Linear Modelling of Six Pulse Rectifier and Designee of Model Predictive Controller with Stability Analysis

Model predictive control (MPC) is used in the CO2 removal process in the Subang field to improve its control performance. MPC is used to maintain the CO2 concentration at the sweet gas output by controlling the feed gas pressure (PIC-1101), makeup water flow rate (FIC-1102), and amine flow rate (FIC-1103). The empirical model applied to MPC to represent the process model is the auto-regressive exogenous (ARX) model. The ARX model is compared with the first order plus dead time (FOPDT) model based on the root mean square error (RMSE) between the model and the actual process, then MPC parameters are tuned which include sampling time (T), prediction horizon (P) and control horizon (M) to control for the three variables. Improved control performance is measured based on the integral square error (ISE). The results show that the ARX model is the best model for the CO2 removal process with an RMSE value of 35%-91% smaller than the FOPDT model. The optimal control parameters Prediction Horizon (P), Control Horizon (M) and Sampling Time (T) in the CO2 removal process are 75, 25 and 1 on PIC-1101, 25, 10 and 1 on FIC-1102, and 30, 25 and 1 on FIC-1103. The MPC-ARX (MPC using ARX model) can improve the control performance of 33% in the servo control and 6-56% on the regulatory control. However, not all of them showed an increase in control performance improvement from previous studies even though they had used the best model (ARX). This is due to the MPC parameter setting that is not yet appropriate, so it needs to be retuning.

An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method.

The challenges of contingent island operation of hydropower plants are addressed by proposing an enhanced frequency control with advanced control algorithms. The study is based on a real power plant and considers the relevant power system in its entirety along with the load and transmission lines. Integrated transfer functions between the guide vane opening of a hydro turbine and the frequency of the associated generator were determined by in-situ identification. A model predictive controller, a fractional order PID controller, and a PID controller utilizing integral absolute error and integral time absolute error criteria were designed and developed. The performance of the proposed algorithms with the identified plant model was tested on a NI-cRIO 9049 FPGA platform. Improved frequency control in terms of setpoint change was achieved with the MPC and FOPID controllers. The MPC controller also features betters disturbance rejection compared to conventional PID controllers, which are tuned according to integral error criteria. The considered algorithms have been simulated in different operating scenarios, such as variations of active power load and transmission length. The proposed approach enhances stability and consequently avoids operational failures of hydropower plants in contingent islanding mode.

A bi-Ievel architecture based on model predictive control is proposed for air traffic management. The aim is to reduce the computational complexity of online optimization in air traffic control, while producing accurate control decisions. We integrate a slow-rate centralized MPC controller including a global, long-term prediction window (top level of control), with several fast-rate decentralized MPC controllers including local, short-term prediction windows (bottom level of control). All aircraft are steered towards their destination in the shortest possible time, while forbidden areas are avoided. The performance of the proposed bi-Ievel control architecture is compared via a case study with (1) a slow-rate centralized MPC controller similar to the top control level, (2) a fast-rate decentralized MPC controller similar to the bottom control level, and (3) a fast- rate centralized MPC controller with a prediction horizon the same as the top control level and decision sampling times the same as the bottom control level: The results show that while the bi-Ievel MPC controller is significantly more efficient - w.r.t. the computation time - than the fast-rate centralized MPC controller, these two controllers are comparable w.r.t. accuracy of the decisions. Moreover, with a comparable computation time, the bi-Ievel MPC controller attains a superior control performance - considering the accuracy of decisions - compared to the slow-rate centralized and fast-rate decentralized MPC controllers individually. This shows that the integration of these two control approaches in the proposed bi-Ievel architecture can significantly boost their performance.

Asymptotic Characteristics of Toeplitz Matrix in SISO Model Predictive Control

Linear switched reluctance motor (LSRM) has the advantages of simple structure, strong robustness, high efficiency, low cost, and low maintenance. They are used in many fields. This paper mainly studies the position tracking control of linear switched reluctance motor and its model predictive controller design. First, the physical system of the linear switched reluctance motor is analyzed and a discrete equation of its state space model is constructed. In order to better eliminate the static error of the motor during position tracking, the increment form of the state equation of the linear switched reluctance motor is further given. By constructing the prediction equation of the motor and predicting the system output in a given time domain. The problem of solving the optimal control sequence online at each sampling time is further transformed into a standard quadratic programming problem. So the optimal control sequence with state constraints and control quantity constraints can be further obtained. To further optimize the design of the model predictive controller, the effects are compared among different prediction horizon, control horizon, weighting factors, and sampling time on the control performance of the model predictive controller to achieve the optimal control effect. Finally, simulation results prove that the model predictive controller has a good control effect on the position tracking of the linear switched reluctance motor.

Optimizing Model Predictive Control horizons using Genetic Algorithm for Motion Cueing Algorithm

Because nonlinear model predictive control (NMPC) can be applied to deal with system constraints explicitly and to cope with paths with curvature mutations, it has become increasingly widely used in vehicle path tracking. But up to now, the influence of horizon parameters on path tracking is not very clear. To this end, we built a controller and completed some tests using the co-simulation by MATLAB/Simulink and Carsim. According to the result, it can be known that setting the control horizon is short and the prediction horizon is similar to the control horizon or setting the control horizon is short and the prediction horizon is too long will cause the accuracy of the NMPC-based vehicle path tracking to deteriorate. As for real-time, the control horizon has a more significant impact than the prediction horizon. If the control horizon is shorter, the real-time performance is better. Further inferring, setting a shorter control horizon first, and then selecting an appropriate prediction horizon, is an appropriate way to set the horizon parameters.

Optimistic optimization for model predictive control of max-plus linear systems

Despite recent advances in computing hardware and optimization algorithms, solving model predictive control (MPC) problems in real time still poses some technical challenges when long prediction and control horizons are used, due to the presence of several optimization variables and constraints. In this paper, we propose to reduce the computational burden by shortening the prediction and control horizon to a single step while preserving good closed-loop performance. This is achieved by using machine learning techniques to construct a tailored quadratic and convex terminal cost that approximates the cost-to-go function of constrained linear (possibly parameter-dependent) MPC formulations. The potentials of the proposed MPC with Learned Terminal Cost (LTC-MPC) approach is demonstrated in two numerical examples.

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A detailed dynamic model for the poly(ethylene terephthalate) (PET) solid-state polymerization (SSP) process is employed for the design of an advanced control scheme based on model predictive control (MPC). The original model of the reactor, consisting of a system of 16 time-dependent partial differential equations in two spatial coordinates, is simplified by contracting the radial direction. Such a reduced model is then incorporated into an MPC methodology. The overall control scheme is simulated to verify its reliability by maintaining the reactor temperature and the final intrinsic viscosity (IV) of the outlet PET resin constant. The validation of the resulting control loop composed of the MPC controller and the SSP unit is performed through the use of a detailed model that substitutes for the real plant. Control parameters such as the prediction and control horizons and the sampling time are tested and optimized. Control performances in response to unmeasured disturbance and set-point variations are i...

A model predictive control (MPC) strategy is developed for the diesel engine air path. The objective is to regulate the intake manifold pressure (MAP) and exhaust gas recirculation rate (EGR rate) to the specified set-points by coordinated control of the Variable Geometry Turbine (VGT), and EGR valve. The approach taken enforces a decay in a flexible Lyapunov function so that a computationally simple MPC controller can be constructed using a single-step prediction and control horizon. A rate-based framework is also utilized to achieve zero-steady state tracking in the presence of model and plant mismatch. Closed-loop simulation results are reported.

<div class="section abstract"><div class="htmlview paragraph">This paper presents a comparative study between many control techniques to investigate the efficiency of the path tracking in various driving scenarios. In this study the Model predictive control (MPC), the adaptive model predictive control (AMPC) and the Stanley controller are employed to ensure that the vehicle follows reference paths accurately and robustly under varying environmental and vehicular conditions. Two driving scenarios are utilized S-road and Curved-road with MATLAB/Simulink under three different vehicle speeds to investigate vehicle performance employing the root mean square error (RMSE) as the performance evaluation function. Particle swarm optimization (PSO) utilized for optimizing the six parameters of the MPC prediction horizon (P), Control horizon(m), manipulated variable rates, manipulated variables weights and two output variables weights. Four objective functions are employed with PSO and compared to each other in terms of the time domain regarding the RMSE of the lateral position and yaw angle to produce an enhanced performance characteristic for S-road. The comparison results shows that the Adaptive-MPC gives reduced RMSE value of yaw angle error compared to MPC at three different speeds for S-Road and Curved-Road. Stanley controller gives reduced RMSE value of lateral position error compared to MPC and Adaptive-MPC at three various speeds for S-Road and Curved-Road. Also, the results of MPC optimized with PSO gives better results compared to MPC for the all four objective functions.</div></div>

The control of high-speed AC/DC-converters is still a very demanding task. This paper presents an online optimizing nonlinear model predictive control (MPC) strategy, which allows to reach long prediction horizons in the range of 100 times the sampling time. The MPC is complemented by estimation strategies for the grid phase voltages and the load current. To achieve a short calculation time of the MPC in the range of a few $\mu$s, the MPC is tailored to the efficient implementation on an FPGA. This is achieved by using the parallel computation capabilities of FPGAs and pipelining methods, and by avoiding time-and resource-demanding operations like trigonometric functions. Compared to other MPC methods discussed in the literature, the proposed MPC strategy allows for significantly longer prediction horizons. The performance and the robustness of the control strategy is evaluated by measurements on a hardware-in-the-loop test bench.