Model Predictive Control Design Research Articles

Dynamic Event-Triggered Asynchronous MPC of Markovian Jump Systems With Disturbances.

This article investigates the model predictive control (MPC) for discrete-time Markov jump systems (MJSs). First, the asynchronization between the modes of the controller and those of the plant is studied. An asynchronous MPC controller is designed to tackle this issue. Next, to reduce the computational cost and communication burden, a version of the dynamic event-triggered mechanism (ETM) is presented. Finally, the exogenous disturbances are considered and the notion of mean-square input-to-state stability (ISS) is taken into account in the controller design. The highlight of this article is the introduction of both dynamic ETM and asynchronous control into the MPC design. The control algorithm is developed and formulated as a convex optimization problem. Moreover, the recursive feasibility and the closed-loop mean-square ISS are both studied. Finally, some simulations are given to show the effectiveness of the derived MPC method.

Economic model predictive control of integrated energy systems: A multi-time-scale framework

An integrated energy system (IES) that comprises diverse operating units typically exhibits time-scale multiplicity in its dynamics, which makes the application of predictive control schemes to such an IES challenging. In this work, a multi-time-scale framework for subsystem decomposition and the corresponding economic model predictive control (EMPC) design is proposed to explicitly address the time-scale-multiplicity and optimal control of IESs. Specifically, a stand-alone IES is considered. According to the time-scale multiplicity, the IES is decomposed into three reduced-order subsystems with slow, medium, and fast dynamics. A composite EMPC (CEMPC) designed based on the subsystems is proposed to dynamically coordinate the operations of the operating units, aiming at meeting customers’ electricity and cooling demands while reducing fuel consumption. In the design of the CEMPC, a zone tracking objective for the building temperature regulation is adopted to maintain thermal comfort, reduce operating costs, and achieve more control degrees of freedom for satisfying the electricity demands. The proposed CEMPC is composed of a short-term distributed EMPC and a long-term EMPC. The short-term distributed EMPC consists of three local EMPCs corresponding to the slow, medium and fast dynamics of the IES. The long-term EMPC optimizes the operations of the IES taking into account long-term forecasts for external conditions. These EMPCs communicate to cooperate in decision-making. Extensive simulations are carried out to evaluate the performance of the proposed CEMPC framework compared with a hierarchical real-time optimization (HRTO) framework. The simulation results show that the proposed CEMPC framework can increase the system’s overall performance by about 5% if set-point tracking is used and by about 30% if zone tracking is incorporated. The proposed CEMPC framework is also much more computationally efficient and reduces the computation time by over 65%. The proposed CEMPC framework fully leverages the capability of each operating unit to provide superior electricity load tracking and economic performance while maintaining customers’ thermal comfort. Our studies also indicate that the proposed zone tracking approach to building temperature significantly enhances the operational flexibility of IESs in satisfying customized requirements.

An embedded Hildreth-based model predictive control of an Elbow Joint Orthosis Robot

In this paper, we consider a constrained model predictive control design for an Elbow Joint Orthosis Robot. The challenge of the robotic device control is to achieve passive and resistive rehabilitation movements with hard and soft constraints. A fast quadratic programming solver based on the Hildreth method is developed to meet the short real-time implementation sampling time. The online optimization solver based on the Hildreth method is compared with two other solvers proving its superiority in terms of computation time in the simulation environment. Simulation results show successful set point tracking and constraints satisfaction. An embedded version of constrained model predictive control using the Hildreth quadratic programming solver is implemented on an Arduino Due microcontroller. The results are satisfactory, and motion tracking errors and inputs variance are minimized. Less effort, optimal performances, and absence of overshoot and control signal oscillations are considered to evaluate the different studied motions. A lower computational cost is obtained taking into account the trade-off between the control effort and the closed-loop performances.

Optimal Path Following Control With Efficient Computation for Snake Robots Subject to Multiple Constraints and Unknown Frictions

This letter proposes a real-time optimal robust path following control scheme for planar snake robots without sideslip constraints using model predictive control (MPC). One of the features is that a linear double-integrator model rather than the complex dynamic model of snake robots is used for the MPC design to improve calculation efficiency. Moreover, in addition to constraints on joint angles, velocity, and acceleration for security, constraints on the joint offset and velocity are also considered to keep the snake robot moving forward efficiently. And the multiple inequality constraints are handled by a novel constraints translator. Furthermore, to achieve robust path following control subject to unknown and varied friction forces and disturbances, two reduced-order structure-improved extended state observers are designed to avoid complex environmental modeling. Extensive comparative experiments were conducted to verify the constraints design and the effectiveness of the proposed control scheme.

Sliding mode predictive control of a five-level rectifier with only four–IGBT

PurposeFive-level rectifiers have received widespread attention because of their excellent performance in high-voltage and high-power applications. Taking a five-level rectifier with only four-IGBT for this study, a sliding mode predictive control (SMPC) algorithm is proposed to solve the problem of poor dynamic performance and poor anti-disturbance ability under the traditional model predictive control with the PI outer loop.Design/methodology/approachFirst, mathematical models under the two-phase stationary coordinate system and two-phase synchronous rotating coordinate system are established. Then, the design of the outer-loop sliding mode controller is completed by establishing the sliding mode surface and design approach rate. The design of the inner-loop model predictive controller was completed by discretizing the mathematical model equations. The modulation part uses a space vector modulation technique to generate the PWM wave.FindingsThe sliding mode predictive control strategy is compared with the control strategy with a PI outer loop and a model predictive inner loop. The proposed control strategy has a faster dynamic response and stronger anti-interference ability.Originality/valueFor the five-level rectifier, the advantages of fast dynamic influence and parameter insensitivity of sliding mode control are used in the voltage outer loop to replace the traditional PI control, and which is integrated with the model predictive control used in the current inner loop to form a novel control strategy with a faster dynamic response and stronger immunity to disturbances. This novel strategy is called sliding mode predictive control (SMC).

Enhancing topological information of the Lyapunov‐based distributed model predictive control design for large‐scale nonlinear systems

SummaryIn this paper, a distributed model predictive control architecture based on graph theory for integrated large‐scale nonlinear process systems is proposed. This architecture is first agglomerated using popular community detection techniques and then organized on account of the relative master–slave relationship according to the ample information of interactions among separate subsystems. Both sequential and iterative distributed nonlinear model predictive coordination forms are considered for the reduction of the communication and computational burden within an acceptable loss of performance. Furthermore, the control performance of the large‐scale integrated system could be improved to some extent under the architecture and the communication strategy we propose, whereby a brave exploration is made on the relationship between the control structure and the control performance, ulteriorly obtaining some notable results towards the untapped territory. The effectiveness of the proposed coordination method is evaluated by a standard reactor‐separator process system.

Improved constrained min-max optimization for MPC tracking control in industrial networked process systems

Based on a modified min-max optimization strategy, an improved design of model predictive tracking control (MPC) is proposed to guarantee the performance of industrial process control systems when the networked system suffers from communication faults. Packet losses and uncertainties exist in the networked control system, which may deteriorate the performance of the systems and even cause a safety accident. To handle with this problem, an extended state space model is utilized in the proposed MPC design for enhancing control performance firstly. Then an improved min-max optimization approach is adopted for obtaining the optimal solution of the corresponding MPC method, where two separate optimization steps are proposed to enhance the control system operation. Different from MPC strategies that employ traditional state space models, the state variables can be tuned additionally for the modified MPC design because the tracking error information and the state variables are united in the improved model. As a consequence, extra degree of freedom is acquired for the relevant MPC scheme, and then better control performance is expected. Simulations on the temperature regulation process under model/plant mismatches and packet losses are simulated to evaluate the validity of the proposed MPC method finally.

Design and Assessment of a LIDAR-Based Model Predictive Wind Turbine Control

The development of the Light Detection and Ranging (LIDAR) technology has enabled wider options for wind turbine control, in particular regarding disturbance rejection. The LIDAR measurements provide a spatial, preview wind information, based on which the controller has a better chance to cope with the wind disturbance before it affects the turbine operation. In this paper, a model predictive controller for above-rated wind turbine control was developed with the use of pseudo-LIDAR wind measurements data. A predictive control algorithm was developed based on a linearised wind turbine model, in which the disturbance from the incoming wind was computed by the LIDAR simulator. The optimal control action was applied to the nonlinear turbine model. The developed controller was compared with the baseline control and a previously developed LIDAR-assisted control combining a feedback-and-feedforward design. Our simulation studies on a 5 MW nonlinear wind turbine model, under different wind conditions, demonstrated that the developed LIDAR-based predictive control achieved improved performance in the presence of small variations in the out-of-plane rotor torque and fore-aft tower acceleration, as well as a smoother generator speed regulation and satisfied pitch activity control constraints.

Neural Network Model Predictive Motion Control Applied to Automated Driving With Unknown Friction

Many innovative applications of vehicle control involve trajectory following while avoiding collisions, respecting actuator and dynamic limits, and using complex nonlinear dynamics. Additionally, these vehicle controllers must operate in the presence of difficult-to-model and uncertain dynamic forces which are often a function of the environment. To solve these problems, we present a design and experimental validation of neural network model predictive control (NNMPC), a method that uses vehicle operation data to construct a neural network model which is efficiently implemented in MPC. By learning a neural network model with a history of states and controls, NNMPC is capable of predicting vehicle dynamics in changing and complex operating conditions. We challenge NNMPC with the difficult task of automated racing near the friction limits without prior knowledge of the road-tire friction coefficient. The experimental results on an automated test vehicle demonstrate the capability of NNMPC to follow a trajectory near the limits on both high- and low-friction test courses. Furthermore, NNMPC outperforms a physics-based benchmark MPC on both the courses where the environmental latent state of road-tire friction is explicitly considered.

Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon

Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distributed stochastic model predictive control (DSMPC) design for a human-leading heavy-duty truck platoon. The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon. The driver behavior of the human-driven leader truck is learned by a stochastic inverse reinforcement learning (SIRL) approach. The proposed stochastic driver behavior model aims to learn a distribution of cost function, which represents the richness and uniqueness of human driver behaviors, with a given set of driver-specific demonstrations. The distributed formation control includes a serial DSMPC with guaranteed recursive feasibility, closed-loop chance constraint satisfaction, and string stability. Simulation studies are conducted to investigate the efficacy of the proposed design under several realistic traffic scenarios. Compared to the baseline platoon control strategy (deterministic distributed model predictive control), the proposed DSMPC achieves superior controller performance in constraint violations and spacing errors.

A combined Control by Interconnection—Model Predictive Control design for constrained Port-Hamiltonian systems

This paper proposes a Control by Interconnection design, for a class of constrained Port-Hamiltonian systems, which is based on an associated Model Predictive Control optimization problem. This associated optimization problem allows to consider both state and input constraints simultaneously. Based on the first order Karush–Kuhn–Tucker optimality condition, the primal–dual gradient method is then used to build a passive feedback controller, derived from the MPC-induced optimization problem. The resulting passive controller is coupled with the original Port-Hamiltonian system through a power-preserving interconnection, in order to guarantee both the closed-loop stability and constraints satisfaction, but not the optimality anymore. Comments on parameters tuning for the proposed control design, together with validations of the approach through simulations first on a linear LC circuit, then on a nonlinear Permanent Magnet Synchronous Motor and comparisons with a classical MPC design, are provided to discuss the effectiveness of the approach. • A controller combining CbI and MPC is proposed to tackle PHs with constraints. • The MPC principle is used to deal with both state and input constraints. • The CbI takes advantage of the PHs passivity for convergence and stability analysis. • The primal–dual gradient method significantly reduces the computational effort. • The explicit form of the controller facilitates implementation and maintenance.

Dynamic horizon selection methodology for model predictive control in buildings

The interest in model predictive control (MPC) for buildings has grown in recent years due to the widespread implementation of dynamic electricity tariffs, energy flexibility and distributed energy resources. The MPC applied on buildings is a computational-based methodology used to optimize the performance of heating, ventilation and air conditioning systems (HVAC) by predicting the energy behavior and minimizing a specific cost function in a determined forecasting horizon. The forecasting horizon is one of the critical parameters in MPC design applied in buildings; it should be long enough to activate the buildings’ flexibility potential, but the computational resources grow exponentially with the horizon increase, which could difficult the real-time operation. Furthermore, long periods of non-occupancy, holidays or abrupt comfort-bound changes can significantly affect the optimal forecasting time horizon length. Unfortunately, very few studies have focused on ascertaining this key optimization process aspect. The contribution of this research paper is to demonstrate, through an innovative methodology, that the optimal horizon length can be dynamically updated according to the effects of building inertia. This methodology is validated by assessing the reduction of the economic costs of a space heating system based on a synthetic representation of an experimental building placed in Germany.

Koopman operator based model predictive control for trajectory tracking of an omnidirectional mobile manipulator

Omnidirectional mobile manipulators (OMMs) have been widely used due to their high mobility and operating flexibility. However, since OMMs are complex nonlinear systems with uncertainties, the dynamic modeling and control are always challenging problems. Koopman operator theory provides a data-driven modeling method to construct explicit linear dynamic models for the original nonlinear systems, using only input-output data. It then allows to design control system based on well-established model-based linear control methods. This paper designs a Koopman operator based model predictive control (MPC) scheme for trajectory tracking control of an OMM. Firstly, using Koopman operator and extended dynamic mode decomposition method, an approximate high-dimensional linear dynamic explicit expression for the OMM system is obtained. Then MPC is employed to achieve tracking control based on the derived linear Koopman model. Finally, to show modeling accuracy for the OMM, the Koopman model is evaluated via both simulation and experimental tests. The control performances of the Koopman operator based MPC design are also verified in the simulation and experimental results.

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

AbstractCement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Gain scheduling model predictive controller design for tankless gas water heaters with time-varying delay

• Thermal model with time-varying delay is developed for a tankless gas water heater. • Feedback, feedforward and model predictive controllers are designed. • Controllers are assessed for cold start and water flow rate sudden disturbances. • Impact of temperature overshoots and undershoots are diminished. • Gain scheduling MPC presents superior performance than classic controllers. One of the most significant drawbacks of tankless gas water heaters is the difficulty of controlling the outlet hot water temperature as changes in water demand cannot be predicted. These sudden changes in hot water flow rate, are associated with the system inhered delays, and are responsible for temperature overshoots and undershoots that severely affect the comfort perception of the user. In the present work, a linear model predictive controller (MPC) design is employed for the stabilization of a constrained nonlinear thermal process with time-varying delay, however, control performance degrades significantly when operating far from the linearization operating point. The idea is to design multiple linear MPC controllers, each with its linear state-space model and constant time delay, describing process dynamics at a specific level of operation. Gain scheduling MPC presents improved performance when compared with classic PID and combined feedforward-feedback controllers, reducing the settling time up to two thirds.

Model Öngörülü Kontrol ile DA Motor Konum Kontrolü

Bu çalışmada sabit mıknatıslı fırçalı doğru akım (DA) motor sisteminin konum kontrolü için modern kontrol yöntemlerinden model öngörülü kontrolcü tasarımı gerçekleştirilmiş ve önerilen kontrolcü tasarımı farklı testler ile gerçek zamanlı olarak doğrulanmıştır. Ele alınan sistem modeli, sistem tanımlama yöntemlerinden kara kutu modeli yaklaşımı ile elde edilmiştir. Deneysel veri setleri ile elde edilen sistem modeli üzerinden model öngörülü kontrolcü tasarımı geliştirilmiştir. Geliştirilen tasarımcı modeli, Matlab destekli Waijung blok seti ve STM32F4 uygulama geliştirme kiti kullanılarak gerçek zamanlı olarak gerçekleştirilmiştir. Farklı referans girişleri ve parametre değişimleri altında kontrolcü performansı test edilmiştir. Sistem çıkışının tüm referans girişleri başarılı bir şekilde takip etmesi ve değişen sistem parametrelerine rağmen başarısını sürdürmesi önerilen kontrolcü tasarımını doğrulamıştır. Ayrıca klasik kontrol yaklaşımlarından PID denetleyici ile sonuçlar karşılaştırılarak model öngörülü kontrolcü üstünlüğü deneysel sonuçlarla vurgulanmıştır.

Congestion-mitigating MPC design for adaptive cruise control based on Newell’s car following model: History outperforms prediction

Currently, model predictive control (MPC) for adaptive cruise control (ACC) systems relies on the prediction of the leader’s motion to plan the follower’s trajectory. However, such predictions must be accurate to guarantee string stability, which represents an ongoing challenge for machine learning approaches. This issue can be circumvented by simply incorporating the leader’s history, which follows from Newell’s car-following (CF) model where a trajectory under congestion corresponds to a temporal–spatial shift of the leader’s past trajectories. By leveraging this insight, this paper develops a family of MPC models based on Newell’s CF model, labeled Newell MPCs, which are safe and can reduce traffic congestion.Specifically, We first present baseline Newell MPCs to replicate the original Newell’s CF model, including the Xbound-Model, which uses the shifted leader trajectory as an upper envelope; and the Xref-Model, which adopts the shifted leader trajectory as a reference to avoid the issue of infeasible solution triggered by hard constraints. To further improve the control performance, we propose the XV-Model which uses the leader speed history as an additional reference to enhance the model robustness and regulate speed over/under-shootings. In addition, we extend the single-leader Newell’s model through incorporating multiple leaders and propose the Xmul-Model, which can achieve driver anticipation, and correspondingly reduce reaction time and improve string stability. Finally, based on the XV-Model, we present two additional extensions: (i) the XVrelax-Model, which incorporates driver relaxation to achieve smooth response to merging traffic; and (ii) the XVss-Model, which achieves strict string stability to further dampen traffic oscillations. The proposed Newell MPCs are tested using both numerical simulations and field studies on a stock 2019 Honda Civic using Openpilot and Comma.ai; the source code is available at https://github.com/HaoZhouGT/openpilot.

Model predictive control of a second‐order hyperbolic transport‐reaction process

The model predictive controller (MPC) design is developed for a tubular chemical reactor, considering a second-order hyperbolic partial differential equation as the model of the transport-reaction process with boundary actuation. Without loss of generality, closed–closed boundary conditions and relaxed total flux are assumed. At the same time, the model is discretized in time by the Cayley–Tustin method, and, under the assumption that only the reactor's output is measurable, the observer design for the state reconstruction is addressed and integrated with the MPC design. The Luenberger observer gain is obtained by solving the operator Ricatti equation in the discrete-time setting, while the MPC accounts for constrained and optimal control. The simulations show that the output-based MPC design stabilizes the system under the input and output constraints satisfaction. In addition, to address the models' disparities, the results for both parabolic and hyperbolic equations are presented and discussed.

A Unified Approach to Structural Analysis and Design of Model Predictive Controllers

A Unified Approach to Structural Analysis and Design of Model Predictive Controllers

Real-world implementation and cost of a cloud-based MPC retrofit for HVAC control systems in commercial buildings

Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building’s thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes.