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 deals with the problem of robust tire/road friction force estimation. Availability of actual value of the friction force generated in contact between the tire and the road has significant ...importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate effects of the friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire/road friction force when friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel friction model. Simulation results suggest very fast friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.
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.
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.
This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of ...thermal zones controlled by a central air handling unit and local variable air volume boxes. In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs). These pdfs are initialized using historical data and updated as new data becomes available. In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations. SMPC uses predictive knowledge of uncertain loads in each zone during the design stage. The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort. This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme. The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests.
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.
This paper proposes a dual-mode model predictive direct current control (MP-DCC) of a permanent magnet synchronous generator (PMSG). The proposed algorithm is capable of minimizing switching losses ...in a two-level synchronous generator side converter (SGSC). A new prediction model that takes the converter dead time into consideration when choosing the optimal switching state is introduced. The proposed prediction model provides a more accurate state prediction, ensuring that the states of the system stay inside the control invariant set in the steady state, even in the case of a significant converter dead time. To guarantee recursive feasibility and closed-loop stability, a flexible control Lyapunov function (CLF) is employed as an optimization problem constraint, which enables the minimization of switching losses both during transients and in the steady state. The influence of the converter dead time on the performance of the proposed algorithm is considered, and accordingly, a control invariant set is determined. Simulation results show that stator currents are kept within the control invariant set if dead time is taken into account in the prediction model. Furthermore, the proposed algorithm is implemented in a digital control system and experimentally verified on a 375 kW interior PMSG. Experimental results verify that the proposed control algorithm provides a successful flying start of the PMSG and show that the application of the flexible CLF results in a lower switching frequency, but also in higher current ripple. By adjusting the upper bound of the control invariant set, a desired tradeoff between the low stator current ripple and the minimization of switching losses can be achieved.
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