Constrained Model Predictive Control Research Articles

Constrained Model Predictive Control for Generation Power Distribution on Aircraft Engines

Aiming at the increasing demand for electric energy in aircraft in the future, a multi-objective optimization aircraft engine constrained model predictive control method based on generation power distribution is proposed. Firstly, based on the aircraft engine component level model and the equilibrium manifold theory, the aircraft engine equilibrium manifold expansion model is established. Secondly, the influence of the power generation is modeled, and the influence of the low- and high-pressure shaft generators on the normal operation of the aircraft engine is studied and compared. The control variables such as fuel flow and total generation power are taken as the constraint conditions to design the constraint model predictive controller. Furthermore, the multi-objective grey wolf optimization algorithm is introduced to intelligently optimize the parameters of the designed controller. At last, the simulation based on the component level model shows that the high-pressure shaft generator has less influence on the state quantity, including engine thrust, than the low-pressure shaft generator. The proposed control method using the multi-objective gray wolf optimization (MOGWO) algorithm has rapid response and no steady-state error.

Predictive functional control for chamber pressure: A quantum simultaneous whale optimization approach

This study introduces a Constrained Predictive Functional Controller (CPFC) designed for regulating chamber pressure in a coke furnace. While the traditional controllers face challenges posed by the system’s complex multi-input multi-output (MIMO) structure, cross-coupling, time delays, physical constraints, and uncertainties, this article’s approach realized set-point tracking and disturbance rejection, addressing model mismatches while considering constraints on system inputs, input variations, and outputs. To tackle the constrained Model Predictive Control (MPC) optimization problem and minimize the cost function, the Quantum Simultaneous Whale Optimization Algorithm (QSWOA) is employed. This meta-heuristic optimization method integrates quantum coding and simultaneous search with the whale optimization algorithm, enhancing convergence speed and precision. Furthermore, the performance of the proposed CPFC-QSWOA controller is assessed through simulations of the chamber pressure control of a Coke furnace. Results demonstrate the superiority of the CPFC-QSWOA approach, showcasing high precision and robustness, particularly in the face of uncertainties and disturbances.

Constrained model predictive control for networked jump systems under denial‐of‐service attacks and time delays

Constrained model predictive control for networked jump systems under denial‐of‐service attacks and time delays

Model-based predictive greenhouse parameter control of aquaponic system.

The effectiveness of an aquaponic system significantly relies on the habitat provided for both the fish and plants. As an integral component of aquaponics, hydroponic cultivation benefits greatly from the controlled environment of a greenhouse. Within this environment, factors such as temperature, carbon dioxide levels, humidity, and light can be carefully adjusted to maximize plant growth and development. This precise regulation ensures an ideal growing environment, fostering the flourishing of plants and contributing to the overall success of the aquaponic ecosystem. This study presented a control approach for an aquaponic greenhouse system. It aims to keep the greenhouse climate parameters (temperature, CO2 concentration, and humidity) at their ideal levels. The proposed control strategy is a two-layered mechanism in which the first layer presents an optimization framework using particle swarm optimization (PSO) algorithm to give the setpoints for the controller, and the second layer demonstrates a constrained discrete model predictive control (CDMPC) strategy to maintain the desired trajectories received from the optimization layer. To validate the results obtained using PSO, this study incorporates genetic algorithms (GA) and assesses their performance in comparison. Given similar computational efficiency and low computational time for both algorithms, the optimal values identified by particle swarm optimization (PSO) are adopted as the setpoints. Two performance criteria, relative average deviation (RAD) and mean relative deviation (MRD), are derived to evaluate the tracking performance of the proposed CDMPC controller under external disturbances. A comparison of the proposed CDMPC with the PI controller is also offered. According to the comparison results, our proposed CDMPC performs better than the PI controller with lower RAD values (temperature, 1.1315; CO2 concentration, 0.9225; humidity, 2.547) and MRD values (temperature, 0.315; CO2 concentration, 0.25; humidity: 1.013). The controller is validated to be efficient by its strong control performance, highlighted by robustness, efficient setpoint tracking, and adequate disturbance rejection. This novel approach might prove to be a useful technique for developing environmental control strategies that can be used for potentially boosting production rates of aquaponic greenhouse systems, maximizing profitability, and reducing labor needs. By maintaining optimal conditions, it can enhance ecosystem health, improve yields, and streamline operations, paving the way for greater system performance and sustainability.

Adaptive fault-tolerant control of distributed electric propulsion aircraft based on multivariable model predictive control

Distributed Electric Propulsion (DEP) aircraft presents a highly promising technology aligned with the demands of green aviation due to its superior flight efficiency, reduced energy loss, and diminished noise. This paper firstly designs and establishes a flight dynamics model for DEP aircraft, followed by an analysis of flying qualities, thus laying the foundation for control method design. Secondly, addressing the challenge of detecting subtle control surface faults, a deep learning fault diagnosis method based on Data Dimensionality Reduction (DDR) structure is devised, achieving an accuracy rate of 99.8 %. Finally, an adaptive fault-tolerant control method for the lateral motion mode of DEP aircraft is introduced, utilizing Constrained Multivariable-multiobjective Model Predictive Control (CMMPC). Notably, for the rudder complete fault, adaptive fault-tolerant control is achieved by switching controllers and utilizing differential thrust from distributed redundant propulsion units to mitigate yaw channel anomalies, thus showcasing the advantages of DEP aircraft. Simulation results validate the theoretical soundness of the proposed method, providing a theoretical basis for the future engineering application and advancement of control systems for DEP aircraft.

Long-Horizon Constrained Model Predictive Direct Speed Control for PMSM Drives Based on Laguerre Functions

Long-Horizon Constrained Model Predictive Direct Speed Control for PMSM Drives Based on Laguerre Functions

Robust Constrained Model Predictive Voltage Control Scheme and its Classification

In this study, a prospective power system is suggested (PPS), predictive current control (PCC), enhanced voltage control (EVC), model predictive control (MPC), induction motor (IM) circuit. Instead of monitoring the torque and speed as in conventional MP DTC, the stator voltage is directly regulated in the suggested PVC model thanks to the predictive control theory. A voltage source inverter's use of a model predictive control approach. For all potential voltage vectors produced by the inverter, this method forecasting value of the load using a discrete model of the system. "Voltage-source converter based high voltage direct current (VSC-HVDC)" connected offshore wind farms using a "model predictive control (MPC)" based Enhanced Voltage Control Strategy (EVCS) (OWFs). All wind farm generator (WTGs) and wind farm side VSCs are effectively synchronized in the proposed Midi controller EVCS to maintain voltages within a practical range and reduce system power loss. Review of induction motor defects, including mechanical and insulating issues. The summary of static current signature of machine failures. Numerous useful feature extraction techniques have been introduced for induction motor problem detection.

Coordinated Operation of Virtual Coupling High-Speed Trains Under a Novel Sequential Model Predictive Control Framework

This paper deals with the coordinated control problem of the multiple high-speeds operating under the virtual coupling mode with special consideration of the coupled safety inter-train distance constraints and other independent constraints affecting train dynamics. Considering the inertial lag phenomenon within longitudinal dynamics, this study employs a third-order nonlinear control model to characterize train motion. To cope with the intricate running constraints explicitly, a centralized constrained model predictive (MPC) control problem is formulated for the regulation of the trains. Within the framework of sequential solving approach, a novel distributed model predictive control (DMPC) algorithm is designed based on a distributed train running information exchange mechanism. The algorithm decomposes the centralized MPC problem into a sequence of local problems, allowing for sequential computation and meeting the real-time control requirements. Notably, the algorithm mitigates solution conservatism arising from the distributed optimization mechanism by accounting for potential hypothetical control policies among a train’s topological neighbors when designing its control policy. The feasibility and effectiveness of the proposed results are rigorously confirmed through theoretical analysis and illustrated by numerical experiment results.

Constrained model predictive fault-tolerant control for nonlinear batch processes with time delay by integrating a LRF method and a switching strategy

To guarantee the control performance of batch processes under time delays and actuator failures, a novel iterative learning model-based predictive fault-tolerant control approach is developed. First, a two-dimensional switched model was developed by linearizing the system at multiple operating points so as to describe the actual process with and without actuator faults. Second, a switching controller with greater robustness and diminished conservativeness was developed based on this switched model. Compared to conventional fault-tolerant control methods, the switching controller has stronger fault-tolerant ability, less conservativeness, and higher energy efficiency. Third, by integrating the Lyapunov-Razumikhin function (LRF) method, sufficient conditions for system stability could be provided based on the robust positive invariant and terminal constraint sets. Solving the stability conditions could then allow us to obtain the control law gains under faulty or normal cases. Compared to the existing Lyapunov-Krasovskii function method (LKF) method, the developed stability conditions were simpler, less complex, and less conservative. Finally, the effectiveness of the proposed method is tested on a continuous stirred reactor (CSTR).

Guaranteed H∞ Performance of Switched Systems with State Delays: A Novel Low-Conservative Constrained Model Predictive Control Strategy

In this paper, for the first time, a simultaneous design of a model predictive control plan and persistent dwell-time switching signal utilizing the conventional multiple Lyapunov–Krasovskii functional is proposed for linear delayed switched systems that are affected by physical constraints and exogenous disturbances. The conventional multiple Lyapunov–Krasovskii functional with a ‘jump high’ condition is used as a step forward to reduce the strictness of constraints on controller design compared with the switched Lyapunov–Krasovskii functional. However, a dwell-time constraint is inflicted on the switching signal by the ‘jump-high’ condition. Therefore, to decrease the dwell-time limit, the persistent dwell-time structure is used and compared with other structures. Also, a new online framework is proposed to reduce the number of constraints on controller design at each time step. Moreover, for the first time, exogenous disturbances are considered in the procedure of MPC design for delayed switched systems, and non-weighted H∞ performance is ensured. The simulation outcome demonstrates the great performance of the suggested plan and its ability to asymptotically stabilize the switched system.

Robust offset-free constrained Model Predictive Control with Long Short-Term Memory Networks

Robust offset-free constrained Model Predictive Control with Long Short-Term Memory Networks

Constrained model predictive control of an industrial high-rate thickener

High-rate thickeners are used in the mining industry to improve water recovery from slurries and increase their solids ratio. High-rate thickeners operate under strict constraints and several disturbances. To control this process, a constrained model predictive control (MPC) is developed in this paper. For process identification, a historical data-driven methodology is used and a vector autoregressive with exogenous variables (VARX) model structure is considered. The model takes underflow slurry density as both a state variable and the process output, along with turbidity, bed level, rake torque, and cone pressure as additional state variables. It takes feed slurry and flocculant flow rates as manipulated inputs and considers inlet slurry density, slurry circulation flow rate, and underflow slurry flow rate as disturbances. The VARX model structural parameters (orders and delays) and coefficients are estimated using a bilevel optimization method. From the model obtained, a discrete state-space representation is derived. This latter is augmented to obtain a standard formulation without delays. The MPC is then formulated considering the process constraints. To evaluate the control performance, simulations are conducted and a baseline comparison is established using proportional integral (PI) control. Simulation results demonstrate that the proposed control method outperforms the baseline method by providing reduced settling times (−32%), minimized peak errors (−20%), and constraints handling ability. Accordingly, the proposed MPC is implemented in an industrial environment and compared to existing manual control based on an object linking and embedding (OLE) for process control (OPC) architecture. Finally, the industrial results show that the proposed control method effectively stabilizes the underflow slurry density and handles process constraints, resulting in a minimized average error (−90%) and a reduced standard deviation (−50%) compared to existing manual control.

Control Optimization of Air Supply System Based on Asymptotic Method Identification

In order to optimize the control quality of the air supply system aiming at its multi-disturbance, based on the historical operation data of the boiler air supply control system of a 300 MW unit, this paper adopts a two-stage identification method based on asymptotic theory and establishes an accurate model and simulation system of the air supply system. Based on the model established by asymptotic identification, we design a constrained Model Predictive Control with Kalman Filter state estimation and an Active Disturbance Rejection Control based on extended state disturbance observation two optimization schemes. The simulation based on field data shows that both schemes can be considered to possess comparatively higher practicability in the optimization of the air supply control system.

Constrained Model Predictive Control Based on Event Triggering for the Rare Earth Extraction Process.

Since the reagent dosage is manually adjusted according to work conditions, an event-triggered constrained model predictive control is proposed for rare earth extraction. First, the linear predictive system, based on a state space model, is established. Subsequently, the feedback correction link is fine-tuned to reduce the prediction error. Following this, an objective optimization function, incorporating input and output constraints, is introduced to calculate the appropriate reagent dosage. Finally, an event-triggering mechanism, underpinned by a designated threshold, is designed to update the controller. Simulation outcomes substantiate the efficacy of the proposed approach.

Constrained model predictive control for 3-D offshore boom cranes

Offshore boom cranes are complex nonlinear underactuated systems, whose control problems, when considering the 3-dimensional (3-D) model affected by ship-induced disturbances, are full of various challenges. In fact, it still remains an open problem to efficiently control the 3-D offshore boom cranes. Moreover, most existing works on offshore cranes concentrate upon designing the force/torque controller, whose performance degrades badly in the presence of friction. In this paper, considering the above issues, a novel model predictive control (MPC) method, which successfully considers the constraints of both input and output of the system, is proposed to achieve satisfactory control performance even under the effect of persistent ship roll and heave perturbations. Specifically, a discrete model is first obtained by some careful transformation and discretization on the unactuated dynamics equations, based on which a novel model predictive controller is constructed. To reduce the complexity of the system, the unactuated states and the accelerations of actuated states are considered as system states and control inputs respectively. After that, the requirements for the payload position accuracy and swing suppression as well as other system constraints, are taken into full account by converting them into input constraints to facilitate subsequent handling. At last, hardware experiments are implemented on a self-built testbed, with the obtained results clearly illustrating the effectiveness and robustness of the proposed method.

Backup Plan Constrained Model Predictive Control with Guaranteed Stability

This paper proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty using model predictive control (MPC). Backup plan safety is defined as the ability to complete an alternative mission when the primary mission is aborted. To include this new safety concept in control problems, we formulate a feasibility maximization problem aiming to maximize the feasibility of the primary and alternative missions. The feasibility maximization problem is based on multi-objective MPC, where each objective (cost function) is associated with a different mission and balanced by a weight vector. Furthermore, the feasibility maximization problem incorporates additional control input horizons toward the alternative missions on top of the control input horizon toward the primary mission, denoted as multihorizon inputs, to evaluate the cost for each mission. We develop the backup plan constrained MPC algorithm, which designs the weight vector that ensures asymptotic stability of the closed-loop system, and generates the optimal control input by solving the feasibility maximization problem with computational efficiency. The performance of the proposed algorithm is validated through simulations of an unmanned aerial vehicle path planning problem.

Constrained DNN-Based Robust Model Predictive Control Scheme with Adjustable Error Tube

This paper proposes a novel robust model predictive control (RMPC) scheme for constrained linear discrete-time systems with bounded disturbance. Firstly, the adjustable error tube set, which is affected by local error and error variety rate, is introduced to overcome uncertainties and disturbances. Secondly, the auxiliary control rate associated with the cost function is designed to minimize the discrepancy between the actual system and the nominal system. Finally, a constrained deep neural network (DNN) architecture with symmetry properties is developed to address the optimal control problem (OCP) within the constrained system while conducting a thorough convergence analysis. These innovations enable more flexible adjustments of state and control tube cross-sections and significantly improve optimization speed compared to the homothetic tube MPC. Moreover, the effectiveness and practicability of the proposed optimal control strategy are illustrated by two numerical simulations. In practical terms, for 2-D systems, this approach achieves a remarkable 726.23-fold improvement in optimization speed, and for 4-D problems, it demonstrates an even more impressive 7218.07-fold enhancement.

PDE‐constrained model predictive control of open‐channel systems

AbstractA PDE‐constrained model predictive control (MPC) algorithm for open‐channel systems based on the Saint‐Vevant(S‐V) equations is investigated in this paper. The S‐V equations, which precisely model the dynamics of open‐channel systems, are quasi‐linear hyperbolic partial differential equations (PDEs) without analytical solutions. Directly applying the S‐V equations to an MPC controller design becomes sophisticated. In this work, the calculus of variation is used to obtain the adjoint equations and the adjoint analysis method is utilized to deduce the gradients of the MPC optimization problem. Particularly, the physical constraints involving both the state and control variables are also considered. A gradient‐based optimization algorithm in combination with the numerical computation of Preissmann implicit scheme is proposed to solve the constrained MPC optimization problem. The control performances of the developed PDE‐constrained MPC algorithm with respect to the controlled water levels and gate openings are compared with those of the MPC controller designed for the linearized model. All the simulation tests are carried out on an aqueduct reach in Yehe Irrigation District in Hebei Province, China. The results show that the proposed PDE‐constrained MPC algorithm is a promising method in dealing with the constraints in terms of hyperbolic PDEs, control variables and state variables simultaneously.

Constrained Neural Network Model Predictive Controller Based on Archimedes Optimization Algorithm with Application to Robot Manipulators

Constrained Neural Network Model Predictive Controller Based on Archimedes Optimization Algorithm with Application to Robot Manipulators

Multi-source motion constrained model predictive control for tractor-trailer trucks with coupled dynamics

This paper proposes a constrained model predictive control (MPC) for tractor-trailer trucks with the complicated physical characteristics, where motion constraints can be considered to govern the vehicle maneuver. Firstly, a targeted theoretical derivation method is developed to establish a more accurate nonlinear model for articulated vehicles, with expressly considering the articulation coupling effects. As an essential basis, the kinematic coupling behavior is further investigated. Secondly, multi-source motion constraints are introduced in particular with the analysis of nonholonomic constraints on the underactuated trailer: (i) keeping trucks traveling in the feasible domain away from collision; (ii) maintaining the vehicle states in a stable field through the steady-state response and side-slip thresholds. Thirdly, a constrained MPC controller is constructed for the automatic tracking operation, wherein vehicle demands of position security and yaw stability will be taken into reasonable consideration. For this purpose, vehicle states information can be fully utilized in a parallel approach to optimize quadratic programming (QP) in MPC: model prediction and updated constraints. In the end, the contrasted-simulation analysis is carried out to verify the accuracy of derived model and effectiveness of the designed controller, along with the feasibility of the additive multi-source constraints.