Hierarchical Predictive Control Research Articles

Hierarchical Predictive Optimal control for Range Extension of EV with ANN based Torque Control for IPMSM drives

This study presents a novel methodology to enhance the energy efficiency of Electric Vehicles (EVs) while maintaining dynamic stability and driving comfort, even on uneven roads, using onboard vehicle measurements. The approach involves a hierarchical control scheme that integrates a model predictive controller with a torque vectoring algorithm to optimize torque demand and accurately anticipate road-specific torque requirements. The control is implemented on EV with Interior Permanent Magnet Synchronous Motors (IPMSM) as four wheel drives. The optimal torque control is realised through an Artificial Neural Network (ANN)- based motor torque control scheme. The design is validated through tests in real-world driving scenarios. In comparison with conventional methods, the proposed method shows a 37% increase in energy efficiency across different test conditions, thereby resulting in an increase in EV driving range. These advancements are achieved without substantial modifications to the EV’s drivetrain, representing a significant step forward in sustainable and efficient electric mobility.

A Hierarchical Data-Driven Predictive Control of Image-Based Visual Servoing Systems With Unknown Dynamics.

In this article, a hierarchical predictive control (PC) algorithm is designed for visual servoing mobile robot systems. At the kinematic level, the image-based visual servoing model of a wheeled mobile robot is established. By defining the corresponding performance index of the PC, an iterative linear quadratic regulator (iLQR) is used to obtain the velocity controller and to provide reference velocity for dynamics. In dynamics, a data-driven PC controller based on the Gaussian process (GP) is proposed to obtain the torque controller with unknown dynamics. The input-to-state practical stability (ISpS) of the system based on the proposed data-driven PC method is proved by introducing reasonable assumptions. The corresponding theorem also analyzes the maximum upper bound of GP inference error. Finally, the effectiveness of the proposed hierarchical controller is verified by simulations and experiments.

Performance Comparison Between Centralized and Hierarchical Predictive Functional Control for Adaptive Cruise Control Application

This paper presents a performance comparison between two different control structures of Predictive Functional Control (PFC), namely centralized and hierarchical architectures for an Adaptive Cruise Control (ACC) system. Centralized PFC utilizes a linearized model of the vehicle longitudinal dynamics to compute the control law, while hierarchical PFC uses a simple kinematic model with inverse vehicle longitudinal dynamics. Based on the simulation results, both control structures produced a satisfactory performance. However, in the presence of disturbances such as road slopes and wind gusts, the centralized PFC becomes more sensitive and conservative than the hierarchical PFC. The main reason is that the linearization model used in centralized PFC is only effective around the selected nominal operating points. Since the decision-making process depends on the internal model performance, centralized PFC cannot compensate for this effect. Besides, it also takes a longer computation time than hierarchical PFC since more mathematical operations must be solved than the kinematic model, especially when physical and operational constraints are considered. The standard performance parameters such as Root Mean Squared Error (RMSE), settling time, and rise time are also used for the analysis. These findings can become a solid justification for using the hierarchical PFC structure in designing an ACC system for future work.

Hierarchical Control of Megawatt-Scale Charging Stations for Electric Trucks With Distributed Energy Resources

Electrifying medium- and heavy-duty trucks is critical to decarbonizing the transportation sector. Energy needs of electric trucks will likely require megawatt-scale charging stations, which could significantly stress the electric distribution grid. Distributed energy resources (DER) can alleviate this stress and reduce charging costs with proper management. To that end, this work develops a hierarchical predictive control algorithm for future multi-port megawatt-scale charging stations that can provide real-time energy management for stations, decide charging rates, dispatch energy storage system (ESS), and provide grid voltage support. We integrate three algorithmic components: (i) an energy management optimization (EMO) that provides supervisory control to DER assets and charging loads at minute scale, (ii) a real-time energy management system (RT-EMS) that heuristically compensates for fast disturbances at sub-second scale, and (iii) a model predictive control (MPC)-based battery management system (BMS) that communicates future charging demands to the EMO, to manage the overall megawatt-scale site. Validation in a controller hardware-in-the-loop (CHIL) environment shows that the hierarchical controller can reduce the total energy consumption from the grid by approximately 28% compared to an uncontrolled case for the station configuration in this paper, without impacting charging time.

Hierarchical Predictive Control of an Unmanned Aerial Vehicle Integrated Power, Propulsion, and Thermal Management System

Increasing electrification of air vehicles presents challenges to the energy management of their coupled electric power, propulsion, and thermal systems. Notably, the complexity and multi-timescale nature of this class of systems make it difficult to design control algorithms that can simultaneously optimize dynamics across each energy domain. The unique contribution of this work is the design and experimental validation of a hierarchical model predictive controller that coordinates the electro-mechano-thermal dynamics of an unmanned aerial vehicle (UAV) integrated power, propulsion, and thermal management system. To support this contribution, a novel UAV energy system testbed is introduced. A graph-based framework is used to model the multi-domain dynamics of the UAV. To estimate the states of the experimental platform, a decentralized observer is described. When compared to a baseline approach, the hierarchical control strategy results in a higher performing and more reliable closed-loop system while decreasing fuel utilization by approximately 16%.

Study on the Hierarchical Predictive Control of Semiconductor Silicon Single Crystal Quality Based on the Soft Sensor Model

Silicon single crystal (SSC) quality monitoring and control has been a hot research topic in the field of the Czochralski crystal growth process. Considering that the traditional SSC control method ignores the crystal quality factor, this paper proposes a hierarchical predictive control strategy based on a soft sensor model for online control of SSC diameter and crystal quality. First, the proposed control strategy considers the V/G variable (V is the crystal pulling rate, and G is the axial temperature gradient at the solid-liquid interface), a factor related to crystal quality. Aiming at the problem that the V/G variable is difficult to measure directly, a soft sensor model based on SAE-RF is established to realize the online monitoring of the V/G variable and then complete hierarchical prediction control of SSC quality. Second, in the hierarchical control process, PID control of the inner layer is used to quickly stabilize the system. Model predictive control (MPC) of the outer layer is used to handle system constraints and enhance the control performance of the inner layer. In addition, the SAE-RF-based soft sensor model is used to monitor the crystal quality V/G variable online, thereby ensuring that the output of the controlled system meets the desired crystal diameter and V/G requirements. Finally, based on the industrial data of the actual Czochralski SSC growth process, the effectiveness of the proposed crystal quality hierarchical predictive control method is verified.

Hierarchical Model Predictive Control to Coordinate a Vehicle-to-Grid System Coupled to Building Microgrids

Aiming to take full advantage of Electric Vehicles’ (EVs) batteries, this paper proposes a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in Building Microgrids (BMGs). This paper provides a complete framework for the design of this control structure and analyses its performance regarding the state of charge of the EVs at departure time, the self-consumption rate, and the coverage rate, considering a residential BMG equipped with photovoltaic panels and static Li-ion batteries. The results and performance of the proposed control architecture are compared to two other solutions: a hierarchical predictive controller with no scheduler and a rule-based algorithm. A technological and economical study is also performed considering variables such as the dimension of the EV's park, the price of energy, the cost of maintenance, the possibility to discharge or not into the grid, and the execution time of the control architecture. The simulation results conducted in MATLAB Simulink demonstrated that the proposed control structure ensures the full charging of all vehicles at departure time while also improving the self-consumption rate of the BMG with a relatively low stress on the needed computation capacities, even when considering a large fleet of vehicles.

Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales

To improve the control of active power in wind power clusters, an active power hierarchical predictive control method with multiple temporal and spatial scales is proposed. First, the method from the spatial scale divides the wind power clusters into the cluster control layer, sub-cluster coordination layer and single wind farm power regulation layer. Simultaneously, from the temporal scale, the predicted data are divided layer by layer: the 15 min power prediction is deployed for the first layer; the 5 min power prediction is employed for the second layer; the 1 min power prediction is adopted for the third layer. Secondly, the prediction model was developed, and each hierarchical prediction was optimized using MPC. Thirdly, wind farms are dynamically clustered, and then the output power priority of wind farms is established. In addition, the active power of each wind farm is controlled according to the error between the dispatch value and the real-time power with feedback correction so that each wind farm achieves cooperative control with optimal power output. Finally, combined with the simulation of practical wind power clusters, the results show that the wind abandonment rate was reduced by 2.13%, and the dispatch of the blindness was overcome compared with the fixed proportional strategy. Therefore, this method can improve the efficiency of cooperative power generation.

Hierarchical Predictive Control to Frequency of DFIG‐Integrated Power System Based on Improved Dynamic Power Flow

For the power system integrated with the doubly‐fed induction generators (DFIGs), the frequency regulation may be provided by the DFIGs in addition to the synchronous generators (SGs). With the model predictive control (MPC), the active power output of the DFIG is adjusted by predicting the system frequency with the dynamic power flow (DPF) algorithm. In this paper, the governor delay of the SGs is included to improve the accuracy of the DPF‐based prediction model. The DPF predicts the frequency response with the desired active power response of the DFIG. To reach the reference with the high accuracy and efficiency, the MPC‐based direct rotor current control is applied to adjust the active power output in coordination with the frequency regulation, forming the proposed hierarchical frequency control strategy. The mechanical transient of the DFIGs involving the shaft and pitch angle system is included in the DPF model to introduce the mechanical constraints of the DFIGs to the MPC. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Design and Application of Hierarchical Multi-objective Predictive Control for Continuous Flow Stirred Tank Reactor

Design and Application of Hierarchical Multi-objective Predictive Control for Continuous Flow Stirred Tank Reactor

Anti-disturbance composite tracking control for a coupled two-tank MIMO process with experimental studies

Coupled multiple-tank systems are very important for a wide range of industrial applications due to their unique uses. However, the liquid level control for the coupled two-tank multi-input multi-output (MIMO) system is quite challenging because it has strong nonlinearity and coupling, and it is susceptible to multiple external disturbances. For this process, this paper proposes a novel anti-disturbance control strategy consisting on a nonlinear composite hierarchical anti-disturbance predictive control (CHADPC). First, a model-based explicit nonlinear model predictive controller (ENMPC) is designed assuming that all disturbances are measurable and its global exponential stability is proved. Then, a nonlinear disturbance observer (DO) is designed to estimate the lumped disturbances. The composite controller handling the estimated disturbances is then proposed. Finally, simulation and experimental tracking control tests under perturbations and comparisons with recently reported works have been carried out to highlight the promising performance of the proposed ENMPC and CHADPC schemes.

Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty

In recent years, the increasing penetration of non-programmable renewable energy technologies has made energy system flexibility essential for ensuring power grid stability, in terms of balance between power demand and supply. In particular, cogeneration plants can provide flexibility services by making a defined amount of power available at a given schedule, upon request. The grid operator can request it or not depending on the actual dispatch management. In the presence of this uncertain request, producers need smart controllers to meet thermal demand and, at the same time, to maximize profit while fulfilling the needs of the grid. This work presents a hierarchical predictive control architecture for optimal management of combined heat and power production sites equipped with a thermal energy storage tank supplying district heating networks. The feasibility of the proposed approach is investigated by simulating several scenarios with different degrees of uncertainty about the actual power request from the grid operator. It is shown that the controlled system is able to comply with the requirements to the maximum extent when the exact power dispatch is known a few hours in advance, also giving a profit increase as well as environmental benefits. While the grid operator request for flexibility is met up to 100%, the reduction in carbon emissions ranges from 3% to 7% and the operating margin is increased by 20%. This can be realized when the power availability is scheduled at periods of both maximum and minimum heat demand. Thus, the method is promising also in improving production planning of future integrated energy systems from the perspective of grid flexibility.

Hierarchical predictive control for electric vehicles with hybrid energy storage system under vehicle-following scenarios

For electric vehicles with hybrid energy storage system, driving economy depends not only on novel energy management strategies but also on load power demand. In order to optimize the power demand and energy management simultaneously, this paper proposes a hierarchical model predictive control framework for electric vehicles with a Li-ion battery/supercapacitor hybrid energy storage system under vehicle-following scenarios. In the vehicle-following level, based on vehicle-to-vehicle and vehicle-to-infrastructure communications, the following vehicle can acquire the real-time velocity and position of the preceding vehicle, optimize the motor electricity consumption, and ensure driving safety through velocity planning. Such cost-effective power demand is further allocated in the energy management level, in order to minimize battery degradation and power losses. Urban, suburban, and highway driving conditions are tested to evaluate the effectiveness and robustness of the proposed method. Determination of prediction horizon and detailed comparison with existing methods are investigated. Simulation results show that compared with optimizing energy management alone under a classical car-following model, the proposed method can reduce the total operation cost by 4.69–14.55% and yield results closer to offline dynamic programming, which provides the globally optimal results.

Learning-based hierarchical control of water reservoir systems

The optimal control of a water reservoir system represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear model through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach outperforms stochastic dynamic programming.

Designing a hierarchical model-predictive controller for tracking an unknown ground moving target using a 6-DOF quad-rotor

In this paper, a hierarchical predictive controller is designed in order to solve the tracking problem of a moving ground target by a quad-rotor in an unknown and uneven environment. This controller has internal and external predictive controller levels. In the lower layer of the controller, a constrained predictive controller is designed that is capable of rejecting perturbations and quickly tracking the reference path, and in the outer loop, a model predictive controller is designed to optimally detect the moving ground target where, the sub-cost functions were defined so that the quad-rotor would be able to track the moving ground target even if it was temporarily out of sight of the flying robot, as well as in the event of sudden direction changes and uneven path. In this method, since there is no need to switch between different controllers and not having to engage in delusional maneuvers of the target, the total control effort is reduced, which was shown in the simulations by the indifference reactions and shortening of the path of the quad-rotor. Finally, the tracking problem is simulated in the MATLAB to show the effectiveness of the proposed controllers, and the results are compared with that of a benchmark controller.

Predictive Hierarchical Control of Power Flow in Large-Scale PV Microgrids With Energy Storage

Large-scale PV microgrids constitute a growing portion of distributed renewable generation in many grids worldwide and are expected to continue their growth in the foreseeable future. Managing the operation of such microgrids and in particular their interaction with the main electricity grid is a challenging task as it involves controlling large intermittent PV resources. The addition of auxiliary DERs and energy storage systems to such microgrids is a promising solution that provides some flexibility to the operation of these fluctuating PV resources. In this work, a hierarchical predictive controller is designed to perform daily scheduling and real-time control of a PV microgrid with energy storage and auxiliary diesel generation. The controller aims to increase utilization of available PV resources, reduce the daily variability of power flow to the main grid, while at the same time controlling power fluctuations at the point of connection. The controller is designed to utilize updating load and PV predictions on a receding time horizon and uses the flexibility of energy storage and diesel resources to compensate for fluctuations and prediction errors. A study of multiple operational scenarios demonstrates successful performance of the proposed controller.

Hierarchical Predictive Control of a Combined Engine/Selective Catalytic Reduction System with Limited Model Knowledge

Hierarchical Predictive Control of a Combined Engine/Selective Catalytic Reduction System with Limited Model Knowledge

The ConDialInt Model: Condensation, Dialogality, and Intentionality Dimensions of Inner Speech Within a Hierarchical Predictive Control Framework.

Inner speech has been shown to vary in form along several dimensions. Along condensation, condensed inner speech forms have been described, that are supposed to be deprived of acoustic, phonological and even syntactic qualities. Expanded forms, on the other extreme, display articulatory and auditory properties. Along dialogality, inner speech can be monologal, when we engage in internal soliloquy, or dialogal, when we recall past conversations or imagine future dialogs involving our own voice as well as that of others addressing us. Along intentionality, it can be intentional (when we deliberately rehearse material in short-term memory) or it can arise unintentionally (during mind wandering). We introduce the ConDialInt model, a neurocognitive predictive control model of inner speech that accounts for its varieties along these three dimensions. ConDialInt spells out the condensation dimension by including inhibitory control at the conceptualization, formulation or articulatory planning stage. It accounts for dialogality, by assuming internal model adaptations and by speculating on neural processes underlying perspective switching. It explains the differences between intentional and spontaneous varieties in terms of monitoring. We present an fMRI study in which we probed varieties of inner speech along dialogality and intentionality, to examine the validity of the neuroanatomical correlates posited in ConDialInt. Condensation was also informally tackled. Our data support the hypothesis that expanded inner speech recruits speech production processes down to articulatory planning, resulting in a predicted signal, the inner voice, with auditory qualities. Along dialogality, covertly using an avatar’s voice resulted in the activation of right hemisphere homologs of the regions involved in internal own-voice soliloquy and in reduced cerebellar activation, consistent with internal model adaptation. Switching from first-person to third-person perspective resulted in activations in precuneus and parietal lobules. Along intentionality, compared with intentional inner speech, mind wandering with inner speech episodes was associated with greater bilateral inferior frontal activation and decreased activation in left temporal regions. This is consistent with the reported subjective evanescence and presumably reflects condensation processes. Our results provide neuroanatomical evidence compatible with predictive control and in favor of the assumptions made in the ConDialInt model.

A hierarchical predictive control for supercapacitor-retrofitted grid-connected hybrid renewable systems

This paper presents a two-layer control strategy designed for easy integration of supercapacitors in a grid-integrated solar photovoltaic-battery hybrid renewable system, initially controlled by a typical model predictive control method. To operate the upgraded energy system, either without or with little modifications of the pre-existing architecture, an additional control layer is applied at the bottom of the original control system. Considering the complementary characteristics of batteries and supercapacitors, the design of the new model predictive control layer and its coordination with the original one help to deliver a stable power flow between the hybrid renewable system and the utility grid, and remove fast variations from the battery power. Actual measurements of solar radiation in South Africa are used to test the effectiveness of the proposed strategy. Simulations carried out on a 1-MW photovoltaic plant confirm the benefits in terms of adherence to power quality regulations, improved conditioning of the power generated by the intermittent renewable sources, and lifetime extension of the battery.

Hierarchical Model Predictive Control for Autonomous Vehicle Area Coverage

Abstract Area coverage using autonomous vehicles receives increasing attention due to a widespread range of possible applications. Examples are surveillance and monitoring tasks or search and rescue missions. Efficient and safe area coverage in dynamic environments, however, is challenging. It requires tight integration of the planning and control task to guarantee collision avoidance and optimal coverage. We propose a combination of two coupled model predictive controllers for optimal area coverage with dynamic obstacle avoidance. The planning is based on a mixed integer programming formulation of the predictive controller. It allows to take dynamic objects, such as other autonomous vehicles into account and considers a simplified dynamic model of the autonomous vehicle. The autonomous vehicle itself is controlled by a continuous time nonlinear model predictive path following controller, which obeys detailed dynamic and kinematic constraints and follows the provided path. The design of the controllers takes the interconnections in terms of dynamic constraints and reference definitions between them into account. Simulation results for a quadcopter illustrate the performance and real-time feasibility of the proposed hierarchical predictive control strategy.