Improved Model Predictive Control Research Articles

Hybrid Energy Storage Power Adaptive Optimization Strategy Based on Improved Model Predictive Control and Improved DBO-VMD

Hybrid Energy Storage Power Adaptive Optimization Strategy Based on Improved Model Predictive Control and Improved DBO-VMD

Improved model predictive control based on Luenberger state observer for LC‐coupling hybrid active power filter

AbstractAn improved model predictive control (MPC) with Luenberger state observer is proposed in this article for LC‐coupling hybrid active power filter (LC‐HAPF). Traditionally, the implementation of MPC for LC‐HAPF requires additional sensors to measure coupling capacitor voltage, thereby increases the system cost and reduces reliability. The existence of one‐step sampling delay in practice will also affect the final compensation performance. To relax these problems, both continuous and discrete models are deduced first. Then, the Luenberger observer is utilized based on the discrete model to mitigate the steady‐state error without additional sensors. In addition, the sampling delay compensation is provided by improving the prediction horizon. Finally, the effectiveness of the improved MPC for LC‐HAPF is verified by experiment results, indicating that the proposed method has fast dynamic response and low steady‐state error.

Improved model predictive control based on Luenberger state observer for LC‐coupling hybrid active power filter

AbstractAn improved model predictive control (MPC) with Luenberger state observer is proposed in this article for LC‐coupling hybrid active power filter (LC‐HAPF). Traditionally, the implementation of MPC for LC‐HAPF requires additional sensors to measure coupling capacitor voltage, thereby increases the system cost and reduces reliability. The existence of one‐step sampling delay in practice will also affect the final compensation performance. To relax these problems, both continuous and discrete models are deduced first. Then, the Luenberger observer is utilized based on the discrete model to mitigate the steady‐state error without additional sensors. In addition, the sampling delay compensation is provided by improving the prediction horizon. Finally, the effectiveness of the improved MPC for LC‐HAPF is verified by experiment results, indicating that the proposed method has fast dynamic response and low steady‐state error.

Gradient projection-based trajectory tracking control for automatic guided vehicle

In order to solve the problems of long operation time for solving the trajectory tracking controller and low real-time tracking. This paper proposes an improved model predictive control algorithm. By introducing the gradient projection method (GPM), the projection direction is used as the fastest descent direction of the objective function, which is used to reduce the number of iterations of the operation. Meanwhile, this paper adds constraints and relaxation factors to improve the trajectory tracking accuracy and prevent the controller operations from falling into local optimal solutions. Simulation by Simulink, the improved model predictive control algorithm reduces the average operation time by 24.74% and the single maximum operation time by 36.04%, effectively improving the real-time trajectory tracking performance. Finally, the verification experiment of trajectory tracking control is carried out by actual vehicle, proving the method’s effectiveness.

An improved model predictive control for linear induction machine drive-based split-source inverters

The Split Source Inverter (SSI) is a single-stage DC-AC converter topology widely recognized for its beneficial features compared to the Z-source inverter. These advantages include a reduced number of components, a constant input current, and minimized losses. This study aims to extensively investigate and analyze the operation of a 3 kW Linear Induction Motor (LIM) using SSI through comprehensive simulations. The suggested control approach, employing finite control-model predictive thrust control (FC-MPTC), exhibits remarkable dynamic behavior and achieves a fast transient response without necessitating modifications to the control loop. Moreover, the proposed method allows for the manipulation of multiple variables by utilizing a single cost function, eliminating the need for lookup tables. The efficacy of the proposed control method is validated through comprehensive simulations and experimental tests, which exhibit accurate reference tracking speed and superior dynamic performance in regulating the thrust and primary flux. The results also showed that the proposed method had lower undulations of 5.8% than the traditional method. The proposed approach outperforms the DTC-SVM method under varying load and speed conditions by delivering remarkable accuracy and improved control characteristics.

An improved state space model predictive control for linear systems with input disturbance

This paper presents an improved model predictive control (MPC) algorithm for linear systems with input disturbance. Based on the developed extended non-minimum state space input disturbance (ENMSS-ID) model, the input disturbance model structure is incorporated into the MPC framework and the objective function of the MPC optimization problem is improved to weigh the system output increments. This enables the algorithm simultaneously to achieve good input disturbance rejection performance for systems with known input disturbances and reduce the controllers’ sensitivity to model mismatch. An existing optimal estimation method is introduced to estimate the input disturbance, together with the proposed strategy to improve estimation convergence. Offset-free property is also proven to show the steady-state performance of the designed control scheme. Finally, two benchmark plants are studied to illustrate the effectiveness and advantages of the proposed algorithm.

Dynamic modelling and a dual vector modulated improved model predictive control with auto tuning feature of active front‐end converters for distributed energy resources

AbstractThe operational performance of grid‐connected active front‐end converters (AFEs) faces challenges arising from the intricate interplay among phase‐locked loop (PLL) non‐linearities, grid impedance, and conventional control strategies, resulting in compromised stability. This study introduces a refined approach to dynamic model predictive control (MPC) by integrating recursive least squares (RLS) for the precise estimation of physical model parameters, thereby addressing stability concerns. Unlike conventional methodologies, the proposed enhanced RLS‐based MPC approach, equipped with an auto‐tuning feature, allows for the design of controllers without a prerequisite understanding of exact external dynamics. Notably, this technique exhibits exceptional disturbance rejection capabilities. The evaluation of the cost function at each sampling interval facilitates the determination of optimal switching states based on predicted variables. Gate pulses for the switches of the AFEs are generated accordingly. Employing a simulation platform, the proposed control structure's performance across varied conditions is comprehensively assessed, encompassing alterations in grid impedance and system non‐linearities. The method adeptly integrates inherent non‐linearities within the system, showcasing exceptional robustness in diverse dynamic scenarios. To further substantiate the efficacy of the proposed control system over conventional approaches, simulation results are validated using a laboratory hardware platform equipped with Typhoon HIL and dSPACE real‐time emulators, providing tangible evidence of the proposed control system's effectiveness in real‐world hardware setups. The multifaceted approach, encompassing precise parameter estimation, predictive control, auto‐tuning, disturbance rejection, robust design, and real‐time evaluation, collectively establishes a resilient foundation for enhancing and maintaining the overall stability of the system across diverse operating scenarios.

A novel cascaded trajectory tracking control for UUVs under time-varying disturbances

In this paper, a novel cascaded trajectory tracking control strategy based on the characteristics of kinematics and dynamics is proposed for unmanned underwater vehicles (UUVs) under complex disturbances. A kinematic controller based on an improved model predictive control (MPC) is designed to solve the speed jump problem by considering system constraints. Moreover, an improved whale optimizer with elite strategy (EIWOA) is used to the rolling optimization process of MPC, ensuring that the global best solution is derived by obtaining the elite individuals in the population. Besides, a dynamic controller that consists of an adaptive disturbance observer and a fuzzy integral sliding mode control (ADO-FISMC) is proposed to obtain optimal thrust force under time-varying disturbances. The benefit of ADO lies in that it can estimate time-varying disturbances without specific information about disturbances. The fuzzy logic system is introduced to alleviate the chattering of sliding mode control. The simulation results demonstrate the great tracking performance of the cascaded trajectory tracking controller. The viability of the proposed algorithm is also confirmed by trials conducted in water pools.

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine. The latest research demonstrates that Subspace-based Improved Model Predictive Control (SIMPC) can overcome the difficulty in solving the predictive model in MPC/NMPC applications. However, applying constant design parameters cannot maintain consistent control effects in all states. Meanwhile, the designed system relies too much on sensor-measured data, and thus it is difficult to thoroughly validate the safety of the system because of its high complexity. This means that any potential hardware/software faults will endanger the engine. Therefore, this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle (PLA) and real-time relative tracking error. Thus, without introducing additional design parameters, an Adaptive Tracking Weight-based SIMPC (ATW-SIMPC) controller is designed to improve the control performance in all operating states effectively. Then, a Primary/Backup Hybrid Control (PBHC) strategy with the ATW-SIMPC controller as the primary system and the traditional speed (Nf) controller as the backup system is proposed to ensure safety. The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems, so as to avoid bump transition. The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults; otherwise, the ATW-SIMPC controller keeps acting on the engine. The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship, compared with the existing SIMPC controller, uplifts the dynamic control performance by 32% and reduces overshoots to an allowable limit, resulting in a better control effect in full state. The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults, such as sensor/onboard adaptive model faults. The approach proposed is applicable to the design of a model-based engine intelligent control system.

Optimization of frequency dynamic characteristics in microgrids: An improved MPC-VSG control

For the power imbalance caused by the load switching in microgrids (MGs), which in turn causes the frequency crossing limit problem. In this paper, we propose an improved model predictive control (MPC) based on the existing MPC-VSG control, combining adaptive inertia damping control and adaptive weight coefficient control for joint control, and adjusting the inertia damping coefficient of VSG in real-time by adaptive inertia damping control. In MPC control, the measured parameters at the current moment are used to predict the state quantities at the future moment, and then the target function is designed with the angular frequency deviation and the VSG power output as performance indicators, and the weight coefficients in the target function are updated in real-time by detecting the frequency deviation. After solving the objective function to obtain the required power compensation, the power reference value of the VSG is updated to optimize the dynamic response of the frequency. Experiment and comparison analysis with two existing methods show that the proposed method can further optimize the frequency response and smooth the output power of other power supply components in the microgrid when they are disturbed.

Research on traction guidance cooperative control of virtual track train based on path-tracking and articulation hinge forces optimisation

Virtual track train (VTT) is a new urban public transportation system that adopts rubber wheel and rail transit management mode. Its characteristics of all-wheel-driving all-axle-steering (AWDAAS) enable this multi-articulated vehicle to run along the target path (virtual track) with high precision. This paper proposes a new traction guidance cooperative control strategy aiming at path-tracking and hinge forces optimisation. Firstly, the vehicle dynamics model is established using the two-step method, which mainly includes the centre of gravity (CG) forces model between the wheel steering angles, torques and the generalised forces at every unit. Secondly, the vehicle multi-body dynamics model is built with the CG generalised forces as the control input and carries on the concrete modelling for the three-unit six-axle AWDAAS-VTT. This modelling method can satisfy the rapid expansion and reconstruction according to the different marshalling numbers and axles. Then the hinge forces are analysed and expressed quantitatively by vehicle state and control inputs. A traction guidance cooperative control strategy based on a hierarchical framework is proposed, which has mainly three parts: the cooperative distribution of path-tracking and longitudinal speed targets, the CG generalised forces calculation of every unit based on improved model predictive control (MPC) and Stanley algorithm, and the control allocation (CA) of wheel torque and angle. The control strategy is verified by Co-simulation. The results show that the proposed control strategy can achieve excellent tracking accuracy and effectively reduce hinge forces. In addition, the vehicle under cooperative control also has excellent curve passing stability and continuous curve adaptability. The control strategy has high robustness under different running speeds, curve radii and payloads. Even under the extreme condition of passing through the R20 m curve at 14 m/s, the maximum tracking error is less than 0.035 m.

Improved Model Predictive Control With Reduced DC-Link Capacitor RMS Current for Back-to-Back Converter-Fed PMSM Drives

A low root mean square (RMS) current flowing through the intermediate DC-link capacitor is always desired in the back-to-back (BTB) voltage source converters (VSCs)-fed motor drive, such that the designed capacitor lifespan can be extended or a lower DC-link size could be the alternative with improved power density. The conventional modulator-based control involves the inherent carrier-based switching operation, and hence the dominant harmonics at carrier frequency multiplier and carrier sideband are introduced into the capacitor current. Therefore, the finite-control-set model predictive control (FCS-MPC) is proposed to optimize the capacitor RMS current. The direct switching pulse generation without using the carrier can give a distinctive capacitor current spectrum and more possible current pulse cancellation between the BTB VSCs can be conveniently achieved through the cost function. Besides, the acceptable grid-current quality and the motor performance are maintained by using a tunable weighting factor. The simulated and experimental results highlight the effectiveness and benefits of the proposed method in terms of 20%-35% capacitor RMS current reduction compared to the synchronous carrier-based space vector pulse-width modulation (SVPWM) scheme and also significant improvements over the distributed predictive manner.

A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies.

Research on the Optimal Output Power of Proton‐Exchange Membrane Fuel Cell Based on Improved Model Predictive Control Strategy

To achieve optimal power output of proton‐exchange membrane fuel cell (PEMFC) under load variation and noise disturbance, this article proposes a recursive subspace model predictive control based on Laguerre function (RSMPCL) strategy. First, a fractional PEMFC identification model is established, and an optimal power setter is introduced to provide an optimal power value corresponding to the current load. Second, during the predictive process, a subspace predictor is updated in real time where a Householder transformer is applied to decrease the computation time of the frequent QR decomposition, and a variable forgetting factor is introduced to enhance the sensitivity and accuracy of predictive control. In the control process, a Laguerre function is employed to transform the problem of computing the input increment at the next step into calculating the coefficients of the input variables. This transformation reduces the computation time of the control quantities. Finally, load and disturbance tests conducted on a dSPACE semiphysical simulation platform indicate that compared with constrained subspace model predictive control, the proposed RSMPCL strategy can help the fuel cells achieve the optimal power point with control accuracy, robustness, and calculation speed.

Improved model predictive control method for a five-level inverter with coupled inductors

Improved model predictive control method for a five-level inverter with coupled inductors

An improved model predictive control method for path tracking of autonomous vehicle considering longitudinal velocity

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature.

Adaptive look-ahead model predictive control strategy of vehicular PEMFC thermal management

This study presents an improved model predictive control (MPC) strategy for the thermal management of vehicular fuel cells, with the incorporation of model adaptation, the utilization of look-ahead information, and temperature trajectory planning. To describe the thermal dynamics of the fuel cell cooling system, a three-volume model of a 60 kW fuel cell system is proposed and validated with dynamic load experiments. Additionally, a fuel cell temperature sensitivity experiment is conducted to identify a comfort temperature reference. The predictive models developed to cover the entire range of power levels are based on the integration of look-ahead power information and weigh adjustment of actuators. Results from the analysis of look-ahead times suggest that a 4-s preview is suitable for mitigating fuel cell dry and flooding risks. Dynamic control results demonstrate a 22%–60% reduction in temperature tracking error compared to MPC-H, MPC-M, MPC-L and MPID, while energy-saving abilities are increased by 50% relative to MPID, contributing to improve the state balance and economy for vehicular fuel cells.

Hybrid Path Planning for Unmanned Surface Vehicles in Inland Rivers Based on Collision Avoidance Regulations.

In recent years, with the continuous advancement of the construction of the Yangtze River's intelligent waterway system, unmanned surface vehicles have been increasingly used in the river's inland waterways. This article proposes a hybrid path planning method that combines an improved A* algorithm with an improved model predictive control algorithm for the autonomous navigation of the "Jinghai-I" unmanned surface vehicle in inland rivers. To ensure global optimization, the heuristic function was refined in the A* algorithm. Additionally, constraints such as channel boundaries and courses were added to the cost function of A* and the planned path was smoothed to meet the collision avoidance regulations for inland rivers. The model predictive control algorithm incorporated a new path-deviation cost while imposing a cost constraint on the yaw angle, significantly minimizing the path-tracking error. Furthermore, the improved model predictive control algorithm took into account the requirements of rules in the cost function and adopted different collision avoidance parameters for different encounter scenarios, improving the rationality of local path planning. Finally, the proposed algorithm's effectiveness was verified through simulation experiments that closely approximated real-world navigation conditions.

An improved model predictive control of back-to-back three-level NPC converters with virtual space vectors for high power PMSG-based wind energy conversion systems

In this study, an improved virtual space vector (VSV)-based two-stage model predictive control (MPC) scheme is presented for neutral point clamped (NPC) converters in high power-rated permanent magnet synchronous generator (PMSG)-based wind energy conversion system applications. The presented MPC scheme utilizes the VSVs and involves two-stage prediction for effectively reducing computation complexity. In stage-I prediction, a virtual space vector is identified based on the optimum cost function. Then, a set of sub-sector vectors are placed in stage II prediction based on the capacitor voltage levels. From this, the cost function predicts the optimum switching state. At the same time, the conditional selection of small voltage vectors in stage II prediction eliminates the neutral point voltage balancing constraint. Finally, the presented method is verified by the simulation of the back-to-back NPC converter of a high-power-rated PMSG-based wind energy conversion system. Further, the adaptability of the presented scheme for maximum power point tracking, reactive power control, and dc-link voltage control is also verified by simulation.

An Improved Model Predictive Control for a Novel Flying-Capacitor Five-Level Converter With Reduced Switching Devices

An Improved Model Predictive Control for a Novel Flying-Capacitor Five-Level Converter With Reduced Switching Devices