Hydrodynamic Characteristic-Based Adaptive Model Predictive Control for the Spherical Underwater Robot under Ocean Current Disturbance

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.

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.

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

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.

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.

Robust model predictive control for continuous nonlinear systems with the quasi-infinite adaptive horizon algorithm

Nonlinear model identification and adaptive model predictive control using neural networks

A Model Predictive Control (MPC) strategy has been suggested and simulated with the empirical dynamic data collected on the Hybrid Performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy, in Morgantown, WV. The HyPer facility is able to simulate gasifier/fuel cell power systems and uses hardware-based simulation approach that couples a modified recuperated gas turbine cycle with hardware driven by a solid oxide fuel cell model. Dynamic data was collected by operating the HyPer facility continuously during five days. Bypass valves along with electric load of the system were manipulated and variables such as mass flow, turbine speed, temperature, pressure, among others were recorded for analysis. This work was developed by focusing on a multivariable recursive system identification structure fitting measured transient data. The results showed that real-time or online data is a viable means to provide a dynamic model for controller design. The excursion dynamic data collected between the setup changes of the experiments was processed off-line to determine the feasibility of applying an adaptive Model Predictive Control strategy. One of the strengths of MPC is that it can allow the designer to impose strict limits on inputs and outputs in order to keep the system within known safe bounds. Two identification structures, ARX and a State-Space model, were used to fit the measured data to dynamic models of the HyPer facility. The State-Space identification was very accurate with a second order model. Visual inspection of the tracking accuracy shows that the ARX approach was approximately as accurate as the State-Space structure in its ability to reproduce measured data. However, by comparing the Loss Function and the FPE parameters, the State-Space approach gives better results. The MPC proved to be a good strategy to control the HyPer facility. The airflow valves and the electric load were used to control the turbine speed and the cathode airflow. For the ARX/State Space models, the MPC was very robust in tracking set-point variations. The anticipation feature of the MPC was revealed to be a good tool to compensate time delays in the output variables of the facility or to anticipate eventual set-point moves in order to achieve the objectives very quickly. The MPC also displayed good disturbance rejection on the output variables when the fuel flow was set to simulate FC heat effluent disturbances. Different off-design scenarios of operation have been tested to confirm the estimated implementation behavior of the plant-controller dynamics.

Nonlinear model predictive control of PEMFC anode hydrogen circulation system based on dynamic coupling analysis

Model Predictive Control and Parameter Estimation applied to Gene Expression in Yeast

In this paper, a “third generation” benchmark problem that focuses on the control of wind excited response of a tall building, using the Model Predictive Control (MPC) scheme, is presented. A 76 story, 306 m tall concrete office tower proposed for the city of Melbourne, Australia, is being used to demonstrate the effectiveness of MPC. The MPC scheme is based on an explicit use of a prediction model of the system response to obtain the control actions by minimizing an objective function. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. By incorporating input/output hard constraints, the MPC scheme provides an optimal control force that satisfies the prescribed constraints.

A model predictive control for a multi-chiller system in data center considering whole system energy conservation

Two-stage inverters composed of the front DC-DC stage and the rear DC-AC stage are broadly employed for the current tracking. Structures vary according to the different control objectives and application occasions. The concerning structure in this paper is made up of the buck-boost converter stage and the single-phase inverter stage. The simulation results show that the intermediate voltage will affect the total harmonic distortion (THD) values. Moreover, under the same other conditions, a minimum THD can be attained by regulating the intermediate voltage. In this paper, a time-varying observer is introduced for the interface parameters identification based on the robustness of model predictive control (MPC) scheme. Combined with the input voltage, the reference current and grid voltage, the optimal intermediate voltage with lower THD can be determined. Besides, a cascaded proportional-integral (PI) MPC scheme is adopted for the intermediate voltage tracking without steady-state error. Meanwhile, based on the accurate interface parameters obtained by the observer, an adaptive MPC scheme is applied for the output current tracking. The simulation results on MATLAB/Simulink and experimental results on FPGA test bench validate the accuracy of the voltage determination and the effectiveness of the control scheme.

Modeling and model predictive control of the BioPower combined heat and power (CHP) plant

This paper studies the optimal control problem for discrete-time switched linear systems with quadratic cost. A model predictive control (MPC) scheme is proposed which results in closed-loop strategies for both switching and control inputs. To reduce the online computation and ensure stability of the MPC scheme, a two-stage pruning algorithm is constructed which is performed offline. The resulting MPC scheme ensures exponential stability, and the cost function is suboptimal. Stability, feasibility, and suboptimality of the MPC scheme are studied. Simulation results are given for the MPC scheme which shows the proposed approach results in reduced computation and acceptable performance.