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This paper presents and experimentally validates a new control scheme for electrical drive systems, named cascaded predictive speed and current control. This new strategy uses the model predictive control (MPC) concept. It has a cascaded structure like that found in field-oriented control or direct torque control. Therefore the control strategy has...
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... Weighting factor removal by reference transformation 33,34 Higher computational burden as compared with conventional PTC and difficult to incorporate multiple control objectives 35 Weighting factor tuning based on coefficient of variation 36 Optimized weights are uncertain in this method and complex calculations are required to implement on hardware Weighting factor tuning based on TOPSIS and NSGA-II methods 37 TOPSIS and NSGA-II algorithms require complex calculations leading to computational challenges 12 Weighting factor removal by Ranking method 38 Ranking based techniques become unfeasible as number of control objectives increases 39 Tuning of weighting factor based on simple additive technique 40 Although technique is simple but not suitable for multiple control objectives 11 Weighting factor tuning based on current ripples 41 Highly dependent on parameter estimation 8,42 Tuning of weighting factor based on error of control objectives 43 This method becomes challenging and complex when number of control objectives increases 44 Weighting factor tuning using Genetic Algorithm (GA) 45 , Simulated Annealing (SA) 42 or Gravitational Search Algorithm (GSA) 43 , Artificial Neural Network 46 , Ant colony based optimization 47 These algorithms are very complex and pose computational challenges 48 Weighting factor tuning based on algebraic/numerical techniques 49 Design complexity increases as slection of weighting factor increases 50 Weighting factor selection based on homogeneous cost functions [51][52][53] This technique is relatively efficient but unable to include multiple control objectives 54 Direct vector selection based techniques to remove weighting factors from cost function 55,56 Direct vector selection techniques provid lower computational burden and lower complexity , however cannot incorporate multiple control objecitve 57 Weighting factor elimination by using cascaded structure of FCS-MPC 58,59 The cascaded structure highly depended on proper selection of dealing cascaded structure 60 www.nature.com/scientificreports/ ...
In the conventional finite control set model predictive torque control, the cost function consists of different control objectives with varying units of measurements. Due to presence of diverse variables in cost function, weighting factors are used to set the relative importance of these objectives. However, selection of these weighting factors in predictive control of electric drives and power converters still remains an open research challenge. Improper selection of weighting factors can lead to deterioration of the controller performance. This work proposes a novel weighting factor tuning method based on the Multi-Criteria-Decision-Making (MCDM) technique called the Entropy method. This technique has several advantages for multi-objective problem optimization. It provides a quantitive approach and incorporates uncertainties and adaptability to assess the relative importance of different criteria or objectives. This technique performs the online tuning of the weighting factor by forming a data set of the control objectives, i.e., electromagnetic torque and stator flux magnitude. After obtaining the error set of control variables, the objective matrix is normalized, and the entropy technique is applied to design the corresponding weights. An experimental setup based on the dSpace dS1104 controller is used to validate the effectiveness of the proposed method for a two-level, three-phase voltage source inverter (2L-3P) fed induction motor drive. The dynamic response of the proposed technique is compared with the previously proposed MCDM-based weighting factor tuning technique and conventional MPTC. The results reveal that the proposed method provides an improved dynamic response of the drive under changing operating conditions with a reduction of 28% in computational burden and 38% in total harmonic distortion, respectively.
... The authors presented a model predictive control with constant switching frequency in conjunction with a PWM modulator and the voltage vectors are chosen dynamically through loops using an efficient cost function optimization approach. In [15], the full predictive control approach uses two predictive controllers to regulate speed and currents. Dynamic characteristics comparison between MPC and FOC on PMSM is presented in [16]. ...
... In the CPC approach, the outer speed loop is still controlled by the PI Controller as in FOC but the inner loop PI controllers of the FOC strategy are omitted by using the error between the reference current and predicted currents based on a cost function [22]. The CPC block diagram of PMSM is shown in Fig. 4. The continuous time model of PMSM should be discretized for the purpose of implementation of the model predictive control to get the following predicted sampling instant states [13][14][15][16][17][18][19][20][21][22][23]. Applying Euler approximation method as given in: ...
In this study, a simplified accurate technique for sensorless direct speed predictive control (DSPC) for the permanent magnet synchronous motor (PMSM) based on the model reference adaptive system (MRAS) is presented. The suggested (DSPC) approach utilizes an innovative method that uses all electrical and mechanical variables in a single control law to determine the best switching vector for the inverter during the following sampling interval. The proposed cost function is simple without weighting factors due to the employment of a sliding term containing the speed/current tracking. As a result, the proposed technique eliminates the PI controllers compared to the conventional vector control approach, getting rid of the drawbacks of the common vector control's cascaded control structure (speed and current loops). Model reference adaptive system is employed to estimate the PMSM speed/position. Particle swarm optimization (PSO) technique is employed to tune the MRAS PI controller gain and integration parameters to get an accurate speed/position estimation. A fair comparison between the suggested DSPC and the conventional current predictive control (CPC), which employs a PI controller in the outer speed loop, is provided to clarify the aspects of the proposed method. To ensure the robustness of the proposed approach, the response for different dynamic operating points is examined using MATLAB M-file/Simulink simulation. The simulation results illustrate distinct response of the DSPC providing good settling time, rise time and steady state error and accepted overshoot with a satisfied torque/current ripples. Also, the results show the excellent tracking of PMSM motor speed/position using MRAS technique. According to simulation comparison results between the suggested DSPC and conventional CPC, the proposed DSPC exhibits much better transient and steady state performance.
... 3) Cascaded FCS-MPC strategies: cascaded FCS-MPC strategies, depicted in a general block diagram in Fig. 7, have been proposed to overcome the limitations of linear controllers used in the outer speed control loop of conventional FCS-MPC schemes, such as MPCC and MPTC [77]- [79]. This strategy combines both electrical and mechanical systems in two separate MPC loops while maintaining the cascade structure in MPCC and MPTC. ...
... Consequently, this either enables the elimination of WFs or reduces the number of WFs by one compared to the MPDSC strategy. Given the cascaded FCS-MPC in [77] and [78], which combines model predictive speed control (MPSC) and MPCC, this method provides a WF-free FCS-MPC strategy in the absence of ACOs. However, when it comes to combining MPSC and MPTC as in [79] and/or in the presence of ACOs, the use of WFs becomes unavoidable. ...
... Nevertheless, their primary advantage, computational complexity, is still preserved. Cascaded FCS-MPC strategies provide a reduction in the number of WFs by separating mechanical and electrical submodels in the MPDSC strategy but do not completely solve the WF design problem [77]- [79]. Therefore, they are not an efficient way to eliminate the WFs. ...
Model predictive control has been widely applied to AC electric drives over the last decade. Despite the proposed solutions, researchers are still seeking to find more effective solutions for weighting factor design, parameter dependency, current/torque harmonics, variable switching frequency, and computational complexity. This paper presents a comprehensive review of the weighting factor design techniques for finite control set model predictive control strategies for AC electric drives. First, the paper introduces the conventional model predictive control techniques for electric drives over permanent magnet synchronous motors. Second, weighting factor design methods are discussed under two main headings: weighting factor selection and weighting factor elimination methods. Third, the ongoing challenges and future trends are addressed by considering the current literature. Based on this review, it is obvious that each weighting factor design method still has problems that await more effective solutions. Finally, this paper reviews various weighting factor design methods for AC electric drives, reveals the advantages and disadvantages of existing methods in terms of control performance, flexibility, design complexity, and computational complexity, and highlights future trends.
... The inner loop generates the output control voltages, considering the stator voltages and current constraints, and the outer loop is the torque reference, keeping in mind the constraints of the torque/speed characteristics of the IM. The full predictive cascaded speed and current control of an IM is presented in [6]. Here, the currents from the inner loop are controlled using a finite control set MPC algorithm, while, for speed control, a continuous control set MPC based on the explicit inversion of the mechanical model is suggested. ...
Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control.
... The inner loop generates the output control voltage considering the constraints of stator voltages and currents and the outer loop the torque reference, having in view the constraints of torque/speed characteristics of IM. A full predictive cascaded speed and current control of an IM is presented in [6]. The currents from the inner loop are controlled using a finite control-set MPC algorithm, while for the speed control, a continuous control-set MPC, based on the explicit inversion of the mechanical model is suggested. ...
Usually, the vector control of the induction machine (IM) is done by using cascade control structures with conventional linear PI controllers, the inner loop being designed for current control, and the outer loop for rotor flux and speed control. In this paper, starting from the dq model of the IM with the rotor magnetic flux space vector aligned along the d axis, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling control of the two dq currents, predictive algorithms with constraints are used to control and limit the currents. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a non-singular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation of the homotopy parameter, the model of the plant is variable, in which case the model free algorithms are properly used to control the flux and speed of the IM. The comparative analysis obtained for the cascade structure with conventional PI controllers and advanced algorithms, respectively, shows that the last lead to improve the dynamical performance of the IM.
... IMs are multi variable, nonlinear systems that are vulnerable to parametric fluctuations and outside instabilities [13]; as a result, they need a highly performing control system. As a result, radical control methods like MPC [14], fuzzy control [15], ANN [16], and sliding mode control have been used to electric drives. MPC has established itself a popular contemporary control techniques in power electronics, especially for three-phase inverters. ...
In recent years, Model Predictive Torque Control (MPTC) has gained popularity as a powerful method of controlling Induction Motors (IMs). There are, however, some factors such as the weighted coefficient that must be taken into account to attain satisfactory performance at different operation points for stator flux. As of now, an empirical procedure has been used for tuning the weighting factor in MPTC, which is not as effective. Model Predictive Flux Control (MPFC) is proposed in this paper, which eliminates complicated process of predicting the stator current by using the flux vectors as control variables. In order to get over these obstacles, a novel Model Predictive Control (MPC) method utilizing a Cascaded Artificial Neural Network (ANN) is proposed in this research. As a result, the control complexity is diminished and weighting factor in standard MPTC is eliminated. In-depth evaluations and comparisons of MPTC and MPFC are conducted, covering low-speed function, dynamic response, and steady-state performance. In contract to traditional approaches, the preferred method has much lower computational cost, virtuous dynamic response, and superior steady-state performance. The overall proposed work is simulated using MATLAB/SIMULINK platform and experimental tests performed on 3Φ VSI fed IM drives are used to verify the efficacy of proposed approach.
... In the traditional PTC strategy, a proportional-integral (PI) controller with antiwindup is generally used as a speed controller. However, the PI controller suffers from limited bandwidth and low disturbance rejection capability and may not meet sufficient control performance where higher control performance is required [15]. To overcome this problem, advanced control structures based on fuzzy logic [16], sliding mode control [17], and model predictive control [15] have been applied to the outer control loop. ...
... However, the PI controller suffers from limited bandwidth and low disturbance rejection capability and may not meet sufficient control performance where higher control performance is required [15]. To overcome this problem, advanced control structures based on fuzzy logic [16], sliding mode control [17], and model predictive control [15] have been applied to the outer control loop. Another solution is to combine a PI controller with a disturbance observer [16], [18], [19]. ...
Recently, model predictive control (MPC) has gained popularity in the control of power converters and electric drives. In this paper, the advantages of two MPC strategies are combined, and a cascaded encoderless predictive speed and torque control (PSTC) based IM drive is proposed. The use of linear controllers in the speed control loop of the traditional PTC strategy degrades the speed control performance in the presence of unknown disturbances and mechanical parameter changes. Also, eliminating encoders from electric drives reduce the cost, size, and hardware complexity, while increasing reliability and mechanical robustness. For this purpose, an extended Kalman filter is designed to estimate the flux, speed, and load torque information required for encoderless PSTC. The proposed electric drive system is validated by simulation studies under different operating conditions, resulting in good control and estimation performance.
... For a liner cascaded control, since the response speed of the internal current loop is faster than that of the external speed loop, the speed loop must have a compromise on the bandwidth to guarantee that the bandwidth of the speed loop is smaller than that of the current loop [20]. In [14], the FCS-MFPCC strategy is applied in the current loop to regulate the stator currents, and the reference current can be effectively tracked. ...
... ⋅̂k. In (20), the lumped disturbancêk can be calculated. Then, substituting (20) into (21), the k q can be derived as ...
... In (20), the lumped disturbancêk can be calculated. Then, substituting (20) into (21), the k q can be derived as ...
... Generally, a single inner-loop system performs well under steady-state conditions, but its PI parameters' adjustment under variable conditions is complicated [7] . Based on the inner-loop MPCC, C. Garcia proposed a full predictive control strategy of PMSM with outer-loop CCS-MPSC and inner-loop FCS-MPCC [8] . ...
... As an optimal control approach, MPC has gained increasing attention among control practitioners due to its distinct advantages of optimizing dynamic performance and handling constrained problems [20], and thus has been widely applied in PMSM drives for improved performance. Specifically, in [16]- [18], the cascade MPC approach is proposed, in which the PI controller for the speed loop or current loop in the cascade structure is replaced by MPC to enhance the control performance of the PMSM system. However, since the cascade configuration of speed and current controllers, the dynamic response performance of speed regulation is still limited to a certain extent [21]. ...
... Therefore, to guarantee that both the voltage and current constraints are respected, the q-axis voltage u cq generated by our proposed controller (16), must meet ...
... Theorem 1: For the PMSM system (1), with appropriate selections of the control parameters T 0 , ρ, and the observer parameters λ i , ℓ i,j , the closed-loop system comprising the HOSMOs (5), (6), the composite predictive speed controller (16), the self-tuning horizon mechanism (15), and the constraints handling mechanism (23), will yield the following conclusions: ...
This article proposes and investigates a novel composite generalized dynamic predictive control (GDPC) approach for wide-range speed regulation of permanent magnet synchronous motor (PMSM) drives under a non-cascade configuration. Firstly, to eliminate negative effects arising from both matched and mismatched disturbances within the receding-horizon optimization of the baseline generalized predictive control (GPC) design, two high-order sliding mode observers (HOSMOs) are constructed for disturbance estimation, which allows for offset-free tracking of the reference speed. Furthermore, a novel self-tuning horizon mechanism is introduced for wide-range speed regulation scenarios of PMSM drives. This feature enables autonomous and dynamic adjustment of the prediction horizon across diverse speed regulation ranges, resulting in significantly improved performance optimization compared to the conventional GPC method. Finally, the proposed GDPC methodology is implemented on a digital signal processor (DSP) hardware system. The simulations and experiments demonstrate the effectiveness of the proposed control approach.
