Traditional Predictive Control Research Articles

ENHANCED MODEL-FREE PREDICTIVE CONTROL FOR VOLTAGE SOURCE INVERTERS USING AN ADAPTIVE OBSERVER

In this paper, a free model predictive control based on the active vector execution time (AVET-MFPC) using an adaptive observer is proposed for two-level voltage source inverters. The traditional model-free predictive control (MFPC) uses the sampling period to select one voltage vector for all candidate vectors according to the minimizing cost function principle. With the proposed control, two vectors are selected at one sampling period. The first vector is an active vector that uses the execution time of the active vector to select it, while the second one is a zero vector as it is applied after the active vector. The execution time is calculated using the ultra-local model (ULM) equation. In the traditional MFPC, the factor in the ULM is chosen with approximate values ranging between ±50 % of the nominal value. This paper proposes an adaptive sliding mode observer (ASMO) with an improved design to observe the variation of this factor, especially in case of mismatch parameters and during a step change in the reference signal. Combining the proposed ASMO observer and the AVET-MFPC controller gives faster system response, good tracking results, and less computational burden. Finally, the effectiveness of the proposed control model is approved and confirmed under various conditions, as well as the simulations carried out and the results obtained.

Efficient predictive control method for ORC waste heat recovery system based on recurrent neural network

The model predictive control performs well in regulating operating parameters and ensuring system safety when the organic Rankine cycles are operating with variable heat sources. However, traditional nonlinear predictive control based on the organic Rankine cycle mechanism model involves significant computational complexity, making it challenging to quickly find a control solution. This limitation hinders its application in the organic Rankine cycle for rapid response control. To address this issue, a fast model predictive control method is proposed in this work. A recurrent neural network model is well-trained using the input–output data of the organic Rankine cycle process, and it is used as a surrogate model in the design of the model predictive controller for the control of organic Rankine cycle operating parameters. The formulated optimal control problem is then transformed into a mixed integer linear programming problem, which can obtain high-quality and fast solutions during the control process. Through comparison with recurrent neural network-based nonlinear predictive control and pseudo-sequential method-based fast nonlinear predictive control, the results show that the designed controller can effectively accomplish the control of organic Rankine cycle operating parameters with smaller overshoot. Moreover, its average control solution time is shorter by 89.59% and 93.27% respectively while the total net output power of the system during the control process is 0.54% and 1.3% higher than that of the other two controllers. It exhibits superior control performance, even under variable waste heat conditions.

Data-Driven Distributed Predictive Control for Voltage Regulation and Current Sharing in DC Microgrids With Communication Constraints.

The use of nonideal communication networks makes communication constraints become a topical issue in the research of dc microgrids. How to design a distributed secondary control scheme for voltage recovery and accurate current sharing in islanded dc microgrids subject to communication constraints is of interest in this article. In order to restore the bus voltage to the rated value, a nonlinear element is first introduced into the primary control layer. Then, the closed-loop system of primary control is modeled as a data-driven time-varying linear system. Based on the established model, considering communication constraints, a distributed secondary predictive control strategy is developed to achieve accurate current sharing. While actively compensating for network delays and packet losses, the proposed method renders mathematical physical models unnecessary for the traditional predictive control, and simultaneously completes the multitask in dc microgrids. Finally, several case studies are conducted on a hardware microgrid experimental platform, which not only verifies the effectiveness of the designed data-driven predictive control strategy but also tests microgrid properties such as the plug-and-play ability.

An improved data-driven predictive optimal control approach for designing hybrid electric vehicle energy management strategies

This paper proposes an improved “prediction + optimal control” method for energy management in hybrid electric vehicles equipped with planetary gears. A differentiable predictor and a differentiable optimal controller are developed using supervised learning and reinforcement learning approaches, respectively. Three training steps are performed for the initial predictor, the optimal controller, and the final predictor. This method improves the traditional energy management predictive optimal control approach by incorporating an additional step of retraining the differentiable predictor. This adjustment ensures that the predictor does not blindly improve its performance based on evaluation criterion irrelevant to energy management, which was commonly used in previous studies. Instead, it focuses on enhancing the overall performance of energy management under the “prediction + optimal control” framework. The approach introduced in this paper is compared with the globally optimal dynamic programming results and traditional predictive optimal control methods on the Next Generation Simulation (NGSIM) data. Our method outperforms traditional approaches in energy management on both the training dataset and the test dataset. This further illustrates that the conventional practice of presumptuously optimizing predictors in “prediction + optimal control” methods can be improved using the proposed method.

Fast Terminal Sliding-Mode Predictive Speed Controller for Permanent-Magnet Synchronous Motor Drive Systems

This paper proposes a kind of discrete-time sliding-mode predictive control (SMPC) based on a nonlinear sliding function for permanent-magnet synchronous motors’ (PMSMs’) speed control to improve the convergence performance. Compared with traditional sliding-mode predictive control based on a linear sliding function, the proposed SMPC has a faster convergence rate thanks to the design of a nonlinear fast terminal sliding function. Moreover, one-step prediction is employed, which greatly simplifies the algorithm and improves the real-time performance of its operation. The sliding state will follow the expected trajectory of a predefined sliding-mode reaching law. The stability and convergence performance of the proposed method is analyzed. The results of the theoretical analysis, simulations, and experiments indicate that the proposed method has excellent convergence performance and robustness.

A Novel Predictive Voltage Control Technique for a Grid Connected Five Phase Permanent Magnet Synchronous Generator

This study focuses on developing an effective control strategy to enhance the dynamics of a wind turbine grid-connected five-phase permanent magnet synchronous generator (PMSG). To visualize the superior performance of the newly proposed controller, the generator's performance is evaluated with another traditional predictive control scheme: predictive torque control (PTC). However, the vector control principle is applied to the GSC converter. The PTC has limitations such as significant ripple, substantial load commutation, and the inclusion of a weighting element in its cost functions. The proposed predictive methodology aims to overcome limitations, uses a simple cost function, and doesn't require weighting elements to address concerns about stability errors. Comparing the proposed predictive voltage controller (PVC) to the PTC, the findings show that the suggested PVC has many benefits, including faster dynamic response, a simpler control structure, fewer ripples, reduced current harmonics, low computation burdens, and robustness, so the generated power affects system efficiency, leading to improved power quality and reduced switching losses, enhancing power converters efficiency and their switches lifespan, this fact is verified mathematically as the total harmonic distortion (THD) has reduced to 1.346% average percentage for the proposed controller. However, the THD of the PTC is 3.05%. In addition, the study examines the incorporation of pitch angle control (PAC) and maximum power point tracking (MPPT). These controls restrict the consumption of wind energy when the generator speed surpasses its rated speed and optimize the extraction of wind energy during periods of low wind availability. In summary, the proposed PVC-enhanced control system reveals superior performance in dynamic response, control simplicity, current quality, and computational efficiency compared to other methods.

Neural network predictive controller based on the improved TPA-LSTM model for ultra-supercritical units

To mitigate the impact of large-scale renewable energy power on the national grid in China, it is imperative to enhance the flexible peaking capability of coal-fired thermal power units. The coordinated control system, central to the load control of coal-fired units, faces challenges such as multivariable coupling, sluggish response, and uncertain coal quality parameters. This paper introduces a neural network predictive controller based on the improved TPA-LSTM model, aimed at addressing these issues. Initially, a data-driven control model is established to break through the limitations of traditional linear predictive control and effectively handle disturbance uncertainties. Then, a multivariable coordinated control strategy based on the neural network controller is designed, achieving effective decoupling of multiple parameters and ensuring high adaptability across all load conditions. Additionally, by integrating an automatic model updating mechanism, the system can recalibrate in real-time when model mismatches occur due to equipment aging, maintenance, or changes in coal quality, thereby enhancing overall control performance. Simulation results demonstrate that this strategy has excellent control effectiveness, meeting the flexible peaking demands of 1000 MW ultra-supercritical units. The calibration feature of the data-driven model significantly improves control performance following model mismatches.

Application of fuzzy prediction control model based on neural network in teaching resource recommendation and matching

As technology evolves, the allocation and use of educational resources becomes increasingly complex. Due to the many factors involved in recommending and matching English education resources, traditional predictive control models are no longer adequate. Therefore, fuzzy predictive control models based on neural networks have emerged. To increase the effectiveness and efficiency of using English educational resources (EER), this research aims to create a neural network-based fuzzy predictive control model (T-S-BPNN) for resource suggestion and matching. The results of the study show that the T-S-BPNN model α proposed in the study starts from 0 and increases sequentially by 0.1 up to 1, observing the change in MAE values. The experiment’s findings demonstrate that the value of MAE is lowest at values around 0.5. The T-S-BPNN model, on the other hand, gradually plateaued in its adaptation rate up to 7 runs, reaching about 9.8%. The accuracy rate peaked at 0.843 when the number of recommendations reached 7. The recall rate also peaked at 0.647 when the number of recommended English courses reached 7. The R-value for each set hovered around 0.97, which is a good fit. And the R-value of the training set is 0.97024, which can indicate that the T-S-BPNN model model proposed in the study fits well. It indicates that the algorithm proposed in the study is highly practical.

Error-Triggered Adaptive Sparse Identification for Predictive Control and Its Application to Multiple Operating Conditions Processes.

With the digital transformation of process manufacturing, identifying the system model from process data and then applying to predictive control has become the most dominant approach in process control. However, the controlled plant often operates under changing operating conditions. What is more, there are often unknown operating conditions such as first appearance operating conditions, which make traditional predictive control methods based on identified model difficult to adapt to changing operating conditions. Moreover, the control accuracy is low during operating condition switching. To solve these problems, this article proposes an error-triggered adaptive sparse identification for predictive control (ETASI4PC) method. Specifically, an initial model is established based on sparse identification. Then, a prediction error-triggered mechanism is proposed to monitor operating condition changes in real time. Next, the previously identified model is updated with the fewest modifications by identifying parameter change, structural change, and combination of changes in the dynamical equations, thus achieving precise control to multiple operating conditions. Considering the problem of low control accuracy during the operating condition switching, a novel elastic feedback correction strategy is proposed to significantly improve the control accuracy in the transition period and ensure accurate control under full operating conditions. To verify the superiority of the proposed method, a numerical simulation case and a continuous stirred tank reactor (CSTR) case are designed. Compared with some state-of-the-art methods, the proposed method can rapidly adapt to frequent changes in operating conditions, and it can achieve real-time control effects even for unknown operating conditions such as first appearance operating conditions.

Enhancing the Performance of a Renewable Energy System Using a Novel Predictive Control Method

The current study concerns improving the performance of a renewable energy system using systematically designed control algorithms. The performance of the system under study is evaluated under two operating scenarios: the first in which the system consists of only a wind-driven synchronous generator connected to the utility grid; in the second scenario, the generator is combined with a photo-voltaic solar system and a battery for supplying a load. Each system component is modeled and thoroughly described. To maximize the benefits of solar and wind energies, two separate maximum power point tracking procedures are adopted. Furthermore, to enhance the generator’s dynamics, a novel predictive control scheme is designed and validated by comparing its performance with traditional predictive control. The novel predictive controller utilized a simple and unique cost function to avoid the shortages of traditional predictive controllers. For standalone operation, an effective procedure is adopted to ensure the power balance between the generation, storage, and isolated load units. To evaluate the effectiveness of the designed controllers under different operating regimes, Matlab/Simulink is utilized for this task. The obtained results confirm the superiority of the novel predictive scheme used with the synchronous generator over the classic control approach for the two operating scenarios. This has been shown in the form of reduced ripples and reduced current harmonics. The obtained results are also confirming the validity of the adopted maximum power tracking strategies with solar panels and wind turbines as well. Furthermore, balanced power delivery is achieved thanks to the adopted management strategy for standalone operation, which enhances the overall system performance.

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control.

Computationally Efficient Stability-Based Nonlinear Model Predictive Control Design for Quadrotor Aerial Vehicles

This article presents the design of a stability-guaranteed nonlinear model predictive controller for quadrotor-type microaerial vehicles to operate robustly on fast trajectories. The basic controller structure operates without having to use terminal conditions in the optimization problem. As a result, the controller is computationally less demanding and provides more stable closed-loop performance than traditional nonlinear predictive control schemes. This article presents a detailed stability analysis without terminal costs or terminal constraints and proves the asymptotic stability and necessary conditions for the recursive feasibility of the system. This article derives the growth-bound sequence that enables obtaining the shortest possible prediction horizon for stability. The proposed analysis provides the necessary conditions to implement the controller while using the shortest stabilizing prediction horizon compared to major traditional predictive control schemes reported in the literature. This particular feature enables the proposed controller to perform fast optimization and, hence, the capability to implement fast trajectories using feedback regularization. In order to demonstrate the validity of this new proposed control scheme, first, several MATLAB simulations are conducted to demonstrate the improved performance of the controller especially when the quadrotor vehicle follows fast trajectories. Real-time lab experiments are also conducted to validate the performance of the proposed scheme for point stabilization (hovering) and trajectory tracking problems. The results show that the proposed scheme can stabilize the system with a relatively short prediction horizon, a fast convergence rate, and a small tracking error.

Multi-objective predictive control based on the cutting tobacco outlet moisture priority

In this paper, we propose a new priority multi-objective optimization strategy of system output variables in cutting tobacco process. The proposed strategy focuses on the cutting tobacco moisture-controlled output variables optimization in feasible regions with two levels according to the priority. This study aims to provide a novel technical support for the chemical industry contained drying process. In order to alleviate the lack of degree of freedom of the system, strict set-point control is given, meanwhile, other output variables adopt zone control. Firstly, the system control output variables are optimized in ascending order of priority. Secondly, the specific lower-level target constraints are first relaxed. Finally, the relaxation of other high-priority target constraints is stopped when the optimization is feasible. Thus, the system control output variables move along the optimal target trajectory. From the perspective of practical application of engineering, under the condition of disturbance existing in the cutting tobacco drying process, the simulation shows that the proposed approach has good robustness when there is disturbance, and the previous method cannot meet the control requirement. The proposed strategy meanwhile has better tracking effect through single and multiple output variables simulation, which compared with traditional predictive control in real cutting tobacco drying process.

Experimental verification for cost function optimization using TOPSIS approach for predictive control of surface mounted PMSM

AbstractIn this article, the selection of weight factor (WF) is improved using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based cost function (CF) optimization for predictive control in surface mounted permanent magnet synchronous motor (PMSM) drive under different conditions. Basically, the empirical approach used for WF is instinctive and consumes excess time. TOPSIS approach chooses a control action that is closest to the ideal control action's positive solution and farthest from its ideal control action's negative solution. Prioritizing control variables in the CF assures the choice of the best control action throughout each sampling duration. Additionally, predefined switching instants are employed for the CF optimization depending on past optimal control action, reducing the computing duration of the suggested technique. dSPACE controller is used for real‐time execution of traditional and proposed methods under steady and dynamic states. In order to verify the worth of predictive control based on TOPSIS, the hardware responses were compared with traditional DTC and predictive control strategies.

Control for induction motor drives using predictive model stator currents and speeds control

<span>This paper is presented for designing a new controller using the predictive model current and speed control method for the asynchronous motor. This control method is based on traditional predictive controller development to have a cascade structure similar to the rotor flux control (field-oriented control) and direct torque control (DTC). Therefore, this control method will have two control loops. Both inner and outer loop controllers use predictive power. The outer ring is speed control, while the internal circle is stator current control. The inner loop is based on the finite control set – model predictive control (FCS-MPC), while the outer ring to take full advantage of the high dynamic response of the inner circle uses the deadbeat MPC. MATLAB simulation results show that this control method has performance equivalent to traditional controllers while minimizing overshoot and having fast, on-demand response times.</span>

Multiscalar Model-Based Predictive Torque Control Without Weighting Factors and Current Sensors for Induction Motor Drives

This article focuses on eliminating the weighting factor in traditional predictive torque control (PTC) and considers the control scheme without current sensors. A current sensorless direct PTC method without the weighting factor for induction motor (IM) drives is proposed. Based on the multiscalar model, the controller directly predicts the torque and its dual quantity, instead of predicting the stator flux and stator current in advance and then indirectly calculating the torque like PTC. Since the torque and its dual quantity (the inner and external products of the flux and stator current) have the same units and dimensions, the design of the cost function does not require a weighting factor. Furthermore, an adaptive virtual current observer is used to reconstruct the stator currents. The estimated currents replace the measured currents to realize current sensorless, which can avoid the measurement noise caused by the current sensors, thus reducing the current and torque ripples. Simulation and experimental results illustrate the effectiveness of the proposed approach under different working conditions.

TRACKING OF NON-STANDARD TRAJECTORIES USING MPC METHODS WITH CONSTRAINTS HANDLING ALGORITHM

In recent decades, a Model-Based Predictive Control (MPC) has revealed its dominance over other control methods such as having an ability of constraints handling and input optimization in terms of the value function. However, the complexity of the realization of the MPC algorithm on real mechatronic systems remains one of the major challenges. Traditional predictive control approaches are based on zero regulation or a step change. Nevertheless, more complicated systems still exist that need to track setpoint trajectories.
 Currently, there is an active development of robotics and the creation of transport networks of movement without human participation. Therefore, the issue of programming the given trajectories of vehicles is relevant. In this article, authors reveal the alternative solution for tracking non-standard trajectories in spheres such as robotics, IT in mechatronics, etc., that could be used in self-driving cars, drones, rockets, robot arms and any other automized systems in factories.
 The ability of Model-Based Predictive Control (MPC) such as the constraints handling and optimization of input in terms of the value function makes it extremely attractive in the industry. Nevertheless, the complexity of implementation of MPC algorithm on real mechatronic systems remains one of the main challenges.
 Secondly, common predictive control algorithms are based on the regulation approach or a simple step shift. However, there exist systems that are more complicated where a setpoint to be tracked is given in the form of trajectories. In this project, there were made several modifications in order to improve an MPC algorithm to make better use of information about the trajectories.

The Potential of Control Models Based on Reinforcement Learning in the Operating of Solar Thermal Cooling Systems

The objective of this research work was to investigate the potential of control models based on reinforcement learning in the optimization of solar thermal cooling systems (STCS) operation through a case study. In this, the performance of the installation working with a traditional predictive control approach and with a reinforcement learning (RL)-based control approach was analyzed and compared using a specific realistic simulation tool. In order to achieve the proposed objective, a control system module based on the reinforcement learning approach with the capacity for interacting with the aforementioned realistic simulation tool was developed in Python. For the studied period and the STCS operating with a control system based on RL, the following was observed: a 35% reduction in consumption of auxiliary energy, a 17% reduction in the electrical consumption of the pump that feeds the absorption machine and more precise control in the generation of cooling energy regarding the installation working under a predictive control approach. Through the obtained results, the advantages and potential of control models based on RL for the controlling and regulation of solar thermal cooling systems were verified.

Robust data-driven predictive control using reachability analysis

We present a robust data-driven control scheme for an unknown linear system model with bounded process and measurement noise. Instead of depending on a system model in traditional predictive control, a controller utilizing data-driven reachable regions is proposed. The data-driven reachable regions are based on a matrix zonotope recursion and are computed based on only noisy input-output data of a trajectory of the system. We assume that measurement and process noise are contained in bounded sets. While we assume knowledge of these bounds, no knowledge about the statistical properties of the noise is assumed. In the noise-free case, we prove that the presented purely data-driven control scheme results in an equivalent closed-loop behavior to a nominal model predictive control scheme. In the case of measurement and process noise, our proposed scheme guarantees robust constraint satisfaction, which is essential in safety-critical applications. Numerical experiments show the effectiveness of the proposed data-driven controller in comparison to model-based control schemes.

Event-Triggered Neural-Predictor-Based FCS-MPC for MMC

Traditional predictive control schemes dominated the research field of numerous power electronic applications over the past few years, since they usually lead to control solutions with good dynamic and steady results. However, these solutions strongly depend on the available knowledge of the control system (e.g., accurate modeling information), which often results in the lack of robustness in the presence of parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates with high switching frequency, which directly affects efficiency. Aiming to cumbersome aforementioned scarcities, this letter is concerned with a novel approach to the controller design for a modular multilevel converter, which makes use of event-triggered neural-predictor-based finite-control-set model-predictive control methodology under low-switching-frequency operation. The salient feature of the proposed controller is that the uncertainties and unnecessary energy loss in practical systems can be explicitly dealt with, while keeping the switching frequency in a low value. Finally, steady-state and transient-state performance tests together with the analysis of the simulation and experimental results confirm the interest of the proposal, and the results found are promising and motivate further research in this field.