In this brief, we propose a predictive algorithm for direct yaw moment control (DYC) in which a vehicle model is identified by a finite-dimensional approximation of the Koopman operator. The Koopman ...operator is a linear predictor for nonlinear dynamical systems based on raising the nonlinear dynamics into a higher-dimensional space where its evolution is linear. A novel method for the finite-dimensional numerical approximation of the Koopman operator is proposed, called enhanced extended dynamic mode decomposition (E 2 DMD). This method allows the reduction of the basis dimension, determined by a user-defined dictionary of observable functions, to achieve a trade-off between model complexity and accuracy. The E 2 DMD Koopman vehicle model was obtained from the dataset generated by simulating different scenarios using the nonlinear vehicle model and was then used to develop a Koopman operator model predictive control (KMPC) algorithm. KMPC was compared to a linear time variant (LTV) and a nonlinear model predictive control (NMPC), which are widely used in the literature, and showed better performance in some cases and a reduction in computational complexity in all cases.
An asymptotically stabilizing sequential distributed model predictive control (MPC) of a 3D tower crane is proposed. Stability is ensured by employing three locally stabilizing MPC control laws. In ...the case of Lipschitz continuous local MPC control laws, a terminal cost and a terminal set constraint are used as stabilizing ingredients while robust control invariant feasible set is used as an additional constraint to guarantee recursive feasibility. On the other hand, in the case of an arbitrary cost function, switching to a robust dual-mode local control law is used inside of the terminal set to guarantee asymptotic stability.
•A new model predictive controller for VRLA battery charging is developed.•Convexity of the battery charging optimization problem is proved.•Recursive feasibility and stability of the battery ...charging problem is proved.•The developed VRLA battery charging algorithm is experimentally verified.
In this paper an algorithm for optimal charging of a valve-regulated lead-acid (VRLA) battery stack based on model predictive control (MPC) is proposed. The main objective of the proposed algorithm is to charge the battery stack as fast as possible without violating the constraints on the charge current, the battery voltage and the battery temperature. In addition, a constraint on the maximum allowed voltage of every battery in the battery stack is added in order to minimize degradation of the individual batteries during charging. The convexity of the VRLA battery charging optimization problem is proven, which makes the control algorithm suitable for efficient on-line implementation via solving a quadratically constrained quadratic program (QCQP). The recursive feasibility and stability of the proposed control strategy is ensured. The proposed algorithm is validated both through simulation tests and on the experimental setup.
Model Predictive Control (MPC) has attracted much attention and is widely used in power electronics. However, implementing the MPC algorithm is still a difficult task due to the fast dynamics of ...power converters and strict time constraints. In this paper, a computationally efficient MPC algorithm for grid-tied power converters based on the fast gradient projection method and invariant set theory is proposed. The algorithm is implemented and tested through hardware-in-the-loop simulations using Texas Instruments digital signal processors and Xilinx Field Programmable Gate Arrays platforms.
This paper presents a complete solution for constrained control of a permanent magnet synchronous machine. It utilizes field-oriented control with proportional-integral current controllers tuned to ...obtain a fast transient response and zero steady-state error. To ensure constraint satisfaction in the steady state, a novel field-weakening algorithm which is robust to flux linkage uncertainty is introduced. Field weakening problem is formulated as an optimization problem which is solved online using projected fast gradient method. To ensure constraint satisfaction during current transients, an additional device called current reference governor is added to the existing control loops. The constraint satisfaction is achieved by altering the reference signal. The reference governor is formulated as a simple optimization problem whose objective is to minimize the difference between the true reference and a modified one. The proposed method is implemented on Texas instruments F28343 200 MHz microcontroller and experimentally verified on a surface mounted permanent magnet synchronous machine.
We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. ...Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman's exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot's pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman's algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.
Predictive control based on an informative system trajectory, instead of a physics-based model, has received significant attention in recent years. This paper investigates the potential of using such ...data-driven control for vehicle dynamics control and autonomous path following. By considering the path following problem in the error space, the underlying system is approximately linear and existing results for data-driven predictive control can be applied. Also, scheduling based on longitudinal speed can be readily included. The proposed control algorithm was tested on two different lane change maneuvers in a high-fidelity simulation environment.
This paper proposes a direct model predictive control method with a fixed switching frequency for stator flux control of a synchronous reluctance machine. Besides ensuring a fixed switching ...frequency, the main objective is to minimize the ripple of the stator flux. The objective function, which calculates the switching time instants within the switching period based on the stator flux gradients, is formulated as a standard constrained quadratic programming (QP) problem. Instead of using general-purpose QP solvers, an iterative algorithm based on the active-set method with Lagrange multipliers is proposed to solve this particular QP problem while reducing the computational complexity of the proposed algorithm and enabling successful implementation in real-time embedded systems. The proposed method has been successfully implemented on a laboratory model and the results are compared with a conventional indirect model predictive control. The experimental results show better performance in stator flux ripple and lower total stator current distortion factor for the proposed method compared to the conventional method. A 7.5 kW synchronous reluctance motor drive was used for the experimental validation of the proposed control method.
This paper analyses an field-programmable gate array (FPGA) implementation of a set-based model predictive control algorithm for controlling a grid-tied inverter with an LCL filter. Parallelizing the ...MPC algorithm in FPGA hardware led to substantial decrease in computation time. The implementation is show to be synthesizeable on a commercial mid-range FPGA device.
In this paper, we consider the problem of constrained tracking of piecewise constant references for nonlinear dynamical systems. In the considered problem we assume that an existing controller ...satisfies constraints in a corresponding positive-invariant set of the system. To solve the problem we propose the use of homothetic transformations of the positive-invariant set to modify the existing control law. The proposed approach can be implemented as a tracking model predictive control or as a reference governor. Simulation and experimental results are provided, showing the applicability of the proposed approach to a class of nonlinear systems.
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