Tracking performance of model predictive control

Algorithms for improved fixed-time performance of Lyapunov-based economic model predictive control of nonlinear systems

In order to reduce voltage error and stator current distortion caused by dead time in the model predictive control (MPC). Based on the permanent magnet synchronous motor driver and model predictive current control (MPCC), an novel control algorithm is proposed to correct the negative influence caused by dead time to improve its steady-state performance of model predictive control. The correctness of the proposed algorithm is verified by simulation.

Model predictive control for energy-efficient optimization of radiant ceiling cooling systems

Model predictive control (MPC) is an advanced control that has found widespread use in industries, particularly in process industries like oil refining and petrochemicals. Although the basic premise behind MPC is easy to comprehend, its inner workings are still generally viewed or regarded as too advanced for actual plant operator understanding. This lack of understanding is exposed when MPC performance deteriorates sometime after commissioning, as is often the case in some commercially operated process plants. Currently operators might have difficulty over reasoning about MPC performance degradation and formulating steps to investigate the cause. A tool is described that aims to make MPC more transparent to the operators. Commonly reported causes of MPC performance degradation are discussed and ways in which the operator can recognise them when they occur are outlined. Issues that are addressed include: making the set of controlled variables to be used for a given set of manipulated variables simpler and clearer; ways to recognise when a MPC controller is performing poorly and to identify the source of performance deterioration. An aim is to determine under what instances the operator can return the MPC performance to previous levels or request for specialist support or simply switch the MPC off. A goal is to avoid the kind of often reported situation where the operator gets worried that the controller is deteriorating and ends up taking knee jerk actions that cause further problems in MPC. At the top of the maintenance tool hierarchy is the trends comparison group, where MPC reference graphical performance trends are compared with actual graphical performance trends counterpart. If any abnormality is observed in these trends, the operator is encouraged to choose an option from a list of preliminary diagnostic questions contained in a group below trends comparison group, which best describes the observed abnormality. Each abnormality is associated with a list of suspected causes. When a suspected cause is chosen from the associated list, the operator is led to the symptoms investigation window, which contains scripts detailing steps for systematic examination of each symptom, with a view to either rejecting or confirming the suspicion. Assisted in the investigation are four background information windows: the virtual plant without MPC window, the virtual plant with MPC window, the transfer function matrix window and steady state gain, relative gain array (RGA) and relative weight array (RWA) window. The windows contain information and guidance that the operator might refer to from time to time during symptom investigation. Development of the maintenance tool is still at the design stage. The key components described have been research implementing MPC on three nonlinear process models, a continuous stirred tank reactor (CSTR), an evaporator process and a fluid catalytic cracking unit (FCCU). Case studies representing different MPC degradation scenarios are simulated, followed by a systematic procedure for diagnosing, isolating and recovering from such degradation, based on assumed operator’s perspective and expert’s technical reasoning. The knowledge gained from the case studies is used to develop an outline of a vision for a data-driven model predictive maintenance tool to help the operator make sensible judgements about performance degradation, the form and direction of diagnosis and fault isolation, and possibly, the recovery procedure.

An advanced control of hybrid cooling technology for telecommunication base stations

A robust dichotomy solution-based model predictive control (DS-MPC) with a voltage observer for a three-phase grid-connected inverter (GCI) is proposed in this paper. The optimal voltage vector can be dynamically selected in the whole space vector circle plane in a very short time due to the voltage vector dichotomy solution algorithm. Therefore, the proposed DS-MPC control has not only fast dynamic performance but also good steady state performance. Meanwhile, the performance of model predictive control (MPC) usually depends on the accuracy of plant model. In weak grid case, the variation of equivalent grid impedance could impact on the performance. To improve the current control robustness, DS-MPC is combined with a grid voltage disturbance observer using the extended Kalman filter (EKF) so that the disturbance of weak grid voltages is effectively suppressed. Moreover, the grid currents are well regulated even with greatly distorted and unbalanced grid voltages. The test results verify the correctness and effectiveness of the proposed DS-MPC with EKF observer under the weak grid.

Energy consumption reduction for an ultra-low-cost artificial pancreas using an event-trigger MPC strategy

Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control

Data-driven model predictive control (MPC) has gained popularity as a proactive control strategy due to the development of the Internet of Things (IoT) and data storage technologies. To apply the data-driven MPC to residential buildings, it is necessary to consider what data to measure, how to control a target system, and which prediction models are inserted. In this paper, we investigate the effect of different datasets due to sensor and controller set selection on data-driven model and data-driven MPC performance. We confirmed that the control variable according to the controller set affects MPC performance considerably.

The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or on-board sensor information is required. A comparative study is conducted between the ANN-based method and two other velocity predictors: generalized exponentially varying and Markov-chain models. The sensitivity of the prediction precision and computational cost on tuning parameters in examined for each forecasting strategy. Validation results show that the ANN-based velocity predictor exhibits the best overall performance with respect to minimizing fuel consumption.

Wiener model identification and predictive control of a pH neutralisation process is presented. Input-output data from a nonlinear, first principles simulation model of the pH neutralisation process are used for subspace-based identification of a black-box Wiener-type model. The proposed nonlinear subspace identification method has the advantage of delivering a Wiener model in a format which is suitable for its use in a standard linear-model-based predictive control scheme. The identified Wiener model is used as the internal model in a model predictive controller (MPC) which is used to control the nonlinear white-box simulation model. To account for the unmeasurable disturbance, a nonlinear observer is proposed. The performance of the Wiener model predictive control (WMPC) is compared with that of a linear MPC, and with a more traditional feedback control, namely a PID control. Simulation results show that the WMPC outperforms the linear MPC and the PID controllers.

The design and tracking performance of model predictive controllers for complex, control oriented, physics based helicopter models is investigated. A nonlinear model of the helicopter, which also includes blade flexibility, is first linearized around specific flight conditions like level banked turns and helical turns. Model predictive controllers are then designed using the linearized helicopter models and their ability to track discontinuous maneuvering trajectories is evaluated. The performance metrics used herein are tracking quality, satisfaction of heterogeneous constraints, and computation time.

The ability to meet a controlled time of arrival during a continuous descent operation will enable environmentally friendly and fuel-efficient descent operations while simultaneously maintaining airport throughput. However, if the wind forecast used to compute the initial trajectory plan is not accurate enough, the guidance system will need to correct time deviations from the plan during the execution of the descent. Previous work proposed an onboard guidance strategy based on model predictive control, which repeatedly updates the trajectory plan in real-time from the current aircraft state and for the remainder of the descent. However, the wind conditions downstream, at altitudes not explored yet, were difficult to predict due to the lack of data. This paper shows the potential benefits of using wind observations, broadcast by nearby aircraft, to reconstruct the wind profile downstream. The wind profile in the trajectory optimization problem is modeled as a spline, control points of which are updated to fit the observations before replanning the trajectory. Results from simulations using realistic wind data show that the performance of model predictive control significantly improves when including up-to-date wind observations, in terms of time and energy errors at the metering fix and fuel consumption.

<div class="section abstract"><div class="htmlview paragraph">Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust, which allows fuel economy (FE) improvements in hybrid electric vehicles (HEVs) through optimal energy management strategies (EMS). Velocity predictions generated between 5 and 30 s predictions could be implemented using model predictive control (MPC), but the performance of MPC must be well understood. Also, the vulnerability of predictive optimal EMS to velocity prediction accuracy should be addressed. Before an optimal EMS can be implemented, its overall performance must be evaluated and benchmarked against relevant velocity prediction metrics. A real-world highway drive cycle (DC) in the high-fidelity, controls-oriented 2017 Toyota Prius Prime model operating in charge-sustaining mode was utilized to observe FE realization. We propose three important metrics for comparison to no velocity prediction control: (1) perfect full DC prediction using dynamic programming (DP), (2) 10 s prediction horizon MPC, (3) 10 s constant velocity prediction. The very first optimal EMS requires the whole DC prediction in advance, whereas the rest of the two strategies only require limited horizon velocity prediction. These different velocity predictions are input into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. Our results show that the constant velocity prediction algorithm outperformed the baseline control strategy, but underperformed the MPC strategy. We also show that using a 10 s prediction window, MPC strategy provided FE improvement results very close to the full DC prediction case. So with the advancement in perception systems, MPC could be implementable in real vehicles. Also, constant velocity prediction results are good enough that optimization should be implemented in vehicle controllers with MPC type of framework. In future work, we seek to combine these models with velocity prediction models to demonstrate FE improvements on a physical vehicle.</div></div>

This paper reports the optimal control problem on the interior permanent magnet synchronous motor (IPMSM) systems. The control performance of the traditional model predictive control (MPC) controller is ruined due to the parameter uncertainty and mismatching. In order to solve the problem that the MPC algorithm has a large dependence on system parameters, a method which integrates MPC control method and parameter identification for IPMSM is proposed. In this method, the d-q axis inductances and rotor permanent magnet flux of IPMSM motor are identified by the Adaline neural network algorithm, and then, the identification results are applied to the predictive controller and maximum torque per ampere (MTPA) module. The experimental results show that the optimized MPC control proposed in this paper has a good steady state and robust performance.