Developed Model Predictive Control Research Articles

Development of model predictive controller in avoidance design of Unmanned Aerial Vehicle (UAV)

This research was accomplished by employing already developed mathematical model of a 6DoF UAV in free space through the motion of the 6DoF aircraft (unmanned aerial vehicle) determined by coordinate systems which allow an aircraft’s position and orientation in space to be kept tracked. The discrete method of the model developed for simulation in Simulink was realized with a suitable transfer function. Then, an artificial neural network model predictive adaptive controller that can handle the nonlinearities associated with the UAV was developed based on state space technique and the model predictive controller (MPC) utilizes a neural network model to envisage future plant (aircraft) responses to potential control signals. The developed model predictive controller network was successfully trained offline using Feed-forward Back-propagation algorithm with speed and position as inputs since the underlying objective of this work is to improve the speed and position in order to advance the safety collision distance of the UAV because these parameters are mostly considered in the avoidance maneuver performance. Also, the results reveal that the speed of the generated UAV is approximately the input speed of 75MS–1 during the time the UAV is affecting maneuvering. Significance of the result is that the proposed algorithm is capable of generating collision-free trajectory for different waypoint cases.

Data-driven model predictive control: closed-loop guarantees and experimental results

Abstract We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured data and no explicit model knowledge. Our approach relies on an implicit system parametrization from behavioral systems theory based on one measured input-output trajectory. The presented MPC schemes guarantee closed-loop stability for unknown linear time-invariant (LTI) systems, even if the data are affected by noise. Further, we extend this MPC framework to control unknown nonlinear systems by continuously updating the data-driven system representation using new measurements. The simple and intuitive applicability of our approach is demonstrated with a nonlinear four-tank system in simulation and in an experiment.

A developed model predictive control scheme for vibration attenuation of building structures

Model predictive control (MPC) is an optimal control algorithm in which the current control action is obtained by solving an optimization problem in the presence of hard and soft constraints in the finite time horizons sequentially. In most cases, neglecting the effects of the external loads in predicting the future responses of the structures lead to inaccurate control action. Therefore, it could be beneficial to consider the effects of external loads in the future within the MPC to improve its accuracy. In this paper, a developed model predictive control (DMPC) scheme is introduced. For this purpose, a forecasting seismic excitation model is formulated by two sequential autoregressive (AR) models. One of those estimates the future output of the seismic excitation and the second one enhances the estimation accuracy. Then, the efficiency of the presented approach is demonstrated by the numerical study of two benchmark buildings equipped with an active tuned mass damper (ATMD). The performance of the proposed MPC is finally compared with the conventional and ideal MPCs. The numerical outputs prove the competency and higher conformity of the proposed MPC with the ideal one almost in all of the cases. Twelve benchmark performance indices are also utilized for determining the superiority of the method. The average conformity values for all of the performance indices for the proposed method in the three- and nine-story buildings are by up to 17.75% and 9% more than the values in conventional one, respectively.

Stabilization of power output and platform motion of a floating offshore wind turbine-generator system using model predictive control based on previewed disturbances

Model predictive control of a floating offshore wind turbine-generator system, in which wave height as well as inflow wind speed is regarded as the previewed disturbances, is developed to stabilize power output and platform motion and reduce dynamic loads at mechanical and supporting components at high wind speeds. First, the internal model to predict dynamic control behaviors to previewed disturbances is identified from an aero-elastic-hydro-control coupled simulation result, in which pseudorandom binary sequence signals are added to the manipulated variables calculated in a gain-scheduling feedback controller of the generator speed to satisfy a persistently exciting condition. Second, an aero-elastic-hydro-control coupled simulation using the developed model predictive control is performed for a 5-MW floating offshore wind turbine-generator system. The identified internal model has a high prediction accuracy of the system outputs by regarding the spatial mean wind speed in the swept area of the wind turbine as a rotor effective wind speed. The simulation results under turbulent wind fields and irregular wave height variations reveal that the stabilization of the power output and platform motion and the dynamic load reduction are achieved by employing the developed model predictive control with a perfect preview of the wind speed and wave height.

A Data-Driven Model Predictive Control for Alleviating Thermal Overloads in the Presence of Possible False Data

Effective mitigation of thermal overloads is crucial in preventing power grid outages. With large-scale adoption of phasor measurement units, new and more effective mitigation opportunities have emerged. However, the possibility of false data attacks on the measured data, or the relay status information, threatens the promise of utilizing measurements for timely mitigation of thermal overloads. False data can lead to wrong estimation of the system states and the power flow model, which may eventually lead to wrong mitigations. To address these newly introduced challenges, in this article, a data-driven model-predictive control method is introduced for mitigation of the thermal overloads, which is resilient to false data injection attacks. This is achieved by constructing a data-driven power flow model from the trustworthy system measurements, which is independent from system topology and model parameters. Hence, the power flow model becomes immune to wrong system model information. To eliminate the adverse impacts of false data on the measurements, the actual system states are recovered from the historical trustworthy data if an attack is detected. Case studies demonstrate that the developed model predictive control methodology effectively identifies the optimal mitigations in a finite prediction horizon.

Feasibility Study on Model Predictive Control for an EGR-Air Path System in a Turbocharged SI Engine

Abstract The purpose of this study is to develop model predictive control (MPC), which addresses MIMO system optimization control problem under constrains while providing efficient engine calibration process, and to be applied to an intake system control in a turbocharged spark ignition engine. Extended Kalman filter, which was based on an air flow sensor and a pressure sensor, was employed to achieve robustness against actual variations and MPC plant model reduction. The control methodology was demonstrated in MILS through its application to torque and EGR cooperative control using throttle and EGR valves. The results show the control outputs are in good agreement with reference trajectories under various transient operations without large amount of calibration efforts associated with the target valve position.

Input-constrained chaos synchronization of horizontal platform systems via a model predictive controller

Horizontal platform systems are mechanical systems that can exhibit both periodic and chaotic behaviour. Various control techniques have been developed to solve their chaos synchronization. However, the avoidance of control saturation is rarely considered in existing control techniques. In this paper, a model predictive controller is developed to address the input-constrained chaos synchronization of horizontal platform systems. To meet the requirements of computational efficiency, the highly efficient symplectic pseudospectral method is taken as the core solver of the model predictive controller. Two methods to impose the terminal boundary condition are considered. And two indices, i.e., the approximate synchronization time and the synchronization-consumed energy, are proposed to evaluate the synchronization performance. Numerical simulations demonstrate that the developed model predictive controller can achieve faster synchronization while owns better robustness under various kinds of external disturbances when compared to several feedback controllers.

3D surface representation and trajectory optimization with a learning-based adaptive model predictive controller in incremental forming

Abstract In this work, a novel learning-based on-line adaptive shape predictive model is developed to represent the 3D surface of the formed shape after springback in single point incremental forming (SPIF). The model can be updated in each step to predict the forming shapes in future prediction horizons given a new potential tool path, with on-line collected historic geometrical data and their corresponding tool path in previous steps. Furthermore, this model is incorporated into a sequential coupled constrained model predictive control algorithm (MPC), to optimize the potential step-down and step-over sizes in future steps, to minimize the geometric error of the whole formed part in SPIF. Two different geometric shapes, a benchmark truncated cone (with only convex geometric feature) and a non-convex dog-bone (with varying convex and concave feature), are selected for the experimental testing of the new developed on-line adaptive model predictive control algorithm (AMPC). This paper presents the detailed data acquisition and modelling process, on-line feedback control algorithms and experimental validation. The experimental results indicated that the maximum geometric error in the concerned region for the benchmark truncated cone shape and the complex non-convex dog-bone shape can be successfully decreased from above 1.25 mm without control to below 0.75 mm with the current adaptive MPC controller, which cannot be achieved with our previous non-adaptive MPC controller. This is believed to be the first attempt to incorporate a learning-based nonlinear adaptive predictive model with a model predictive controller for tool path optimization in incremental forming. The adaptive model predictive controller (AMPC) demonstrated in this work may provide a powerful tool for geometric accuracy improvement for production of complex geometric shapes in varying forming conditions in incremental sheet forming in the future.

Tire blow-out control for direct drive electric vehicles using reconfiguration of torque distribution and vertical load

This paper proposes a composite stability control strategy for tire blowout electric vehicle with explicit consideration of vertical load redistribution, subject to multi-constraints, uncertainty and redundant actuation. To readily implement system state and actuator increment constraints, a model predictive controller is designed to enhance vehicle stability performance and original lane keeping. Meanwhile, a sliding mode control-based longitudinal velocity controller is collaboratively proposed for maintaining the original longitudinal velocity after tire blowout in the freeway, which can reduce considerably the rear-end collision accidents. After that, a constrained weighting least square-based reconfigurable torque distributor is developed to realize the tracking of the virtual resultant yaw-moment and longitudinal tire force signals generated by the above controllers, considering system physical constraints and actuator capability simultaneously. Simulations with different tire blowout scenarios are conducted on the developed dynamic simulation platform to demonstrate the effectiveness of the designed control methods; furthermore, the influence of different tire blowout on the vehicle movement behaviors is discussed. Statistical results based on the system transient performance and control effort evaluating indictors highlight the considerable superiority of the developed model predictive controller compared with the linear quadratic regulator.

Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system.

Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS. Graphical abstract ᅟ.

Computationally efficient model predictive control for a class of linear parameter‐varying systems

The use of linear parameter varying (LPV) prediction models has been proven to be an effective solution to develop model predictive control (MPC) algorithms for linear and non-linear systems. However, the computational effort is a crucial issue for LPV-MPC, which has severely limited its application especially in embedded control. Indeed, for dynamical systems of dimension commonly found in embedded applications, the time needed to form the quadratic programming (QP) problem at each time step, can be substantially higher than the average time to solve it, making the approach infeasible in many control boards. This study presents an algorithm that drastically reduces this computational complexity for a particular class of LPV systems. They show that when the input matrix is right-invertible, the rebuild phase of the QP problem can be accelerated by means of a coordinate transformation which approximates the original formulation. Then they introduce a variant of the algorithm, able to further reduce this time, at the cost of a slightly increased sub-optimality. The presented results on vehicle dynamics and electrical motor control confirm the effectiveness of the two novel methods, especially in those applications where computational load is a key indicator for success.

Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB

This research work has been carried out to investigate the application of the Model Predictive Control Toolbox contained in MATLAB in controlling a reactive distillation process used for the production of a biodiesel, the model of which was obtained from the work of Giwa et al.1. The optimum values of the model predictive control parameters were obtained by running the mfile program written for the implementation of the control simulation varying the model predictive control parameters (control horizon and prediction horizon) and recording the corresponding integral squared error (ISE). Thereafter, using the obtain optimum value of 5 and 15 for control horizon and prediction horizon respectively as well as a manipulated variable rate weight of 0.025 and an output variable rate weight of 1.10, various steps were applied to the setpoint of the controlled variable and the responses plotted. The results given by the simulations carried out by varying the model predictive control parameters (control horizon and prediction horizon) for the control of the system revealed that optimizing the control parameters is better than arbitrary choosing. Also, the simulation of the developed model predictive control system of the process showed that its performance was better than those used to control the same process using a proportional-integral-derivative (PID) controller tuned with Cohen-Coon and Ziegler-Nichols techniques. It has, thus, been discovered that the Model Predictive Control Toolbox of MATLAB can be applied successfully to control a reactive distillation process in order to obtain better performance than that obtained from a PID controller tuned with Cohen-Coon and Ziegler-Nichols methods.

A Model Predictive Controller for the Cooling System of Internal Combustion Engines

The paper presents some results of the Model Predictive Controller (MPC) methodology applied to the case of the cooling system of an Internal Combustion Engine. To this end, a small spark ignition engine, about 1.2 dm3 displacement volume, is equipped with an electric pump, which is actuated by the controller, independently of engine speed. The goal of the proposed control is to achieve a faster engine warm-up and an effective engine cooling with a much lower coolant flow rate than the one usually adopted, by bringing the cooling system to operate around the onset of nucleate boiling. The developed Model Predictive Control application makes use of a lumped-parameter model, which predicts the heat transfer both in the case of a single-phase forced convection condition and in the presence of nucleate boiling. The performance of the proposed controller is evaluated during the city driving part of the NEDC homologation cycle, which was replicated at the engine test rig. The results show that the proposed controller is robust in terms of disturbance rejection and is effective in reducing warm-up time.

Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions

Building thermal mass control has great potentials in saving energy consumption and cost. Optimal control schemes are able to utilize passive thermal mass storage to shift the cooling load from peak hours to off-peak hours to reduce energy costs. As such, this paper explores the idea of model predictive control for building thermal mass control. Specifically, this paper presents a study of developing and evaluating a multi-objective optimization based model predictive control framework for demand response oriented building thermal mass control. This multi-objective optimization framework takes both energy cost and thermal comfort into consideration simultaneously. In this study, the developed model predictive control framework has been applied in six commercial buildings at Boston, Chicago, and Miami, under typical summer weather conditions. Time-of-use electricity prices from these three locations are used to calculate the cooling and reheating energy costs. Pareto curves for optimal temperature setpoints under different thermal comfort requirements are calculated to show the trade-off between the cost saving and thermal comfort maintaining. Comparing with a typical “night setback” operation scheme, this model predictive control schemes are able to save energy costs from 20% to 60% at these three locations under different weather and energy pricing conditions. In addition, the Pareto curves also show that the energy cost saving potentials are highly dependent on the thermal comfort requirements, weather conditions, utility rate structures, and the building constructions.

Model predictive control for tendon-driven balloon actuator on simulation

Many pneumatic actuators have been developed to be lightweight with high output for decreasing the impact force. However, for pneumatic actuators it is difficult to maintain exact control because these actuators have constraints. In this study, we developed model predictive control (MPC) that can accommodate these constraints when applied to the pneumatic actuator we developed. As described here, we compared and evaluated the control performance using MPC and proportional–integral–derivative (PID) control with an anti-windup control system. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Optimal exergy control of building HVAC system

Exergy or availability is an accurate metric related to quality of energy and it is used to determine sustainability of an energy system. Exergy has been extensively used to evaluate efficiency of energy systems and energy conversion processes. An exergy model for a building is presented in this study. In this paper, exergy destruction, which indicates the loss of work potential, is formulated as a function of physical parameters of the building model and environment. To minimize exergy destruction in an Heating, Ventilation and Air-Conditioning (HVAC) system, we develop model predictive control (MPC) technique using the exergy model. Comparing to a traditional on–off controller for the building, the proposed exergy-based MPC (XMPC) reduces the exergy destruction and energy consumption up to 22% and 36%, respectively. Simulation results also indicate the advantage of XMPC over conventional energy-based MPC (EMPC). The results show that XMPC reduces exergy destruction by 4% compared to EMPC as well as saving 12% more energy.

Experimental Implementation of Optimal Control of an Improved Single-Column Chromatographic Process for the Separation of Enantiomers

This work presents experimental implementation of an improved single-column chromatographic (ISCC) separation process and its optimizing controller. A mixture of guaifenesin enantiomers has been used to evaluate the performance and integrity of (i) the ISCC process and its online monitoring system in open-loop experiments and (ii) the model predictive optimizing controller for closed-loop operation. The open-loop operation has been particularly aimed at assessing the accuracy and precision of the online monitoring system. In the closed-loop operation, the performance of the developed model predictive control (MPC) scheme has been tested for set point tracking and disturbance rejection with an objective function that reflects the process economics. The online optimal operating condition was also compared to the offline optimum condition obtained using a genetic algorithm. Results confirm that the optimizing controller is adequate to operate and maintain the ISCC process at an optimal operating point while ...

Predictive process control using a moving radiant actuator

This paper presents a predictive control framework for a class of industrial distributed-parameter processes employing moving radiant actuators. The use of a moving actuator provides additional control options, but it also requires more elaborate control strategies to ensure a desired level of quality for the target process. The proposed predictive control framework is based on a generic one-dimensional (1D) formulation that describes common characteristics of typical radiant source actuated processes. The developed control framework incorporates carefully reduced process models that capture the main features of the processes and a state/parameter estimator activated with a policy that considers the changing radiant input distribution accompanying the moving actuator. The developed model predictive control framework is then demonstrated on a 1D robotic ultraviolet paint curing process. The results show that the predictive control approach provides a better cure process performance than typical static optimization methods.

Economic model predictive control of nonlinear process systems using Lyapunov techniques

AbstractIn this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed‐loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady‐state—unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunov‐based techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed‐loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady‐state. In this operation mode, suitable Lyapunov‐based constraints are incorporated in the economic MPC design to guarantee that the closed‐loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed‐loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations. © 2011 American Institute of Chemical Engineers AIChE J, 2012

Model predictive control of continuous yeast bioreactors using cell population balance models

Continuous cultures of budding yeast are known to exhibit autonomous oscillations that adversely affect bioreactor stability and productivity. We demonstrate that this phenomenon can be modeled by coupling the population balance equation (PBE) for the cell mass distribution to the mass balance of the rate limiting substrate. An efficient and robust numerical solution procedure using orthogonal collocation on finite elements is developed to approximate the PBE model by a coupled set of nonlinear ordinary differential equations (ODEs). A controller design model is obtained by linearizing and temporally discretizing the ODEs derived from spatial discretization of the PBE model. The resulting linear state-space model is used to develop model predictive control (MPC) strategies that regulate the discretized cell number distribution by manipulating the dilution rate and the feed substrate concentration. Two choices of the controlled output vector are considered: (i) the entire discretized distribution; and (ii) a subset of the discretized distribution. The ability of the MPC controllers to stabilize steady-state and periodic solutions is evaluated via simulation. We show that superior closed-loop performance is obtained when a subset of the distribution is employed as controlled outputs.