MPC Scheme Research Articles

Model Predictive Control for water management of a two-reservoir hydroelectric power plant

In the present work, model predictive control techniques are applied to the water management of a two-reservoir hydropower plant. The availability of forecasts on water requests by the hydroelectric plant was assumed and this motivated the adoption of a model predictive control approach. Ad hoc model mismatch compensation strategies has been developed which takes into account for unknown and unmeasured water flows and model uncertainties. The MPC strategy has been designed based on first principles linear model equations and on assigned control specifications. MPC parameter tuning is suitably adapted to the occurrence of specific state conditions, thus enabling optimal management of conditions of possible reservoir overflow, limiting water wastage while ensuring prompt reaction to possible variation in water demand related to the hydropower production plan. The reliability of the proposed approach has been tested through tailored simulation experiments, taking into account different significant scenarios.

Disturbance feedback-based model predictive control in uncertain dynamic environments

This paper presents a robust MPC scheme for linear systems subject to time-varying, uncertain constraints that arise from uncertain environments. The predicted input sequence is parameterized over future environment states to guarantee constraint satisfaction despite an imprecise environment prediction and unknown evolution of the future constraints. We provide theoretical guarantees for recursive feasibility and asymptotic convergence. Finally a brief simulation example showcases our results.

Data-Driven Model Predictive Control for Uncalibrated Visual Servoing

This paper addresses the image-based visual servoing (IBVS) control problem with an uncalibrated camera, unknown dynamics, and constraints. A novel data-driven uncalibrated IBVS (UIBVS) strategy is proposed, incorporated with the Koopman-based model predictive control (KMPC) algorithm and the adaptive robust Kalman filter (ARKF). First, to alleviate the need for calibration of the camera’s intrinsic and extrinsic parameters, the ARKF with an adaptive factor is utilized to estimate the image Jacobian matrix online, thereby eliminating the laborious camera calibration procedures and improving robustness against camera disturbances. Then, a data-driven MPC strategy is proposed, wherein the unknown nonlinear dynamic model is learned using the Koopman operator theory, resulting in a linear Koopman prediction model. Only input–output data are used to construct the prediction model, and hence, the proposed approach is robust against model uncertainties. Furthermore, with a symmetric quadratic cost function, the proposed approach solves the quadratic programming problem online, and visibility constraints as well as joint torque constraints are taken into account. As a result, the proposed KMPC scheme can be implemented in real time, and the UIBVS performance degradation which arises from the control torque constraints can be avoided. Simulations and comparisons for a 2-DOF robotic manipulator demonstrate the feasibility of the proposed approach. Simulation results further validate that the computation time of the proposed approach is comparable to the one of kinematic-based methods.

Model predictive control for demand response in all-electric school buildings

This paper presents predictive control strategies for all-electric school buildings in cold regions to activate energy flexibility based on changes in electricity prices. A fully electric school building near Montreal, Canada, is used as a case study. This study investigates three scenarios: 1) Reference case with a proportional–integral controller and flat rate electricity price, 2) Model predictive control with flat rate electricity price, and 3) Model predictive control with dynamic electricity price. These scenarios are modelled using the resistance-capacitance thermal networks model, and energy performance is determined and compared over a typical heating season. The proposed approach takes into account the physical parameters of the building, weather predictions, and thermal comfort constraints to maintain optimal energy consumption. A building energy flexibility index is used to quantify the building energy flexibility with a focus on peak demand reduction when the electricity prices are higher than usual. The results show that the MPC strategy can reduce peak power demand by up to 100% and minimize the cost of electricity during demand response events while maintaining acceptable comfort conditions.

Soft sensor model predictive control for azeotropic distillation of the separation of DIPE/IPA/water mixture

BackgroundA novel advanced control scheme for azeotropic distillation (AD) shows superiority in improving product quality and economic benefit. MethodsA soft sensor model predictive control (SS-MPC) strategy was developed for the separation of the diisopropyl ether (DIPE)/ isopropyl alcohol (IPA)/water mixture via AD. The soft sensor model was built based on time-delayed neural networks (TDNN), while the weight of the SS-MPC controller was optimized by multi-objective genetic algorithm. Significant findingsProduct purity of two distillation columns was controlled simultaneously by the proposed soft sensor model predictive controller, meanwhile, avoiding the measurement of composition in practice. The dynamic performance was compared with the conventional PID scheme and MPC scheme in the face of ±10 % feed disturbances. The results show that SS-MPC can better control the product purity than PID without using the composition detection instruments.

Real-time energy management strategy for fuel cell/battery vehicle based on speed prediction DP solver model predictive control

Fuel cell hybrid electric vehicles (FCHEVs) are widely considered to be an ideal substitute for the traditional vehicles. However, their high costs of operation make them uncompetitive in the current automotive market. To maximize the economy of fuel cell/battery-based hybrid electric vehicles, herein, integrating with speed prediction model, a dynamic programming solver model predictive control (DPS-MPC) strategy is applied for the hybrid vehicle energy management. The main principle of the proposed DPS-MPC is to obtain the manipulated variable that minimizes the vehicle operating cost by using the DP solver according to the information of state variables and disturbance variable. Assisted by the predictive vehicle speed, the future demand power of the vehicle is acquired by vehicle dynamic model. The demand power imports into the energy management system response prediction model for improving the control performance by considering more exact disturbance. Additionally, a comparative study is conducted to verify the improvement of economic performance by proposed strategy. The results indicate that DPS-MPC strategy can respectively reduce the operating cost by 21 % in average versus a normal MPC strategy. Moreover, the average computing time per step is 9.27 ms, less than the sampling time 1 s. In order to verify the real-time performance of the DPS-MPC strategy, a hardware in the loop test is conducted.

Tube-Based Event-Triggered Path Tracking for AUV against Disturbances and Parametric Uncertainties

In order to enhance the performance of disturbance rejection in AUV’s path tracking, this paper proposes a novel tube-based event-triggered path-tracking strategy. The proposed tracking strategy consists of a speed control law and an event-triggered tube model predictive control (tube MPC) scheme. Firstly, the speed control law using linear model predictive control (LMPC) technology is obtained to converge the nominal path-tracking deviation. Secondly, the event-triggered tube MPC scheme is used to calculate the optimal control input, which can enhance the performance of disturbance rejection. Considering the nonlinear hydrodynamic characteristics of AUV, a linear matrix inequality (LMI) is formulated to obtain tight constraints on the AUV and the feedback matrix. Moreover, to enhance real-time performance, tight constraints and the feedback matrix are all calculated offline. An event-triggering mechanism is used. When the surge speed change command does not exceed the upper bound, adaptive tight constraints are obtained. Finally, numerical simulation results show that the proposed tube-based event-triggered path-tracking strategy can enhance the performance of disturbance rejection and ensure good real-time performance.

Simplified design of practically stable MPC schemes

Simplified design of practically stable MPC schemes

Distributed and Localized Model Predictive Control—Part II: Theoretical Guarantees

Engineered cyberphysical systems are growing increasingly large and complex. These systems require scalable controllers that robustly satisfy state and input constraints in the presence of additive noise – such controllers should also be accompanied by theoretical guarantees on feasibility and stability. In our companion paper, we introduced Distributed and Localized Model Predictive Control (DLMPC) for large-scale linear systems; DLMPC is a scalable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop</i> MPC scheme in which subsystems need only exchange local information in order to synthesize and implement local controllers. In this paper, we provide recursive feasibility and asymptotic stability guarantees for DLMPC. We leverage the System Level Synthesis framework to express the maximal positive robust invariant set for the closed-loop system and its corresponding Lyapunov function in terms of the closed-loop system responses. We use the invariant set as the terminal set for DLMPC, and show that this guarantees feasibility with minimal conservatism. We use the Lyapunov function as the terminal cost, and show that this guarantees stability. We provide fully distributed and localized algorithms to compute the terminal set offline, and also provide necessary additions to the online DLMPC algorithm to accommodate coupled terminal constraint and cost. In all algorithms, only local information exchange is necessary, and computational complexity is independent of the global system size – we demonstrate this analytically and experimentally. This is the first distributed MPC approach that provides minimally conservative yet fully distributed guarantees for recursive feasibility and asymptotic stability in the nominal and robust settings.

Model Predictive Control With Wind Preview for Aircraft Forced Landing

Autonomous emergency landing capability of fixed-wing aircraft is essential for opening airspace for civil unmanned aviation. This paper proposes a goal-oriented control scheme to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exploit</i> wind information for the benefit of forced landing. Different from general disturbances in a classic control system, a favorable wind would help aircraft to glide to a selected landing site more easily so increase the level of safety, while an adverse wind may render a selected landing site infeasible. We formulate the forced landing problem with wind preview information in the framework of Economic Model Predictive Control (EMPC) which aims to maximize the aircraft's final altitude when reaching a target region. A double-layer MPC scheme is adopted to lessen the computational burden and to increase the prediction time window for practical implementation, where a piecewise-constant disturbance preview based EMPC (DP-EMPC) maximizes the altitude at the upper-level, and a linear MPC is employed at the lower-level to track the reference signal optimized by the upper-level planner. Moreover, the effectiveness of the goal-oriented optimal control scheme is illustrated by several case studies, where an unmanned aircraft is gliding towards potential landing sites under various conditions.

Humanoid motion generation in a world of stairs

Consider the problem of generating humanoid motions in an environment consisting of horizontal patches located at different heights (world of stairs). To this end, the paper proposes an integrated scheme which combines footstep planning and gait generation. In particular, footsteps are produced by a randomized algorithm that guarantees both feasibility and quality of the plan according to a chosen criterion; whereas for 3D gait generation we devise an ad hoc extension of the Intrinsically Stable MPC scheme. In its basic form, the proposed scheme addresses the off-line case (known environments), but a sensor-based adaptation is developed for the on-line case (unknown environments) based on an anytime version of the footstep planner. In order to validate the proposed approach, we present simulations in CoppeliaSim for the HRP-4 humanoid robot navigating scenarios of different complexity, both in the on-line and off-line case.

A learning-based model predictive strategy for pH control in raceway photobioreactors with freshwater and wastewater cultivation media

This article presents a study on the pH control of raceway photobioreactors (PBRs) using a learning-based model predictive control (LBMPC) approach. The primary objective is to enhance the control performance and improve microalgae biomass production. The research builds upon existing control solutions for PBRs found in the literature by integrating the robust and adaptive capabilities of the LBMPC. Specifically, the control algorithm is modified to include a pre-established Linear Quadratic Tracking with a Feedforward controller, facilitating the formulation of the conventional LBMPC. To validate the proposed controller, implementation is performed in an existing facility at the IFAPA center, located near the University of Almería in Southern Spain. The experiment involves two parallel PBRs operating under the same meteorological conditions with different microalgae strains and media (freshwater and wastewater). The LBMPC demonstrates satisfactory results and is compared with the conventional nominal MPC strategy through an analogous experiment. The LBMPC outperforms the conventional approach, achieving up to four times superior performance in terms of the average error index. The results highlight the importance of employing robust adaptive control strategies for highly nonlinear and multi-disturbed systems like the variant biological-chemical microalgae process in PBRs since the complexity associated with adapting the system’s model can negatively impact the performance of conventional model-based controllers.

Model predictive current control with a finite set of novel voltages and modulator in permanent magnet synchronous motor drives

AbstractFinite control set model predictive control (FCS–MPC) has been deployed extensively in electric motor drives and power electronics applications. Many advantages, including fast transient response, have been reported. However, the improvement of its steady‐state performance, that is, suppression of current ripple, is one of the main problems of a conventional FCS–MPC strategy. This article presents a current control strategy based on FCS–MPC using the concept of variable voltage smoothing. In the proposed method, a finite set of novel voltage vectors, produced by the smoothing process, are introduced as candidate control input candidates, and the controller generates the optimum output voltage using a modulator integrated with the FCS–MPC scheme. Thus, the voltage candidates are generated flexibly, and a suitable voltage for acceptable steady‐state performance is determined as the control input. In addition, the smoothness in the proposed concept can be updated automatically depending on the drive situation, that is, transient, or steady‐state operation, thus avoiding degradation of dynamic performance. Simulation and experimental results obtained with a permanent magnet synchronous motor drive system demonstrate that the proposed control strategy is effective to achieve current control with fast tracking performance while suppressing current ripple.

Modulated Model Predictive Control of Modular Multilevel Converters Operating in a Wide Frequency Range

The finite control set model predictive control (FCS-MPC), which considers corresponding instantaneous power in the cost function, becomes a promising solution that allows modular multilevel converters (MMCs) to operate in a wide frequency range due to its ability to balance the internal energy. However, FCS-MPC usually suffers from a heavy computational burden. The modulated MPC, which combines modulation techniques with MPC methods, is usually employed to overcome such a challenge. In this paper, we propose an exhaustive active-set method to implement the modulated MPC scheme enabling the MMC to operate over a wide frequency range. With the proposed method, the optimal solution can be obtained by enumerating all possible active sets rather than iterating, so the worst-case computational burden (or calculation time) can be determined. Finally, a comparative study of our proposed method with conventional modulated MPC methods and FCS-MPC has been conducted to demonstrate the effectiveness of the proposed method.

Model Predictive Control when utilizing LSTM as dynamic models

The prediction model is the most important part of an MPC strategy. The accuracy of such a model influences the quality of predictions and control performance of the algorithm. In some practical cases, a model based on physical equations is not available, or is not easy to get all parameters, or its complexity could affect the real-time computation of the control signal. For this reason, the use of black-box models within a MPC framework becomes attractive, since to fit such models only input and output data are needed. Questions like: “Is it possible to use LSTM’s as predictors?” , “How to implement it?” , “What is the best way to compute derivatives?” , “Which solvers and tools are recommendable?” , “How to ensure the real-time capability?” are discussed in this work.

MPC-based trajectory tracking control of unmanned underwater tracked bulldozer considering track slipping and motion smoothing

The trajectory tracking control strategy of unmanned tracked underwater bulldozer (UUTB) can improve the efficiency of trenching and dredging. The soft sediment increases the running difficulties while UUTB is operating on the river bed. The operation of UUTB will cause track slip between the tracks and soft sediment, resulting in the heading deviation of UUTB. A MPC-based trajectory tracking controller is developed. The effect of track slip rate on the deviation of trajectory tracking control is analyzed. The MPC-based strategy considering slipping and smoothing (MPC-CSS) is proposed in this paper to improve the anti-interference ability of track slipping in the process of trajectory tracking. The simulation results show that the lateral stability of UUTB can be improved by 72.5% with the proposed MPC-CSS strategy. A reduced-scale model prototype of UUTB based on a robot operating system (ROS) is designed. The trajectory tracking control performance on different artificial roads built in the lab is verified. The experimental results indicate that the proposed MPC-CSS strategy follows the desired trajectory compared with pure pursuit control (PPC) and MPC strategy. The effectiveness of the proposed MPC-CSS strategy of reduced-scale model prototype UUTB is verified, which effectively reduces the track slipping and improves the operating stability.

Distributed MPC strategy with multiple time scales and set point tracking

Distributed MPC strategy with multiple time scales and set point tracking

Stable feedback linearization-based economic MPC scheme for thermal power plant

The major concern of modern power plants is changing from tracking control to environmental and economic issues. The economic model predictive control (EMPC) scheme, which incorporates the boiler-turbine unit's dynamic tracking and economic optimization into one online framework, can well enhance the dynamic economic performance. Considering the strong nonlinearity that existed in the boiler-turbine system, this paper presents an advanced EMPC scheme based on the input/output feedback linearization (IOFL) approach. The boiler-turbine dynamics are converted via the IOFL method to a linear form, which can be readily used to constitute the standard EMPC scheme. A dual-mode method is adopted in this paper to guarantee the stability of the IOFL EMPC strategy for the boiler-turbine system, in which the first-mode optimizes the economic objective function while preserving the states of the system within a feasible region, and the second-mode moves the system state to the optimum operating point using an auxiliary controller. The simulations under the MATLAB environment demonstrate that the application of the IOFL-based EMPC scheme enhances the economic and dynamic output performance under load demand changes in comparison with fuzzy hierarchical MPC and fuzzy economic MPC schemes.

Direct Speed Control Based on Finite Control Set Model Predictive Control With Voltage Smoother

A direct speed control strategy based on finite control set model predictive control (FCS–MPC) with a voltage smoother is presented herein. In the proposed concept, a finite set of smoothed voltage vectors characterized by an adjustable amplitude and a movable origin is introduced as voltage candidates in the FCS–MPC scheme. The controller predicts the future current and speed and outputs the optimal smoothed voltage using pulse-width modulation. Owing to this control scheme, an abrupt change in the output voltage, which causes a large current ripple, is avoided without additional computational costs. Simulation and experimental results obtained using a permanent magnet synchronous motor fed by a two-level three-phase inverter show that the proposed method effectively reduces the current ripple while achieving a high dynamic drive compared with the conventional FCS–MPC scheme.

Low-Complexity Dual-Vector Model Predictive Control for Single-Phase Nine-Level ANPC-Based Converter

This article proposes a dual-vector finite-control-set model predictive control (FCS-MPC) with reduced complexity for a novel nine-level active neutral point clamped (ANPC) converter. This topology considerably reduces the used number of power switches compared to other topologies. Only nine power switches and two flying capacitors (FCs) are used to generate nine voltage levels. The proposed MPC scheme notably reduces the computational burden by directly locating the best two vectors without the need for multiple evaluations of the cost function as in the conventional method. Using one weighting factor in the cost function, three objectives are considered, namely, current tracking, FCs voltage control, and dc-link stabilization, reducing the heavy effort of coordinating weighting factors. Mathematical analyzes were carried out to determine the optimal duration of the selected voltage vectors. While the sequence of the two voltage vectors is identified based on the total harmonic distortion (THD) definition to minimize its value. Compared with standard FCS-MPC, lower steady-state errors, lower THDs, better harmonic distribution, and shorter execution times are achieved. The proposed MPC method is validated and compared with other prior-art control methods through experimental implementation.