scholarly journals Robust Two-Layer Model Predictive Control for Full-Bridge NPC Inverter-Based Class-D Voltage Mode Amplifier

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1346 ◽  
Author(s):  
Xinwei Wei ◽  
Hongliang Wang ◽  
Kangliang Wang ◽  
Kui Li ◽  
Minying Li ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Dynamic Performance ◽  
Tracking Error ◽  
Computational Effort ◽  
Multiple Control ◽  
Lumped Model ◽  
Voltage Mode ◽  
Class D ◽  
Lc Filter

Finite control set model predictive control (FCS-MPC) is able to handle multiple control objectives and constraints simultaneously with good dynamic performance. However, its industrial application is limited by its high dependence on system model and the huge computational effort. In this paper, a novel robust two-layer MPC (RM-MPC) with strong robustness is proposed for the full-bridge neutral-point clamped (NPC) voltage mode Class-D amplifier (CDA) aiming at this problem. The errors caused by the parameter mismatches or uncertainties of the LC filter and the load current are regarded as lumped disturbance and estimated by the designed Luenberger observer. The robust control can be achieved by compensating the estimated disturbance to the used predictive model. In order to reduce computation of the controller, a two-layer MPC is proposed for the full-bridge NPC inverter with an LC filter. The first layer is used to calculate the optimal output level which minimizes the tracking error of the output voltage. The second layer is used to determine the switching state for the purpose of capacitor voltage balancing. The experimental results show that the lumped model error is observed centrally through only one observer with low complexity. The two-layer MPC further reduced the computation without affecting the dynamic performance.

Compensatory model predictive control for post-impact trajectory tracking via active front steering and differential torque vectoring

2020 ◽  
pp. 095440702097908
Author(s):  
Mingcong Cao ◽  
Chuan Hu ◽  
Rongrong Wang ◽  
Jinxiang Wang ◽  
Nan Chen
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Error ◽  
Collision Probability ◽  
Critical Conditions ◽  
Physical Analysis ◽  
Compensatory Model ◽  
Torque Vectoring ◽  
Post Impact

This paper investigates the trajectory tracking control of independently actuated autonomous vehicles after the first impact, aiming to mitigate the secondary collision probability. An integrated predictive control strategy is proposed to mitigate the deteriorated state propagation and facilitate safety objective achievement in critical conditions after a collision. Three highlights can be concluded in this work: (1) A compensatory model predictive control (MPC) strategy is proposed to incorporate a feedforward-feedback compensation control (FCC) method. Based on the definite physical analysis, it is verified that adequate reverse steering and differential torque vectoring render more potentials and flexibility for vehicle post-impact control; (2) With compensatory portions, the deteriorated states after a collision are far beyond the traditional stability envelope. Hence it can be further manipulated in MPC by constraint transformation, rather than introducing soft constraints and decreasing the control efforts on tracking error; (3) Considering time-varying saturation on input, input rate, and slip ratio, the proposed FCC-MPC controller is developed to improve faster deviation attenuation both in lateral and yaw motions. Finally two high-fidelity simulation cases implemented on CarSim-Simulink conjoint platform have demonstrated that the proposed controller has the advanced capabilities of vehicle safety improvement and better control performance achievement after severe impacts.

Nonlinear Model Predictive Control for Automatic Train Operation with Actuator Saturation and Speed Limit

2014 ◽  
Vol 678 ◽  
pp. 377-381
Author(s):  
Long Sheng Wang ◽  
Hong Ze Xu
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
High Speed ◽  
Actuator Saturation ◽  
Tracking Error ◽  
Nonlinear Model Predictive Control ◽  
Speed Limit ◽  
Speed Tracking ◽  
Automatic Train Operation

This paper addresses a position and speed tracking problem for high-speed train automatic operation with actuator saturation and speed limit. A nonlinear model predictive control (NMPC) approach, which allows the explicit consideration of state and input constraints when formulating the problem and is shown to guarantee the stability of the closed-loop system by choosing a proper terminal cost and terminal constraints set, is proposed. In NMPC, a cost function penalizing both the train position and speed tracking error and the changes of tracking/braking forces will be minimized on-line. The effectiveness of the proposed approach is verified by numerical simulations.

scholarly journals Total Harmonic Distortion Oriented Finite Control Set Model Predictive Control for Single-Phase Inverters

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3467 ◽  
Author(s):  
Po Li ◽  
Ruiyu Li ◽  
Haifeng Feng
Keyword(s):  
Model Predictive Control ◽  
Cost Function ◽  
Predictive Control ◽  
Single Phase ◽  
Tracking Error ◽  
Harmonic Distortion ◽  
Total Harmonic Distortion ◽  
Finite Control ◽  
Control Set ◽  
The Cost

Inverters are commonly controlled to generate AC current and Total Harmonic Distortion (THD) is the core index in judging the control effect. In this paper, a THD oriented Finite Control Set Model Predictive Control (FCS MPC) scheme is proposed for the single-phase inverter, where a optimization problem is solved to obtain the switching law for realization. Different from the traditional cost function, which focuses on the instantaneous deviation of amplitude between predictive current and its reference, we redesign a cost function that is the linear combination of the current fundamental tracking error, instantaneous THD value and DC component in one fundamental cycle (for 50 Hz, it is 0.02 s). Iterative method is developed for rapid calculation of this cost function. By choosing a switching state from a FCS to minimize the cost function, a FCS MPC is finally constructed. Simulation results in Matlab/Simulink and experimental results on rapid control prototype platform show the effect of this method. Analyses illustrate that, by choosing suitable weight of the cost function, the performance of this THD oriented FCS MPC method is better than the traditional one.

Probabilistic model predictive control for extended prediction horizons

2021 ◽  
Vol 69 (9) ◽  
pp. 759-770
Author(s):  
Tim Brüdigam ◽  
Johannes Teutsch ◽  
Dirk Wollherr ◽  
Marion Leibold ◽  
Martin Buss
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Models ◽  
Computational Effort ◽  
Probabilistic Constraints ◽  
Detailed Model ◽  
Control Approach ◽  
Proposed Model ◽  
Sampling Times

Abstract Detailed prediction models with robust constraints and small sampling times in Model Predictive Control yield conservative behavior and large computational effort, especially for longer prediction horizons. Here, we extend and combine previous Model Predictive Control methods that account for prediction uncertainty and reduce computational complexity. The proposed method uses robust constraints on a detailed model for short-term predictions, while probabilistic constraints are employed on a simplified model with increased sampling time for long-term predictions. The underlying methods are introduced before presenting the proposed Model Predictive Control approach. The advantages of the proposed method are shown in a mobile robot simulation example.

scholarly journals A Simplified Model Predictive Control for T-Type Inverter with Output LC Filter

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 31 ◽  
Author(s):  
Van-Quang-Binh Ngo ◽  
Minh-Khai Nguyen ◽  
Tan-Tai Tran ◽  
Young-Cheol Lim ◽  
Joon-Ho Choi
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Performance Simulation ◽  
Control Scheme ◽  
Finite Control ◽  
Lc Filter ◽  
The Cost ◽  
And Control ◽  
Measurement And Control ◽  
Selection Of

In this paper, a model predictive control scheme for the T-type inverter with an output LC filter is presented. A simplified dynamics model is proposed to reduce the number of the measurement and control variables, resulting in a decrease in the cost and complexity of the system. Furthermore, the main contribution of the paper is the approach to evaluate the cost function. By employing the selection of sector information distribution in the reference inverter voltage and capacitor voltage balancing, the execution time of the proposed algorithm is significantly reduced by 36% compared with conventional model predictive control without too much impact on control performance. Simulation and experimental results are studied and compared with conventional finite control set model predictive control to validate the effectiveness of the proposed method.

CI Engine Model Predictive Control With Availability Destruction Minimization

2018 ◽  
Author(s):  
Muataz Abotabik ◽  
Richard T. Meyer
Keyword(s):  
Energy Source ◽  
Model Predictive Control ◽  
Fuel Consumption ◽  
Predictive Control ◽  
Alternative Fuels ◽  
Combustion Engine ◽  
Tracking Error ◽  
Specific Fuel Consumption ◽  
Nox Emissions ◽  
Compression Ignition Engine

Major interests in the automotive industry include the use of alternative fuels and reduced fuel usage to address fuel supply security concerns and regulatory requirements. The majority of previous internal combustion engine (ICE) control strategies consider only the First Law of Thermodynamics (FLT). However, FLT is not able to distinguish losses in work potential due to irreversibilities, e.g., up to 25% of fuel exergy may be lost to irreversibilities. To account for these losses, the Second Law of Thermodynamics (SLT) is applicable. The SLT is used to identify the quality of an energy source via availability since not all the energy in a particular energy source is available to produce work; therefore optimal control that includes availability may be another path toward reduced fuel use. Herein, Model Predictive Control (MPC) is developed for both FLT and SLT approaches where fuel consumption is minimized in the former and availability destruction in the latter. Additionally, both include minimization of load tracking error. The controls are evaluated in the simulation of a single cylinder naturally aspirated compression ignition engine that is fueled with either 20% biodiesel and 80% diesel blend or diesel only. Control simulations at a constant engine speed and changing load profile show that the SLT approach results in higher SLT efficiency, reduced specific fuel consumption, and decreased NOx emissions. Further, compared to use of diesel only, use of the biodiesel blend resulted in less SLT efficiency, higher specific fuel consumption, and lower NOx emissions.

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