Adaptive model predictive controller for power regulation and load reduction in large wind turbines

The efficiency of a SAGD operation depends on developing a uniform steam chamber and maintaining an optimal subcool along the length of the well pair. Heterogeneity in reservoir properties may lead to suboptimal subcool levels. Recently, Model Predictive Control (MPC) based on the real-time production, temperature, and pressure data along with other well and surface constraint information has been proposed for a real-time feedback control of SAGD well pairs. Reservoir dynamics in MPC is represented using either linear step response model or one-dimensional ordinary differential equation. However, such simplified models are insufficient in MPC since SAGD is more complex and highly nonlinear process. Therefore, MPC framework that represents nonlinear behaviour of SAGD over an extended control period is required to achieve optimized subcool and steam conformance. In this research, two novel workflows are proposed to handle nonlinear reservoir dynamics in MPC. First approach known as Adaptive MPC includes recursive estimations at each control interval based on system identification theory. This allows evolution of the coefficients of a fixed model structure such that the updated system identification model in MPC controller reflects current reservoir dynamics adequately. Another approach, Gain-Scheduled MPC, decomposes the subcool control problem in a parallel manner and uses a bank of multiple controllers rather than only one controller. This ensures effective control of the nonlinear reservoir system even in adverse control situations by employing aggressive variations in input parameters. Suggested workflows are implemented using history-matched numerical model of a reservoir located in northern Alberta. Steam injection rates and liquid production rate are considered as input variables in MPC, constrained to available surface facilities. Well-pair is divided into multiple sections and subcool of each section is considered as an output variable. Optimum set-point for subcool is considered as 20°C. Results are compared with actual field data (in which no control algorithm is used) and analyzed based on two criteria: 1) Do all subcools track optimum set-point while maintaining stability in input variables and 2) Does net present value (NPV) of oil improve in case of Adaptive and Gain-Scheduled MPC? In general, we conclude that both Adaptive and Gain-Scheduled MPC provide superior tracking of subcool set-point and hence better steam conformance due to adequate representation of reservoir dynamics by recursive estimation of coefficients and multiple controllers. In addition, results indicate stability in input parameters and improvement in economic performance. NPV is improved by 23.69% and 10.36% in case of Adaptive and Gain-Scheduled MPC, respectively. Under current economic scenario, proposed workflows can improve the NPV of a SAGD reservoir by optimizing the well operational parameters while considering constraints of surface facilities and minimizing environmental footprints.

Intelligent state estimation for fault tolerant integrated frequent RTO and adaptive nonlinear MPC

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.

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.

It is challenging to design a controller for a wind turbine system because of its nonlinear time-varying dynamics. To solve this issue, linear parameter-varying (LPV) control theory has been applied in the recent ten years to deal with nonlinear and wind-speed-dependent dynamics. Before applying the LPV control method, it is required to transform the nonlinear model of the system to an LPV model. The main objectives of this research are to model a 5-MW Reference Wind Turbine for Offshore System as an LPV system, develop a nonlinear wind turbine simulator using MATLAB/Simulink to validate the LPV model, and conduct a preliminary study on LPV control design to test the simulator, which will be used in the future research for applying different LPV control techniques, such as switching LPV control, LPV antiwindup control, etc.

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.

In the navigation of underwater robots, large ocean current disturbance often causes significant tracking errors. To better resist ocean current disturbance, the hydrodynamic characteristics of the spherical underwater robot are studied, and a model predictive control strategy based on adaptive model parameters is proposed, according to these characteristics. Firstly, the hydrodynamic characteristics of the robot under static water and constant flow disturbance were obtained and analyzed by the computational fluid dynamics method. Then, the dynamic models of the robot under different disturbances could be calculated from the data obtained, based on the least square method. Finally, an adaptive model predictive control (AMPC) strategy, with an ocean current observer, was designed, based on the dynamic models. When the current disturbance velocity was twice the robot velocity, the proposed strategy reduced the tracking error by 39% and 42% in X and Y directions, respectively. In addition, the hydrodynamic characteristics were verified by experiments.

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

This paper addresses the problem that the heavy-duty vehicles are more likely to suffer from rollover and drift over issues considering the steering actuator's fault. In order to isolate and estimate the faulty steering actuator, an integrated fault-tolerant control architecture is thus designed to cope with two types of actuators failure based on fault detection and diagnosis (FDD) and fault-tolerant (FT) control. Firstly, a fault isolation method based on plane projection theorem and an H-infinity fault estimator are designed to acquire the faulty signal and estimate the real-time fault coefficient. Then, considering the estimation error and uncertainty disturbance, an adaptive model predictive controller (MPC) is proposed to guarantee the controller's redundancy when the fault occurs on the actuators, while ensuring the rollover prevention and lateral stability. Finally, the proposed adaptive MPC architecture shows the effectiveness and superiority based on the simulation results in Matlab/CarSim full-vehicle model virtual experiment.

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

One of the key element of many industrial process plants is a crude oil distillation system. This system requires heat for the vaporization of a mixture of feed to the distillation column in vapor form. In the existing process of distillation, an abnormal change of heat is exhibited due to improper control and monitoring of disturbances affecting the plant. This would result for undesired loss of product and product purity to be reduced. The parameters that are expected to be considered in the analysis, model and control of a distillation system described under the proposed study are inlet feed temperature, distillation column temperature, feed composition, internal liquid and vapor composition, feed flow rate, reboiler temperature and an external reflux temperature to the distillation tower. Controlling distillation column parameters with Adaptive model predictive control allows for the determination of the predicted future instant values of the plant outputs. Using such control technique, the controlled plant outputs such as the temperature of distillation column, feed preheater, re boiler and the upper reflux is properly controlled and their parameters are estimated using recursive least squares approach in the entire process adaptation mechanism. From the analysis and optimization work made on the proposed system, the efficiency of the plant outputs in tracking their corresponding set point has improved based on the value of the transient system parameters as well as the value of relative volatility of feed mixture to the column. As per the finding in the analysis of the process, 95.4% and 93.5% improvement on the set point tracking and to amount of evaporation liquid feed has been obtained respectively. On the other hand an improvement on transient parameters has been achieved to all plant outputs. As per the result obtained from the analysis, the peak overshoot, settling time and peak time of the system response has found to be less than 40% including the effect of measured disturbance to the plant. Hence, entire process variable optimization has been performed using the parameters of the model predictive controller to provide the proper degree of stability. Finally the proposed method of study has compared with with other control strategies through which the performance the proposed design has been ensured.

Robust control of gust-induced vibration of highly flexible aircraft

An adaptive tube model predictive control (MPC) scheme is presented to manipulate multiple micro- and nanoparticles in fluid suspensions. The control strategy addresses coupled input actuation from the global electric field and estimates the unknown parameters of system dynamics. With the proposed control law, the manipulability of the electrophoresis-based microfluidic manipulation system is quantified and analyzed. The manipulability is affected by the dimensions of the actuating electrodes, the number of particles to be simultaneously and independently controlled, and the configurations of those particles. The scalability of the lattice-shaped distributed array is discussed. Experimental results validate the performance of the control scheme and the manipulability analysis.

Aside from relying on the robustness of the model predictive control (MPC) against model uncertainties, tracking performance degradation in the transient time is an inevitable challenge which is the main motivation of the adaptive MPC (AMPC). To that aim, in this study, a fast online identification scheme is suggested to identify a suitable linear state‐space model used in the control algorithm. Then, the authors prove the convergence of the identification error sequence using Lyapunov candidate function in the time domain and also, in the frequency domain (graph topology) point of view. Also, due to the dependency of the convergence error on the initial selection of the identification model, the authors introduce a stability and convergence ball as a function of generalised stability margin and the v‐gap criteria. Then, the stability and feasibility of the proposed AMPC is guaranteed when using the identified linear model at each sampling time. Moreover, the identification error convergence applies a constraint on the MPC in which, with a suitable selection of the update rate, MPC cost function is been relaxed from the constraint. In the simulations, the authors consider two examples of highly non‐linear dynamic systems. In both cases the proposed control strategy gives satisfactory conclusions.

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.