Traditional Model Predictive Control Research Articles

Adaptive Model Predictive Control for Cruise Control of High-Speed Trains with Time-Varying Parameters

The cruise control of high-speed trains is challenging due to the presence of time-varying air resistance coefficients and control constrains. Because the resistance coefficients for high-speed trains are not accurately known and will change with the actual operating environment, the precision of high speed train model is lower. In order to ensure the safe and effective operation of the train, the operating conditions of the train must meet the safety constraints. The most traditional cruise control methods are PID control, model predictive control, and so on, in which the high-speed train model is identified offline. However, the traditional methods typically suffer from performance degradations in the presence of time-varying resistance coefficients. In this paper, an adaptive model predictive control (MPC) method is proposed for cruise control of high-speed trains with time-varying resistance coefficients. The adaptive MPC is designed by combining an adaptive updating law for estimated parameters and a multiply constrained MPC for the estimated system. It is proved theoretically that, with the proposed adaptive MPC, the high-speed trains track the desired speed with ultimately bounded tracking errors, while the estimated parameters are bounded and the relative spring displacement between the two neighboring cars is stable at the equilibrium state. Simulations results validate that proposed method is better than the traditional model predictive control.

Embedding Constrained Model Predictive Control in a Continuous-Time Dynamic Feedback

This paper introduces a continuous-time constrained control strategy, which mimics the behavior of a traditional model predictive control scheme using dynamic feedback. The approach is based on the idea that the solution of the optimal control problem can be embedded in the internal states of a dynamic control law running in parallel to the system. Using input-to-state stability arguments, it is shown that if the controller dynamics are sufficiently fast with respect to the plant dynamics, the interconnection between the two systems is asymptotically stable. Additionally, it is shown that, by augmenting the proposed scheme with an add-on unit known as an explicit reference governor, it is possible to guarantee recursive feasibility and limit the residual of the optimal control problem, regardless of any changes in the desired set point. Numerical examples featuring aerospace applications demonstrate the effectiveness of the proposed scheme.

Model Predictive Voltage Control Based on Finite Control Set With Computation Time Delay Compensation for PV Systems

For the conservativeness and computational delay of the traditional finite control set model predictive control (FCS-MPC), a multi-step model predictive voltage control method based on finite control set (FCS-MMPVC) with delay compensation method is proposed in this paper, and the proposed method is validated on a three-level three-phase voltage source PV inverter. In each control cycle, the 27 voltage vectors for inverters are evaluated on line based on predictive model to obtain the optimal and suboptimal switching state. The selected switching state is optimal in two control cycle with the proposed method. Considering that the time spent in the actual sampling and calculation process cannot be ignored, the delay compensation is added in FCS-MMPVC algorithm. The simulation and experimental results show that the proposed method has good stability and reliability. Compared with the traditional FCS-MPC algorithm, the proposed method can reduce the difference significantly between the output voltage and the reference under the unbalanced and non-linear load, and the output voltage quality of the inverter is improved.

Model Predictive Direct Power Control Based on Improved T-Type Grid-Connected Inverter

In this paper, the improved T-type three-level topology can significantly reduce the conduction loss in zero states compared with the traditional T-type three-level topology. In the direct-axis and quadrature-axis rotating coordinate system, a control method combining the model predictive algorithm and the direct power control (DPC) is proposed. This control method divides 27 kinds of switch states into two groups and decides which group of switch states is to be scrolled optimally by comparing the two capacitor voltages on the dc side. This method reduces the computational burden by reducing the number of rolling optimization under the condition of ensuring the neutral point voltage balance. Compared with the traditional multilevel model predictive control, the method eliminates the need for predicting the neutral-point voltage as well as the cost function computation, and the control method is more direct and effective. Simulation and experimental results show that the proposed model predictive DPC method based on the improved T-topology can improve system robustness, fast dynamic response, wide operating range, and stability.

A Modified Model Predictive Power Control for Grid-Connected T-Type Inverter with Reduced Computational Complexity

This study proposed a modified power strategy based on model predictive control for a grid-connected three-level T-type inverter. The controller utilizes the mathematical model to forecast the performance of the grid current, the balance of DC-bus capacitor voltages and switching frequency. The proposed method outlines a new technique to formulate a control objective. The control objective includes the absolute error of the inverter voltage reference and its possible values instead of the grid current error. By using the modified equivalent transformations in the cost function, the execution time was reduced 22% compared to the traditional model predictive control while maintaining the high dynamic performances of the power and low total harmonic distortion of the current. A comparative investigation showed that the proposed method obtains a high-performance control compared with the classical power control scheme with linear PI controllers and space vector modulation. The feasibility of the proposed method was verified by the simulation and experimental results.

Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm

To improve fuel economy and reduce online computation time and microprocessor hardware resources, a real-time implementable energy management strategy for a dual-mode power-split hybrid electric vehicle (HEV) based on an explicit model predictive control (EMPC) method is proposed in this paper. The proposed strategy includes an accurate control-oriented model and a dynamic process coordination control algorithm. The energy management optimal control problem is formulated as a multiparameter quadratic programming optimization problem, and the EMPC control laws are obtained by solving the multiparameter quadratic programming problem offline. The laws are then used online to realize real-time control. A traditional model predictive control (MPC)-based control strategy, DP-based control strategy and rule-based control strategy are considered benchmark strategies for verification of the proposed EMPC-based energy management strategy. The simulation results indicate the EMPC controller has far lower microprocessor hardware costs than the MPC controller but equivalent control performance. As the prediction horizon increases, fuel consumption remains nearly the same between the MPC-based control strategy and EMPC-based control strategy. The consumption time of the MPC-based control strategy increases significantly, while the consumption time of the EMPC-based control strategy is nearly unchanged. Compared with the benchmark algorithms, the elapsed time of the EMPC controller maximum reduced by 97.46%, and the fuel economy improved by 23.37%.

Delay-Range-Dependent Robust Constrained Model Predictive Control for Industrial Processes with Uncertainties and Unknown Disturbances

A delay-range-dependent robust constrained model predictive control is proposed for discrete-time system with uncertainties and unknown disturbances. The dynamic characteristic of the discrete-time system is established as a new extended state space model in which state variables and output tracking error are integrated and regulated independently. It is used as the design of control law of system, which cannot only guarantee the convergence and tracking performance but also offer more degrees of freedom for designed controller. Unlike the traditional robust model predictive control (RMPC), the novel, less conservative, and more simplified delay-range-dependent stable conditions are derived by linear matrix inequality (LMI) theory and some relaxed technologies, which make use of the information of the upper and lower bounds of the time-varying delay. Meanwhile, the H∞ performance index is introduced in the RMPC controller design, which can reject any unknown bounded disturbances. As a result, the design controller has better abilities of both tracking and disturbance rejection. The control results on the liquid level of tank system show that the proposed control method is effective and feasible.

Sequential Model Predictive Control of Three-Phase Direct Matrix Converter

The matrix converter (MC) is a promising converter that performs the direct AC-to-AC conversion. Model predictive control (MPC) is a simple and powerful tool for power electronic converters, including the MC. However, weighting factor design and heavy computational burden impose significant challenges for this control strategy. This paper investigates the generalized sequential MPC (SMPC) for a three-phase direct MC. In this control strategy, each control objective has an individual cost function and these cost functions are evaluated sequentially based on priority. The complex weighting factor design process is not required. Compared with the standard MPC, the computation burden is reduced because only the pre-selected switch states are evaluated in the second and subsequent sequential cost functions. In addition, the prediction model computation for the following cost functions is also reduced. Specifying the priority for control objectives can be achieved. A comparative study with traditional MPC is carried out both in simulation and an experiment. Comparable control performance to the traditional MPC is achieved. This controller is suitable for the MC because of the reduced computational burden. Simulation and experimental results verify the effectiveness of the proposed strategy.

Lyapunov-Induced Model Predictive Power Control for Grid-Tie Three-Level Neutral-Point-Clamped Inverter With Dead-Time Compensation

In this paper, a new power control strategy based on a model predictive power control for the grid-tie three-level neutral point clamped inverter is presented. A dynamical model based on the orientation of grid voltage is used to predict the performance of control variables required for the system. A cost function that includes the tracking power ability, the neutral-point voltage balancing and switching frequency reduction is used to achieve the optimal switching state. A proposed selection scheme of control input is introduced to comprehend the stability of the closed-loop system and reduce the computational cost. At each sampling time, only candidate switching state inputs that guarantee the stability condition through a control Lyapunov function is evaluated in the cost function of the MPC algorithm. Thus, the execution time is remarkably decreased by 26% in comparison with traditional model predictive control. Moreover, the dead-time effect is compensated by incorporating its influence in the proposed prediction model. Simulation and experimental results compared with the traditional model predictive control are used to confirm the effectiveness of the proposed scheme.

Tire State Stiffness Prediction for Improving Path Tracking Control During Emergency Collision Avoidance

In this study, a model predictive path tracking control method based on the prediction of tire state stiffness is proposed to improve the path tracking performance at the limit of vehicle dynamics. Considering the influence of the nonlinear properties of tire force on vehicle dynamics, a nonlinear UniTire model is established, based on which a state stiffness 3D look-up table is designed to linearize the nonlinear tire model. The tire state stiffness in the prediction horizon is predicted by the vehicle motion model using the reference path information. A new linear time-varying path tracking control model in the prediction horizon is designed based on the predicted tire state stiffness. A nonlinear model predictive controller and a traditional linear time-varying model predictive controller are also designed and compared with the proposed method to verify the effectiveness and advantage of the latter. Results clearly show an improved control performance of the proposed method compared with the traditional method under the limit condition. Moreover, the calculation speed of the proposed method is faster than that of the nonlinear model predictive control method.

Output feedback model predictive control of hydraulic systems with disturbances compensation

Enhancing the robustness of output feedback control has always been an important issue in hydraulic servo systems. In this paper, an output feedback model predictive controller (MPC) with the integration of an extended state observer (ESO) is proposed for hydraulic systems. The ESO was designed to estimate not only the unmeasured system states but also the disturbances, which will be synthesized into the design of the output prediction equation. Based on the mechanism of receding horizon and repeating optimization of MPC, the output prediction equation will be updated in real time and the future behavior of the system will be accurately predicted since the disturbances are compensated effectively. Hence, the ability of the traditional MPC to suppress disturbances will be improved evidently. The experiment results show that the proposed controller has high-performance nature and strong robustness against various model uncertainties, which verifies the effectiveness of the proposed control strategy.

A Partial Least Squares Aided Intelligent Model Predictive Control Approach

A data-driven model predictive control (MPC) that combines modified partial least squares (PLSs) and MPC is proposed in this paper. A theoretical comparison among traditional MPC, MPC in PLS framework and in modified PLS framework is presented, which demonstrates that the proposed MPC approach has high prediction precision and the ability in coping with dynamics in the process compared to MPC in traditional PLS framework. Furthermore, the proposed MPC requires no prior knowledge, and the simplicity in computation makes it possible to update the prediction model online. The model validity and intelligence of the control strategy are guaranteed by the model updating strategy to a certain degree. Steady-state performance and dynamic response of the proposed MPC is testified through a tracking control simulation of the benchmark of a continuous stirred tank heater system, which illustrates that the advantages of the proposed MPC.

Improved three-vector based dead-beat model predictive direct power control strategy for grid-connected inverters

Since only one inverter voltage vector is applied during each duty cycle, traditional model predictive direct power control (MPDPC) for grid-connected inverters (GCIs) results in serious harmonics in current and power. Moreover, a high sampling frequency is needed to ensure satisfactory steady-state performance, which is contradictory to its long execution time due to the iterative prediction calculations. To solve these problems, a novel dead-beat MPDPC strategy is proposed, using two active inverter voltage vectors and one zero inverter voltage vector during each duty cycle. Adoption of three inverter vectors ensures a constant switching frequency. Thus, smooth steady-state performance of both current and power can be obtained. Unlike the traditional three-vector based MPDPC strategy, the proposed three vectors are selected based on the power errors rather than the sector where the grid voltage vector is located, which ensures that the duration times of the selected vectors are positive all the time. Iterative calculations of the cost function in traditional predictive control are also removed, which makes the proposed strategy easy to implement on digital signal processors (DSPs) for industrial applications. Results of experiments based on a 1 kW inverter setup validate the feasibility of the proposed three-vector based dead-beat MPDPC strategy.

Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems

How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances.

An improved fuzzy model predictive control algorithm based on the force/position control structure of the five-degree of freedom redundant actuation parallel robot

In this article, two new algorithms of the redundant force branch of 6-PUS/UPU parallel robot are proposed. They are model predictive control combining with proportional, integral, and differential algorithm and fuzzy combining with model predictive control algorithm. The shortcoming of the traditional model predictive control algorithm is complex adjustment, large amount of calculation, the dynamic performance effect of the system. The proposed PID model predictive control algorithm can make the controller parameters adjustment more convenient. However, PID model predictive control algorithm can’t obtain good control performance under sudden change in situation. Combining model predictive control algorithm with fuzzy theory, fuzzy model predictive control algorithm has better anti-interference ability than PID model predictive control algorithm and can reduce predictive horizon length as possible as it can. Simulation results show that fuzzy model predictive control algorithm can effectively improve real-time performance of control system, the dynamic tracking performance and robustness than the traditional model predictive control and PID model predictive control algorithm.

Finite control set model predictive control with feedback correction for power converters

As an open-loop model predictive control algorithm, finite control set model predictive control (FCS-MPC) scheme in power converter system is based on assumption that responses of optimal control implemented on prediction model agree well with actual system. The influence of model parameter mismatches and environment disturbance on control performance of scheme is neglected. Then, based on feedback correction strategy in traditional model predictive control algorithm, we derive a finite control set model predictive control with feedback correction scheme (FCS-MPCFC) that allows us to adjust prediction model output at current instant by model prediction error at previous instant, and the closed-loop correction of prediction model output is achieved. Simulations comparison analyses on a two-level three-phase inverter with multi-type model parameter mismatches controlled by traditional and improved FCS-MPC scheme are presented. Experiments are carried out on DSP controller platform.

Disturbance-Observer-Based Model Predictive Control for Battery Energy Storage System Modular Multilevel Converters

Although the traditional model predictive control (MPC) can theoretically provide AC current and circulating current control for modular multilevel converters (MMCs) in battery energy storage grid-connected systems, it suffers from stability problems due to the power quality of the power grid and model parameter mismatches. A two discrete-time disturbance observers (DOBs)-based MPC strategy is investigated in this paper to solve this problem. The first DOB is used to improve the AC current quality and the second enhances the stability of the circulating current control. The distortion and fluctuation of grid voltage and inductance parameter variation are considered as lump disturbances in the discrete modeling of a MMC. Based on the proposed method, the output prediction is compensated by disturbance estimation to correct the AC current and circulating current errors, which eventually achieve the expected tracking performance. Moreover, the DOBs have a quite low computational cost with minimum order and optimal performance properties. Since the designed DOBs work in parallel with the MPC, the control effect is improved greatly under harmonics, 3-phase unbalance, voltage sag, inductance parameter mismatches and power reversal conditions. Simulation results confirm the validity of the proposed scheme.

Improved Model Predictive Control Strategy for Modular Multilevel Converter

For reducing switching frequency and decrease computation of the traditional model predictive control (MPC) for modular multilevel converter, an improved MPC strategy is proposed. The control of AC current, circulating current and capacitor voltages are realized synchronously by building multi-objective functions. Combining with capacitor voltage sorting method, MPC calculation is decreased. The switch frequency was lowered by introducing weighting factors. Capacitor voltage difference among the sub-modules is lessened through the allowable value of capacitor voltage deviation between sub-modules. The simulation results verify its correctness.

Composite adaptive model predictive control for DC–DC boost converters

A composite adaptive model predictive control (MPC) scheme is proposed to mitigate model mismatches due to parameter uncertainties and disturbances in controller designs for DC-DC boost converters. First, a model-based feedforward compensator based on a cascade proportional-integral (PI) MPC controller was developed as a current reference to improve the dynamic response performance. Then, an observer was employed to construct an accurate model and thereby avoid overcompensation and poor prediction due to model mismatch. Finally, a finite control set MPC was implemented to select a suitable switching state with which to realise output voltage regulation. Compared with a traditional PI MPC, the proposed controller has a rapid dynamic response, and compared with an unknown offset free MPC, the proposed controller is well suited to a simplified model. The proposed methods and benefits were validated by simulations in MATLAB/Simulink and by experiments on an NI Compact RIO rapid control prototype test bench.

Integrated Adaptive Cruise Control with Weight Coefficient Self-Tuning Strategy

This paper presents a novel multi-objective coordinated adaptive cruise control (ACC) algorithm based on a model predictive control (MPC) framework which can comprehensively address issues regarding longitudinal car-following performance, lateral stability, as well as vehicle safety. During the car-following, vehicle dynamics, illustrating the forces acting on the tire contact patches, are established. To simplify the tightly coupled dynamics system, a state-feedback based disturbance decoupling method is employed, by which longitudinal and lateral dynamics can be completely decoupled. Furthermore, the traditional MPC control with a constant weight matrix will probably not be able to solve time-varying multi-objective coordinated optimization issues, especially in transient scenarios. A weight coefficient self-tuning strategy is therefore suggested by which the weight coefficient for each sub-objective can be adjusted automatically with the change of traffic scenarios, accordingly improving the overall car-following performance. The simulations show that the control algorithm utilizing the suggested self-tuning strategy reaps significant benefits in terms of longitudinal car-following performance, while at the same time maintaining a small lateral stability error range.