Fixed-Time Constrained Model Predictive Sliding Mode Control of Spacecraft Simulator

In this paper, a nonlinear robust control technique is proposed for a class of affine nonlinear systems. This technique tries to increase tracking control performance by combining the benefits of sliding mode control (SMC) with model predictive control (MPC), for a class of nonlinear systems, in terms of smooth control law, a zero steady-state error, a faster convergence rate, and more robustness for the closed-loop system against external disturbances and model uncertainties. Furthermore, this paper provides a comparative study with the previously mentioned control methods. The controllers SMC and Model Predictive Sliding Mode Control (MPSMC) have been tested in simulation under Matlab/Simulink environment. Simulations are carried out on a nonlinear affine benchmark to evaluate the suggested technique and demonstrate its efficacy in achieving the required control performances.

Nonlinear model predictive control, which is conceptually similar to its linear counterpart except that nonlinear dynamic model is used for process prediction and optimization, is a developing control field. The proposed nonlinear model predictive control scheme, which is called nonlinear sliding mode model predictive control, integrates model predictive control and sliding mode variable structure control. A feasible dual-mode control scheme is presented in the paper, sliding mode predictive controller is implemented while the system state is outside the terminal region, besides sliding mode variable structure control designed off-line is used. The resulting control scheme has strong points of the two control methods. By predicting the pre-designed switching function, the predictive control sequence can be found by solving constrained open-loop optimal control problem, and the current control is implemented. At next sampling time the optimizing procedure is repeated. By constraining terminal sliding mode with an inequality, the state is steered into the predesigned sliding mode region, then the closed-loop stability is proved, and furthermore the system performance is analyzed. With some conditions, the method can be extended to a quasi-infinite horizon formulation. Simulation results show that less calculation is needed.

Conventional control laws are limited predominantly to control the rotor main passing frequency component of helicopter structural response, while the remaining frequency components still make harsh vibration. The primary objective of this paper is to develop a novel hybrid controller to control multifrequency helicopter vibrations. The architecture of the feedforward–feedback hybrid control law is first proposed based on the filtered-x least mean square algorithm. Subsequently, the feedback loop called discrete model predictive sliding mode controller is designed in detail which combines discrete sliding mode control and model predictive control. Discrete sliding mode control is employed for its robustness, while the chattering phenomenon is eliminated and then the system state is steered to reach the sliding surface precisely in an optimal manner with the assistance of model predictive control. Also, the stability, robustness, and state tracking error bound of the feedback controller are analyzed. Performance enhancement of the hybrid control law is verified by simulations based on a simplified helicopter finite element model. By comparison with the multifrequency filtered-x least mean square algorithm under various cases, the results clearly demonstrate that the proposed algorithm deals with the rotor main passing frequency component and its harmonics simultaneously with faster convergence and better stability.

Filters have become almost indispensable to counter the harmonics injection by the widely used power converters and other non-linear devices. Shunt active power filters (SAPF) controlled by direct power control (DPC) serve as a good option as they offer various advantages. The performance of the existing controllers deteriorates when the three-phase supply system deviates from its ideal situation of balanced and sinusoidal voltages. Unlike the existing methods, this work develops and employs a detailed and accurate system model based on the extended pq theory. With its proven high accuracy, the model predictive control (MPC) is used as the control algorithm for PWM generation. An in-depth comparison between MPC and other predictive methods is presented, aiming to substantiate the superior performance of MPC. Furthermore, an adaptive sliding mode control-based reactive power reference is proposed to eliminate the steady-state error in the reactive power. The proposed controller’s feasibility is rigorously evaluated through meticulous MATLAB simulations and real-world laboratory experiments. Comparative analysis against existing SAPF controllers in similar conditions undeniably establishes better grid power quality by lowering source current THD and overall reactive power consumption.

Model predictive control (MPC) as a nonlinear control method, is being studied for a wide variety of power electronics applications targeting various power ranges. The fast dynamic response, robustness under different load conditions, multi-objective control, and simplicity of implementation are the main features of MPC. However, the implementation of finite control set MPC (FCS-MPC) or direct MPC in its basic form for multilevel converters (MLCs) demonstrates the inability to generate the staircase multilevel output voltage waveform in a voltage sourced inverter (VSI) application. Adding a reference evaluation block to the basic FCS-MPC method can significantly improve its multilevel voltage generation capability. In this paper, an enhanced model predictive nearest level control (EMP-NLC) is proposed and implemented on a 5-Level flying capacitor multilevel converter (FCMC). The proposed method is simulated under different load conditions, while a comparison against existing MPC techniques is also presented. Experimental results verified the efficacy of the proposed controller.

Model predictive control (MPC) presents important advantages in the control of the power converter and drives such as, fast dynamic response and capability to include nonlinear constrains. These have positioned MPC as a powerful and realistic control strategy, however, it also has disadvantages such as variable switching frequency and parameter sensitivity. This paper applied a modulated model predictive speed control that guarantees a fix switching frequency and, thanks to disturbance compensation, robustness to parameters variation. The strategy is validated and compared to finite set model predictive speed control through simulation results.

This paper presents a novel model predictive sliding mode control (MPSMC) strategy for a three-phase grid connected AC/DC converter. The grid current is predicted for controlling the active and reactive power flows for the next sampling time instead of predicting them directly. This MPSMC scheme employs a sliding mode control (SMC) algorithm to calculate the reference values of active and reactive powers in the cost function. The reaching, existing and tracking conditions are analyzed to ensure that the designed sliding surface and control law are effective to control the system. The simulation results by Matlab/Simulink show that the MPSMC strategy is able to meet the system requirements of active and reactive powers, as well as the DC output voltage. Compared with the results obtained from the conventional model predictive PI control (MPPIC) scheme, the proposed strategy can improve the dynamic performance dramatically in terms of the response speed under system disturbances, such as varying output voltage and load demand.

Multi-vector model predictive control of permanent magnet synchronous motor can effectively overcome the shortcomings of conventional single-vector model predictive control such as large motor current ripples and limited control accuracy, but its control performance is more susceptible to the influence of motor parameter perturbation. To improve the robustness of the multi-vector predictive control algorithm under the motor parameter perturbation, a novel nonparametric three-vector model predictive current control method for permanent magnet synchronous motors is proposed in this paper. Through constructing an ultra-local predictive current model based on the input and output signals of the permanent magnet synchronous motor, the influence of motor parameter perturbation on current prediction is avoided, and a disturbance observer is designed to estimate the non-modeled and perturbed parts of the ultra-local model. In addition, the direct calculation method of vector duty cycles based on current error is introduced to suppress the influence of motor parameter uncertainty on the duty cycle calculation link, which further improves the system robustness. Finally, simulations and experiments of the proposed method are demonstrated to compare with the conventional parametric three-vector model predictive current control, and the results show that the proposed control strategy can effectively suppress the disturbances caused by the motor parameters and ensure the steady-state performance during motor operation.

Flutter suppression of wind turbine blade section based on two types of model prediction control (MPC) algorithms is investigated. The pre-twist blade, exhibiting displacements of flap-wise bending and lead-lag bending, employs 2D airfoil analysis method, and is subjected to destructive divergent instability displacements. Dynamic modelling of two-dimensional (2D) blade section is based on data analysis characterized by data fitting. The MPC methods including standard MPC and terminal-weight MPC are used to realize flutter suppression for divergent instability. Vibration control and control effects of terminal-weight MPC on divergent instability are analyzed in detail, with different parameters of prediction horizon and control horizon discussed. The superiority of terminal-weight MPC can be apparently demonstrated by comparison of standard MPC and sliding mode control with disturbance observer (SMCDO). The feasibility of hardware implementation of MPC algorithm is discussed by human-computer interaction platform. The performance improvement method of the terminal-weight MPC based on offset control has been refined from the point of view of practical application, with its remarkable effect compared with the original terminal-weight MPC.

The need for cost-effective fuel economy improvements has driven the introduction of automatic transmissions with an increasing number of gear ratios. Incorporation of interlocking dog clutches in these transmissions decreases package space and increases efficiency, as compared to conventional dry or wet clutches. Unlike friction based clutches, interlocking dog clutches require very precise rotational speed matching prior to engagement. Precise engine speed control is therefore critical to maintaining high shift quality. This research focuses on controlling the engine speed during a gearshift period by manipulating throttle position and combustion phasing. Model predictive control (MPC) is advantageous in this application since the speed profile of a future prediction horizon is known with relatively high confidence. The MPC can find the optimal control actions to achieve the designated speed target without invoking unnecessary actuator manipulation and violating hardware and combustion constraints. This research utilizes linear parameter varying (LPV) MPC to control the engine speed during the gearshift period. Combustion stability constraints are considered with a control oriented covariance of indicated mean effective pressure model (COV of IMEP). The proposed MPC engine speed controller is validated with a high-fidelity 0-dimensional engine model with crank angle resolution. Four case studies, based on simulation, investigate the impact of different MPC design parameters. They also demonstrate that the proposed MPC engine controller successfully achieves the speed reference tracking objective while considering combustion variation constraints.

Nowadays, a model predictive control has gained a lot of attractions for power converter. To overcome the defects of the conventional model predictive control and achieve better performance, this article presents a multivector model predictive power control (MV-MPPC) with low computational burden for the grid-tied quasi-Z-source inverter (qZSI). Compared with the conventional model predictive power control (MPPC) that adopts only one vector in one control period, the proposed MV-MPPC applies several vectors including two active vectors, one null vector, and one shoot-through (ST) vector to fulfill the expected performance for the grid-tied qZSI. By calculating the optimal duty ratios of the ST vector, the inductor current ripple can be greatly reduced. To eliminate the weighting factor, a modified sliding-mode control method for simultaneously controlling the capacitor voltage and the inductor current to their setting points is proposed. Furthermore, a quick and effective optimal sector selection method is given, which chooses the optimal sector through a look-up table instead of a bunch of complex calculations. Hence, the computational burden of the proposed MV-MPPC is decreased with significance. Both simulation and experimental results are shown to validate the effectiveness and the advantages of the proposed method.

Fifty-seven per cent of all water supply systems in the Netherlands are controlled by model predictive flow control; the other 43% are controlled by conventional level-based flow control. The differences between conventional level-based flow control and model predictive control were investigated in experiments at five full-scale water supply systems in the first half of 2011. Quality parameters of the drinking water and energy consumption of the treatment and distribution processes were measured and analysed. The experiments showed that the turbidity values are 12–28% lower, and particle volume values 12–42% lower for the systems which are controlled by model predictive flow control. The overall energy consumption of water supply systems controlled by predictive flow control is 1.0–5.3% lower than conventional level-based flow controlled systems, and the overall energy costs are 1.7–7.4% lower.

This paper presents a model predictive voltage control method for single-inductor multiple-output (SIMO) DC/DC converters operating in the continuous conduction mode (CCM). The state-space model for a SIMO DC/DC converter system is formulated and a Model Predictive Control (MPC) strategy, which has the capability to track the reference voltage automatically and reduce the impact of cross regulation problem, is proposed to regulate the system. Considering that the switches in the converter only have ON/OFF modes, the quadratic programming algorithm is employed to obtain the MPC's solution. Simulation studies are conducted in MATLAB/Simulink to verify the performance of the proposed method.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

The research object of this paper is single-phase PWM rectifier, the purpose is to reduce the total harmonic distortion (THD) of the grid-side current. A model predictive current control (MPCC) with fixed switching frequency and dead-time compensation is proposed. First, a combination of an effective vector and two zero vectors is used to fix the switching frequency, and a current prediction equation based on the effective vector’s optimal action time is derived. The optimal action time is resolved from the cost function. Furthermore, in order to perfect the established prediction model and suppress the current waveform distortion as a consequence of the dead-time effect, the dead-time’s influence on the switching vector’s action time is analyzed, and the current prediction equation is revised. According to the experimental results, the conclusion is that, firstly, compared with finite-control-set model predictive control, proportional-integral-based instantaneous current control (PI-ICC) scheme and model predictive direct power control (MP-DPC), the proposed MPCC has the lowest current THD. In addition, the proposed MPCC has a shorter execution time than MP-DPC and has fewer adjusted parameters than PI-ICC. In addition, the dead-time compensation scheme successfully suppresses the zero-current clamping effects, and reduce the current THD.