays.
Abstract In this paper, model predictive control (MPC) based trajectory generation for a unicycle with obstacle avoidance is addressed. Since MPC based methods can only consider obstacle avoidance at discrete time ste...
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Abstract In this paper, model predictive control (MPC) based trajectory generation for a unicycle with obstacle avoidance is addressed. Since MPC based methods can only consider obstacle avoidance at discrete time steps, a collision may occur in intervals between prediction time steps. In order to prevent this problem, we propose two methods which guarantee the obstacle avoidance not only at prediction time steps but also in intervals between them. The first method reduces the maximum velocity near obstacles. The second one constrains transition of collision avoidance constraints to exclude possibilities of collisions between time steps. Since both methods do not need iteration of optimization, it is expected that the computation time is not significantly increased compared with existing methods. Numerical examples and experiments show the effectiveness of the proposed methods.
In this article, we propose novel and global Architecture-Aware Analytic MAPping (A3MAP) algorithms applied to Networks-on-Chip (NoCs) not only with homogeneous Processing Elements (PEs) on a regular mesh network as d...
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In this article, we propose novel and global Architecture-Aware Analytic MAPping (A3MAP) algorithms applied to Networks-on-Chip (NoCs) not only with homogeneous Processing Elements (PEs) on a regular mesh network as done by most previous application mapping algorithms but also with heterogeneous PEs on an irregular mesh or custom network. As the main contributions, we develop a simple yet efficient interconnection matrix that can easily model any core graph and network. Then, an application mapping problem is exactly formulated to mixed integer quadratic programming (MIQP). Since MIQP is NP-hard, we propose two effective heuristics, a successive relaxation algorithm achieving short runtime, called A3MAP-SR and a genetic algorithm achieving high mapping quality, called A3MAP-GA. We also propose a partition-based application mapping approach for large-scale NoCs, which provides better trade-off between performance and runtime. Experimental results show that A3MAP algorithms reduce total hop count, compared to the previous application mapping algorithms optimized for a regular mesh network, called NMAP [Murali and Micheli 2004] and for an irregular mesh and custom network, called CMAP [Tornero et al. 2008]. Furthermore, A3MAP algorithms make packets travel shorter distance than CMAP, which is related to energy consumption.
This manuscript presents a novel direct current model predictive control with long prediction horizon for medium-voltage polyphase permanent magnet synchronous motor drives. By utilizing the proposed technique, we can...
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This manuscript presents a novel direct current model predictive control with long prediction horizon for medium-voltage polyphase permanent magnet synchronous motor drives. By utilizing the proposed technique, we can reduce the armature current harmonics and torque ripples, minimize the electrical and mechanical losses, improve the torque and speed rendering quality, and achieve a highly reliable permanent magnet motor drive. The state-space model of a representative polyphase permanent magnet synchronous motor is derived with the armature current and flux linkage jointly chosen as the state-space variables. Leveraging completing the squares, the mixed-integer-quadratic-programming (MIQP) problem can be formulated and efficiently solved, which provides the optimal switching sequence for the IGBT inverter. The proposed control algorithm is also implemented and examined with computer simulation to show the robustness and effectiveness.
This paper proposes a Laguerre Model Predictive Control（LMPC） method for trajectory generation of the accompanying satellite in space station to execute detection tasks. The design of ensuring the accompanying satel...
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This paper proposes a Laguerre Model Predictive Control（LMPC） method for trajectory generation of the accompanying satellite in space station to execute detection tasks. The design of ensuring the accompanying satellite reach the target position and avoid obstacles simultaneously are difficult due to the critical non-convex and nonlinear constraints, and solving the large number of decision variables as a result of model predictive control is too computationally intensive to be implemented. To counter this problem, the trajectory generation problem is reformulated as a mixed integer quadratic programming（MIQP） problem according to the quadratic objective function and obstacles constraints, and the LMPC with smaller variables method is employed in this paper. The obstacles avoidance constraints are solved by mixed integer quadratic programming with LMPC framework, with particularly advantages of the method could obtain a feasible and satisfied solution when the constraints are rigorous. Besides, this paper introduces a LMPC approach to reduce the computational burden by a set of Laguerre functions, guaranteed the long control horizon condition is solved by the small number of control variables. Application for trajectory generation of satellite in highly constrained space station is conducted to demonstrate the effectiveness of the newly proposed method.
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