Model Predictive Control of Maneuvering Helicopters

In this paper, constrained model predictive control (MPC) designs for a class of structured uncertain time-delay systems have been developed, where state delays as well as a random input delay have been taken into account. By replacing the control strategy with a freer one, an improved MPC design with larger initial feasible region has been developed. Furthermore, by analyzing the obtained algorithm, some useful properties of the solutions to the MPC optimization problem have been established. With these properties, a variant of the obtained algorithm with dramatically reduced number of online optimizing variables and hence the online computational burden has also been developed. However, the control performance degrades slightly. The two algorithms haven been proved to stabilize the closed loop system in the mean square sense and to guarantee the satisfaction of constraints. Finally a numeric example is given to illustrate the proposed results.

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.

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.

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

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

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

PurposeThe purpose of this paper is to show the feasibility of constrained model predictive control (MPC) for sophisticated helicopter models which are derived by physical considerations.Design/methodology/approachPhysics‐based modeling is used to create control‐oriented helicopter models. Advanced constrained controllers are designed and tested for these sophisticated models.FindingsThe helicopter models are valid and constrained MPC shows considerable promise for robust tracking.Practical implicationsMPCs can be implemented for highly constrained helicopter flights.Originality/valueA complete process of control‐oriented, physics‐based model development for helicopters followed by MPC design is developed. It is also proved that constrained MPC can be used and implemented online to robustly track discontinuous helicopter trajectories with heterogeneous constraints, even when the models are sophisticated and physics based.

Flexible operation of coal-fired power plants is becoming increasingly necessary for successful integration of large-scale renewable power generation into the power grid. The maximum ramp rate and the number of load cycles are generally limited by the thermal stress experienced by the boiler pressure parts, turbine metallurgy and creep and fatigue of critical thick-walled components Main steam temperature is a critical operating parameter that must be controlled within acceptable limits for safe operation. Main steam temperature deviation beyond acceptable limit has impact on boiler pressure parts and turbine material of construction due to creep and fatigue effect. Base load operating units do not require steep ramp rate and hence recommended ramping rates are kept low within the safe operating zone in comparison to the flexible operation of the units with wide range load change width. Thermal stresses are caused by the temperature changes inside the thick-walled components and turbine steam admission parameters. Hence, the quality of main steam temperature control plays a vital role in flexible operation of the coal fired units. Conventional cascaded PID temperature control loop architecture performs well at steady state condition within a limited variation of load change at low ramp rate but it acts slowly and performs poorly at transient operating conditions of flexible operation of the boiler turbine with wide range load variation and load cycle with high ramp rate and remains far from rated conditions. In this paper, a Multi-Input Multi-Output (MIMO) Non-linear Model Predictive Control (MPC) design for regulation of the main steam temperature of a Once-Through supercritical Boiler is proposed. The controller is based on a non-linear dynamic model which incorporates dynamics of the variables of interest. It has the capability to operate effectively across a wide load range while maintaining main steam temperature within acceptable limits. A notable advancement in this design of MPC is the incorporation of coal flow demand and feedwater flow demand as additional control inputs alongside primary and secondary spray flows. In simulation test cases, the MPC controller demonstrates satisfactory performance and computational efficiency.

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.

In this paper, a model predictive control (MPC) design method is proposed for Hammerstein systems with multivariable nonlinearities. Iterative inversion is introduced such that classical linear MPC design can be used. The condition for which the numerical inversion is guaranteed to converge is derived. From this condition, the input space at which the nonlinear function is invertible can be obtained, which poses extra input constraints for MPC design. The overall constraints are transformed and approximated by a set of linear constraints by means of convex hull construction. Illustrative examples are presented to demonstrate the effectiveness of the proposed control method.

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.

This note proposes a model predictive control scheme for tracking a random reference and analyses its performance. In such situations it is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence.