Adaptive estimation-based hierarchical model predictive control methodology for battery active equalization topologies: Part II - equalizer control

Robots and automated systems are increasingly being introduced to unknown and dynamic environments where they are required to handle disturbances, unmodeled dynamics, and parametric uncertainties. Robust and adaptive control strategies are required to achieve high performance in these dynamic environments. In this paper, we propose a novel adaptive model predictive controller that combines model predictive control (MPC) with an underlying $\mathcal{L}_{1}$ adaptive controller to improve trajectory tracking of a system subject to unknown and changing disturbances. The $\mathcal{L}_{1}$ adaptive controller forces the system to behave in a predefined way, as specified by a reference model. A higher-level model predictive controller then uses this reference model to calculate the optimal reference input based on a cost function, while taking into account input and state constraints. We focus on the experimental validation of the proposed approach and demonstrate its effectiveness in experiments on a quadrotor. We show that the proposed approach has a lower trajectory tracking error compared to non-predictive, adaptive approaches and a predictive, nonadaptive approach, even when external wind disturbances are applied.

This work introduces an adaptive model predictive control (MPC) scheme for nonlinear processes. It is well-known that the conventional MPC uses linear model to represent local dynamics of a nonlinear process and resulting optimization problem is convex. However, the model mismatch between linear model and nonlinear process may significantly degrade the performance of MPC and thereby the closed-loop stability may not be guaranteed. To address this issue, the model used in MPC should be accurate enough for different operating conditions. The contribution of this paper is the development of a steady state constrained autoregressive with exogenous terms model (SSARX) that is still linear and adaptive to the variation of operating conditions. This new model then can be integrated with a linear MPC to achieve a trade-off between computational complexity and model accuracy. The proposed controller is applied in a continuous fermentation process and compared to an MPC with unconstrained ARX model (non-adaptive). The simulation shows that the MPC with ARX model leads to an offset during setpoint tracking while the MPC with SSARX model is offset free.

Pitch control is related to the efficient energy conversion and safe operation of wind turbine, which is a challenging control issue because of the highly nonlinear dynamics of wind turbine, system constraints and stochastic environmental disturbances, etc. In this article, we propose the adaptive model predictive pitch control strategy based on linear parameter varying (LPV) model to solve the pitch control issue of large-scale wind turbine. Firstly, a high-fidelity LPV model is constructed to approximate the high-nonlinearity dynamics of wind turbine. The gap metric theory is introduced to optimize the selection of local models. Based on the proposed LPV model, an adaptive model predictive controller with linear time-varying Kalman filter is designed for large-scale wind turbine. The benchmark 5 megawatt (MW) wind turbine model provided by FAST is employed to evaluate the controller performance. The results show that, compared with the traditional gain scheduling proportional–integral control strategy, the proposed control strategy can achieve better power regulation and load reduction performance under gust, step and turbulent wind.

In this article, a nonlinear disturbance observer‐based multiple‐model adaptive explicit model predictive control (NDO‐MMAEMPC) scheme is developed for a nonlinear MIMO system with parametric uncertainty as well as an external disturbance. The proposed method manages external disturbances without affecting the degree of smoothness of the control signals. Further, to cope with the unknown system uncertainty, blending‐based adaptive identification schemes are used for the same class of systems. For each identification model, an explicit nonlinear model predictive controller is computed off‐line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of explicit controllers are being blended online using adaptive weight. An extended Kalman filter algorithm is employed for the estimation of the unavailable states of the nonlinear system. Using an aerodynamic laboratory setup, the effectiveness of the proposed control algorithm is verified.

Adaptive MPC for Uncertain Discrete-Time LTI MIMO Systems with Incremental Input Constraints

Solid oxide fuel cell (SOFC) becomes increasingly popular in dc microgrid applications. Controlling SOFC is challenging because the dynamics of SOFC are difficult to maintain under complex internal reactions and changing operating conditions. To solve these problems, this article proposes a novel adaptive model predictive control (AMPC) algorithm, which adopts a parameter estimator to update the system parameters online. The robustness of the proposed AMPC is investigated under different microgrid scenarios, including the overload, underload, short-circuit, and significant dc bus voltage drop situations. The proposed AMPC algorithm produces superior SOFC control performance over the conventional model predictive control (MPC), Wiener MPC, and PI and fuzzy PI controllers. Furthermore, it significantly reduces the system model dependence that is shared by nearly all the model-based SOFC control methods. The convergence of parameter estimation in the proposed AMPC is rigorously proved. The effectiveness of the proposed algorithm is validated through hardware-in-the-loop experiments under various operating conditions and system parameter variations.

A new adaptive model predictive control (AMPC) is proposed in this paper to enhance the performance of solid oxide fuel cell (SOFC) in complex power systems. Compared to the existing methods, such as the PI control and conventional model predictive control, the proposed algorithm produces better tracking performances and overcomes the problem of model dependence. The effectiveness of the proposed algorithm under power grid fault and system parameter variations are verified by simulation results.

Adaptive model predictive control for a class of constrained linear systems based on the comparison model

For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems.

An adaptive direct recurrent neural network model is developed for nonlinear dynamic system modelling in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. The effectiveness of the developed adaptive model is demonstrated by applying to modelling a simulated continuous stirred tank reactor (CSTR). The model converges to the new process dynamics very quickly after a constant disturbance is added, and therefore can be used as an adaptive model in the adaptive model predictive control or internal model control for time-varying systems or fault tolerant control of nonlinear systems.

Two-stage inverters have been commonly applied in the distribution systems, where Total Harmonic Distortion (THD) is important for the judgement of the tacking effects. This paper focuses on the current tracking of the grid-tied two-stage inverter composed by the front buck-boost converter and the rear single-phase inverter. A cascaded Proportional-Integral (PI) Model Predictive Control (MPC) is applied for the intermediate voltage tracking of the front stage. Meanwhile, an adaptive MPC scheme is adopted for the output current tracking of the single-phase inverter stage by introducing an time-varying observer for the filter inductance and load resistance identification. By regulating the intermediate voltage, there will be a corresponding voltage which minimizes THD under the same other parameters. Hence, a reference voltage generator based on the circuit parameters, the input voltage, the reference current and grid voltage is employed for the voltage regulation to obtain lower THD. The simulation results show the effectiveness of the control scheme for the two-stage inverters applied in this paper.

SummaryThis article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

The performance of the path tracking control system is poor when vehicles navigate under extreme conditions of high speed and large curvature due to the uncertainty of vehicle model parameters, modeling errors and disturbances. To address this issue, this paper introduces a Linear Model Predictive Controller (GP‐SOMPC) based on Gaussian Process (GP) and Snake Optimizer (SO) to achieve high‐performance trajectory tracking control under large curvatures through learning. This algorithm adopts a two‐degree‐of‐freedom vehicle dynamics model and models disturbances and unmodeled dynamics as Gaussian Processes (GP) using collected experimental data. Additionally, an Adaptive Weighting Coefficient Model Predictive Controller based on the Snake Optimizer (SO) method is presented to improve the poor adaptation of vehicle trajectory tracking accuracy according to reference path curvature under fixed weighting coefficients in traditional Model Predictive Control (MPC), which results in inadequate trajectory tracking precision. The simulation experiments have verified that the proposed GP‐SOMPC algorithm can enhance trajectory tracking precision under high‐speed, large curvature conditions and exhibits good adaptability to variations in path curvature.

In this work, an adaptive selective harmonic elimination model predictive control (ASHEMPC) method for a 3L-T type inverter is proposed. This algorithm is based on model predictive control (MPC), introduces adaptive selective harmonic elimination (ASHE) algorithm, and performs digital delay compensation through two-step prediction. Compared with the harmonic suppression strategies based on PI or PR controller, ASHE-MPC significantly improves the current loop bandwidth, speeds up the dynamic response, and greatly increases the harmonic order that can be eliminated. Compared with the harmonic suppression strategies based on the deadbeat model predictive control (DB-MPC), ASHE-MPC can eliminate low-order harmonics caused by multiple factors (including dead-time and grid background harmonics), and can easily eliminate multiple harmonics (5 th -31 th in this paper). The proposed ASHE-MPC method for the first time achieves adaptive selective harmonic elimination in model predictive control. The ASHE-MPC method is novel, simple and efficient. A three-level T-type grid-connected inverter for dead-time compensation is used as an example to verify the feasibility of this method.

Safe operation of Gas Turbine Engines (GTEs) is one of the paramount missions in the GTE industry. Ensuring reliable operation and control performance is a challenging task that involves optimizing engine performance across various operating points. The Model Predictive Control (MPC) technique is a powerful approach that provides an optimization framework to meet the management and control requirements of GTEs during their operation. This work presents a systematic control framework to efficiently control a GTE. The proposed control algorithm employs an adaptive MPC methodology that ensures both safe and efficient engine operation. The MPC structure is adapted across varying operating points, and its parameters are optimized to provide control inputs that ensure best engine performance during both transient and steadystate engine operation. To enhance control reliability, the MPC is systematically coupled with min-max limiters to maintain critical variables within allowable bounds, ultimately to prevent engine surge, flame-out, and exceeding unsafe variable limits. Through this approach, the aim is to achieve a superior level of safety and operability for GTE control and management. Simulation results on a generic commercial turbofan engine with a 40,000-pound thrust show that the proposed algorithm outperforms a gain-scheduled PI controller with min-max limiters, and demonstrate enhanced performance in engine management and control.