Model-free Predictive Control Research Articles

Robust Predictive Control of Grid-Connected Converters: Sensor Noise Suppression With Parallel-Cascade Extended State Observer

Model predictive control (MPC) is a constrained optimization control method with superior performance than linear methods for multi-variable and multi-objective control of power converters. Nonetheless, its performance is limited by model uncertainties and measurement noise. This study tackles this challenge by proposing a new hybrid parallel-cascade extended state observer (PC-ESO) with two key advantages, viz.: (i) higher disturbance rejection than the conventional linear ESO and cascade ESO (CESO) at low bandwidth, and (ii) better noise suppression than the conventional ESO. PC-ESO's time-domain structure, and comprehensive frequency-domain analysis are presented. Furthermore, PC-ESO is applied to improve the transient disturbance rejection of CESO through a novel structurally-adaptive ESO (SAESO) algorithm. The proposed SAESO provides both high-frequency noise-suppression and better disturbance rejection than CESO and cascade-parallel ESO. Finally, the proposed methods are experimentally validated by the model-free predictive control of a grid-connected power converter.

Model-Free Predictive Control for Grid-Connected Converters With Flexibility in Power Regulation: A Solution for Unbalanced Conditions

Model-Free Predictive Control for Grid-Connected Converters With Flexibility in Power Regulation: A Solution for Unbalanced Conditions

Bidirectional DC-DC converters for distributed energy resources: Robust predictive control with structurally-adaptive extended state observers

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise.

Predictive Control for Unknown Dynamics With Observation Loss: A Temporal Game-Theoretic Approach

This paper is concerned with a model-free predictive control problem on systems with unknown dynamics. Different from existing predictive control, the predictive feedback strategy is designed to take control inputs to cover the infinite horizon when the system exists the observation loss. To predict future control inputs, a temporal game-theoretic approach is presented to model such a predictive control issue as an optimization problem and ensure the optimality of the system performance. Moreover, a predictive algebraic Riccati equation (PARE) is constructed to solve such an optimization problem. By leveraging offline datasets and the real-time data of state and input, a data-driven parallel computational framework is developed to iteratively solve the PARE. In this way, the prior knowledge of the systems is avoided and the computational complexity of the proposed algorithm is reduced. Finally, numerical and practical examples are presented to show the applicability of the proposed results.

Event-Triggered Distributed Secondary Control With Model-Free Predictive Compensation in AC/DC Networked Microgrids Under DoS Attacks.

This article presents an event-triggered distributed secondary control with predictive compensation based on the model-free predictive control under Denial-of-Service (DoS) attacks in ac/dc-networked microgrids. First, models of ac/dc networked microgrids in both electric network and communication network are established. On this premise, event-triggered distributed secondary control is proposed to solve the problems of strong communication burden and low-power distribution accuracy. Besides, aiming at the impact of DoS attacks on distributed secondary control, a compensation algorithm based on model-free predictive control is designed to estimate the control variables when the DoS attack occurs, which can improve the control performance and maintain the stable operation without a specific system structure system. Then, the convergence of event-triggered distributed secondary control with the condition of whether DoS attacks happen are analyzed. Finally, the effectiveness of the proposed control is verified on the hardware-in-loop (HIL) simulation platform consisting of the RT-LAB simulator, MATLAB/Simulink simulation model, and DSP controller.

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Enhanced Model-Free Predictive Speed Control Without Weighting Factors for PMSM Based on Newly Designed Cost Function

Model-Free Predictive Current Controller for Voltage Source Inverters Using ARX Model and Recursive Least Squares

Model-Free Predictive Current Controller for Voltage Source Inverters Using ARX Model and Recursive Least Squares

A Switched Ultra-Local Model-Free Predictive Controller for PMSMs

A Switched Ultra-Local Model-Free Predictive Controller for PMSMs

Model-Free Predictive Control of Power Converters with Multifrequency Extended State Observers

The performance of model-free predictive control (MFPC), which is based on the ultra-local model, is highly dependent on the effectiveness of the extended-state observer (ESO). The standard linear ESO has a high-gain which enhances disturbance rejection attributes. Nevertheless, this ESO has two main limitations: i) it amplifies high-frequency measurement noises, and ii) it deteriorates disturbance rejection performance when the noise immunity is improved. Therefore, this article introduces a new technique to improve both the disturbance rejection and noise suppression of ESO without increasing the bandwidth: the multi-frequency-based ESO (MF-ESO). Two novel ESO structures that operate on the MF-ESO principle are analyzed: parallel and cascade ESOs. Extensive frequency domain analyses unravel the comparative capabilities of the MF-ESO structures for improved disturbance rejection and/or high-frequency noise suppression. A novel adaptive gain is proposed to improve CESO's dynamic performance. The superior features of the proposed MF-ESO are experimentally validated by the model-free predictive control of a grid-connected power converter.

Self-Healing Secure Blockchain Framework in Microgrids

Blockchain has recently been depicted as a secure protocol for information exchange in cyber-physical microgrids. However, it is still found vulnerable to consensus manipulation attacks. These stealth attacks are often difficult to detect as they use kernel-level access to mask their actions. In this paper, we firstly build a trusted and secured peer-to-peer network mechanism for physical DC microgrids’ validation of transactions over Distributed Ledger. Secondly, we leverage from a physics-informed approach for detecting malware-infected nodes and then recovering from stealth attacks using a self-healing recovery scheme augmented into the microgrid Blockchain network. This scheme allows compromised nodes to adapt to a reconstructed trustworthy signal in a multi-hop manner using corresponding measurements from the reliable nodes in the network. Additionally, recognizing the possible threat of denial-of-service attacks and random time delays (where information sharing via communication channels is blocked), we also integrate a model-free predictive controller with the proposed system that can locally reconstruct an expected version of the attacked/delayed signals. This supplements the capabilities of Blockchain, enabling it to detect and mitigate consensus manipulation attempts, and network latencies.

Improved cascaded model-free predictive speed control for PMSM speed ripple minimization based on ultra-local model

This paper presents an improved cascaded model-free predictive speed and current control with the periodic and aperiodic disturbances suppression to achieve a smooth speed. The cascaded structure has an external speed loop and an internal current loop, both implemented with model-free predictive control to enhance the robustness of the controller. The current loop is designed based on the finite control set model-free predictive current control (FCS-MFPCC) strategy with an ultra-local model to regulate the stator currents, and the speed loop uses the proposed continuous control set model-free predictive speed control (CCS-MFPSC) to make full use of the excellent dynamic performance of the current-loop controller. To suppress the periodic disturbance that exists in the PMSM system, an improved parallel quasi-resonant controller (QRC) with an error limitation is embedded into the CCS-MFPSC, which can generate the compensated current. Based on the stability condition, the stability of the proposed MFPSC-QRC strategy is directly analyzed in the z-domain. Finally, the effectiveness and feasibility of the proposed cascaded model-free predictive speed and current strategy are validated on a PMSM test platform.

Power Sharing and ZSCC Elimination for Parallel T-Type Three-Level Rectifiers Based on Model-Free Predictive Control

Power Sharing and ZSCC Elimination for Parallel T-Type Three-Level Rectifiers Based on Model-Free Predictive Control

Model-Free Predictive Control of Power Converters With Cascade-Parallel Extended State Observers

The sensitivity of conventional model predictive control to parametric uncertainties has made model-free predictive control (MFPC) necessary. MFPC with disturbance rejection features requires a high-gain extended state observer (ESO) for state and total disturbance estimation. The standard ESO is limited in performance when high-frequency measurement noise is fed into it. Hence, this article proposes a novel hybrid cascade-parallel ESO which has good measurement noise suppression and does not diminish the outstanding dynamic performance of predictive control. The proposed ESO also has better disturbance rejection than the cascade ESO. Comprehensive theoretical analyses of the cascade-parallel ESO and its alternative structures provide further insights for design and application. Finally, the practicality of the proposed method is demonstrated experimentally by the model-free predictive control of a grid-connected converter.

Predictive control of power demand peak regulation based on deep reinforcement learning

As urbanization continues to accelerate, effectively managing peak electricity demand becomes increasingly critical to avoid power outages and system overloads that can negatively impact both buildings and power systems. To tackle this challenge, we propose a novel model-free predictive control method called “Dynamic Dual Predictive Control-Deep Deterministic Policy Gradient (D2PC-DDPG)" based on a deep reinforcement learning framework. Our method employs the Deep Forest-Deep Q-Network (DF-DQN) model to predict electricity demand across multiple buildings, and based on the output of the DF-DQN model, applies the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize coordinated control of energy storage systems, including hot and chilled water storage tanks in multiple buildings. Experimental results show that our proposed DF-DQN model outperforms other traditional machine learning, deep learning, and reinforcement learning methods in terms of prediction accuracy, such as mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, our D2PC-DDPG method achieves superior control performance and peak load reduction compared to other reinforcement learning methods and an RBC-based control method. Specifically, our method successfully reduced peak load by 27.1% and 21.4% over a two-week period in the same regions. To demonstrate the generalizability of our D2PC-DDPG method, we tested it in five different regions and compared its performance with an RBC-based control method. The results showed that our method achieved an average reduction of 16.6%, 7%, 9.2%, and 11% for ramping, 1-load_factor, average_daily_peak, and peak_demand, respectively. These findings demonstrate the effectiveness and practicality of our proposed method in addressing critical energy management issues in various urban environments.

Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression

Model predictive control (MPC) is an efficient and multi-functional control scheme for synchronous permanent magnet generators (PMSGs). However, the effective management of traditional MPC depends on precise system models. Multiple uncertainties of permanent magnet flux, motor inductance, filter inductance and parameter measurement noise will limit MPC’s performance. The conventional linear extended state observer (ESO) can perform robust predictive control of the ultralocal model of the PMSG system to cope with parameter mismatches. However, the ESO is limited in balancing disturbance rejection with measurement noise attenuation. Since the amplification of high-frequency noise pollution can lead to both poor control performance and system instability, this challenge is of significant importance. To solve the problem, a new hybrid parallel cascaded ESO (PCESO) model-free predictive control framework is proposed using the three-level neutral-point-clamped (NPC) power electronic converter, on both the machine side and grid side. Analytical discussions of the time and frequency domain characteristics of the PCESO demonstrate its superior characteristics over the ESO. The proposed method can effectively balance parameter mismatch, disturbance rejection and high-frequency noise suppression. Finally, the effectiveness of the proposed method, under uncertainties of parameter mismatches, measurement noise and permanent magnet flux, is verified through real-time hardware-in-the-loop tests on a back-to-back grid-tied PMSG interfaced with an NPC power converter.

Generalized Data-Driven Model-Free Predictive Control for Electrical Drive Systems

The performance of model predictive control has a strong correlation to the precision of the physical parameters of the plant, and these parameters are hard to determine since they are continuously changing during the operation process. To fully eliminate the influence of the physical parameters and enhance robustness, a model-free predictive control is proposed in this article to suit the electrical drive systems. The plant model is designed as several discrete-time transfer functions used to decouple the input and output signals and to describe their relationships, and the coefficients of these functions are online designed based on the recursive least square algorithm. An observer is designed to obtain accurately sampled current components considering the delays. The proposed method is applied to a permanent magnet synchronous motor speed control system as the stator current controller, and the simulation and experimental results show the advantages of the improved dynamics, stator current quality, and robustness compared with the conventional model-free predictive current control strategy.

Predictor-Based Data-Driven Model-Free Adaptive Predictive Control of Power Converters Using Machine Learning

In this article, a novel robust data-driven model-free predictive control framework based on the I/O data of the controlled plants, which is performed by incorporating the neural predictor-based model-free adaptive control and finite control-set model predictive control, is first proposed. The salient feature of the suggested framework is that the uncertainties, such as unmodeled dynamics and external disturbances, can be explicitly addressed in controlled systems. From a practical standpoint, however, the potential of this proposal is limited by a significantly increased online computational complexity, which makes it difficult to implement. To circumvent this limitation, a supervised imitation learning technique using data labeled is developed to imitate the known suggested controller, which the majority of the online computational burden can be transformed into offline computing by utilizing a trained artificial neural network subject to robustness characteristics. In particular, this development motivates a much simpler robust predictive control solution, which is convenient to implement in applications. Thus, by this proposal, the online implementation of much more complex predictive control strategies is made possible, and it explores a new possibility for future development of the complex control methodology. Finally, extensive simulative and experimental investigations for modular multilevel converter validate the interest and viability of the proposed design methodology.

Predictive control optimization of chiller plants based on deep reinforcement learning

The energy consumption of HVAC systems is enormous, with chiller plants accounting for more than 50% of it. To improve energy efficiency, chiller systems are typically optimized at fixed intervals based on real-time building cooling loads, which usually assumes that the cooling load remains constant within the control interval. However, in many real applications, the current optimal control may be changed when the cooling load suddenly fluctuates. Although this issue can be addressed by shortening the control intervals, more frequent control can damage the equipment or increase energy consumption. To tackle this problem, this paper proposes a model-free predictive control method based on Reinforcement Learning (RL) control and Long Short-Term Memory (LSTM) prediction networks. The LSTM network aims to predict the future cooling load based on historical cooling load data, while the RL is used to make the best control for all chiller plants. By this way, the chilled water supply temperature setpoints can be optimized with the consideration of both instantaneous and predicted cooling loads to minimize energy consumption. In order to validate the effectiveness of the proposed method, an experimental simulation model was constructed based on actual chiller system parameters. Experimental results show that the energy-saving performance of the proposed method is superior to rule-based control (9% improvement), closely comparable to the model predictive control (0.73% difference), and further enhances energy-saving effects compared to non-predictive RL control without shortening the control interval. Additionally, the proposed method just learns by continuously interacting with the environment and does not require any accurate equipment models, making it a viable alternative for buildings when lacking extensive sensors and device information.

Virtual Voltage Vector-Based Sequential Model-Free Predictive Control for Multiparalleled NPC Inverters

Parallel inverters are widely used in the micro-grid systems to meet the demand for increased power capacity and enhanced reliability. However, unbalanced connections between the parallel inverters may cause a spike in the zero-sequence circulating current (ZSCC), leading to performance degradation. To address this issue, a virtual voltage vector-based sequential model-free predictive control method is proposed in this paper for parallel converters. Specifically, extended state observer (ESO) and virtual voltage vectors are introduced into the finite control-set model predictive control (FCS-MPC) architecture to cope with the multi-objective including ZSCC inhibition and robustness performance improvement. Next, to prevent the adjusting of weighting factors, a sequential model-free predictive control solution is constructed. The main feature of the proposed control methodology is that the reliability and robustness of the controlled system can be significantly enhanced without knowing the accurate mathematical models and parameters. Finally, the effectiveness of the suggested approach is verified by performance evaluation.

Fractional-Order Model-Free Predictive Control for Voltage Source Inverters

Currently, a two-level voltage source inverter (2L-VSI) is regarded as the cornerstone of modern industrial applications. However, the control of VSIs is a challenging task due to their nonlinear and time-varying nature. This paper proposes employing the fractional-order controller (FOC) to improve the performance of model-free predictive control (MFPC) of the 2L-VSI voltage control in uninterruptible power supply (UPS) applications. In the conventional MFPC based on the ultra-local model (ULM), the unknown variable that includes all the system disturbances is estimated using algebraic identification, which is insufficient to improve the prediction accuracy in the predictive control. The proposed FO-MFPC uses fractional-order proportional-integral control (FOPI) to estimate the unknown function associated with the MFPC. To get the best performance from the FOPI, its parameters are optimally designed using the grey wolf optimization (GWO) approach. The number of iterations of the GWO is 100, while the grey wolf’s number is 20. The proposed GWO algorithm achieves a small fitness function value of approximately 0.156. In addition, the GWO algorithm nearly finds the optimal parameters after 80 iterations for the defined objective function. The performance of the proposed FO-MFPC controller is compared to that of conventional MFPC for the three loading cases and conditions. Using MATLAB simulations, the simulation results indicated the superiority of the proposed FO-MFPC controller over the conventional MFPC in steady state and transient responses. Moreover, the total harmonic distortion (THD) of the output voltage at different sampling times proves the excellent quality of the output voltage with the proposed FO-MFPC controller over the conventional MFPC controller. The results confirm the robustness of the two control systems against parameter mismatches. Additionally, using the TMS320F28379D kit, the experimental verification of the proposed FO-MFPC control strategy is implemented for 2L-VSI on the basis of the Hardware-in-the-Loop (HIL) simulator, demonstrating the applicability and effective performance of our proposed control strategy under realistic circumstances.