Predictive Control Algorithm Research Articles

Interacting multiple model-based yaw stability control for distributed drive electric vehicle via adaptive model predictive control algorithm

The vehicle lateral stability control is a challenging problem due to the tire nonlinearity and the immeasurable sideslip angle. Thus, an adaptive model predictive control (AMPC) scheme based on interacting multiple model (IMM) vehicle sideslip angle observer is proposed in this paper. First, the observer is composed of the Extended Kalman filter (EKF) and the Unscented Kalman filter (UKF), which improves the real-time performance as well as the observation accuracy. Then, a multi-objective controller based on adaptive vehicle model prediction is designed using model predictive control algorithm. This controller aims to achieve a balance between the actuation and state constraints. The T-S fuzzy algorithm is used to observe the tire cornering stiffness and design an adaptive vehicle model. By utilizing the appropriate objective function and a quadratic programing solver, the controller output is obtained to achieve vehicle stability control. Finally, the effectiveness of the designed AMPC scheme under various working conditions is verified by Carsim-Simulink joint simulation platform and hardware-in-the-loop (HIL) test.

Model Predictive Control based on Long-Term Memory neural network model inversion

Long Short-Term Memory (LSTM) neural networks are well suited for representing time series as, compared to other neural networks, their structure avoids vanishing or exploding gradients. LSTM has been embedded into Model Predictive Control algorithms in order to forecast the behavior of nonlinear systems. The new algorithm presented in the paper is of a different nature, as the LSTM network approximates the inverse of the system over a receding horizon and provides a sequence of future inputs as a function of a specified output trajectory. The main advantage of the method appears when the desired output trajectory is generated from a small set of parameters, for example, a convergence rate. The Model Predictive control optimizes its criterion with respect to this small set of variables, and the LSTM supplies the corresponding future control inputs. Eventually, the modeling error of the LSTM can be compensated by feeding the control sequence to the forward model and updating the controller according to the output deviation. The algorithm allows to design Model Predictive controllers for nonlinear systems in a generic way, using a very small number of decision variables even with a long receding horizon.

A dynamic target tracking framework of UGV for UAV recovery under random disturbances

PurposeDynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.Design/methodology/approachTo mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.FindingsTo validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.Originality/valueThis paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Optimal predictive voltage control of a wind driven five phase PMSG system feeding an isolated load

Wind energy conversion systems (WECS) are considered to be a promising alternative to traditional power generation systems. The principal purpose of this study is to enhance the performance of a stand-alone wind-driven five-phase PMSG system. The initial step involves providing a clear explanation of the system model, which includes the wind turbine, five-phase PMSG, and a battery storage system. Then, the maximum power point tracking (MPPT) and pitch angle control (PAC) technique is employed to optimize the power delivered to an isolated load. Furthermore, the system incorporates the machine-side converter (MSC) and battery converter, which require control to efficiently regulate the power delivered from them to an isolated load. In addition, the MSC is regulated by two predictive control algorithms: predictive torque control (PTC) and a newly formulated predictive voltage control (PVC). The PTC faces limitations such as considerable ripple, significant load commutation, and the weighting factor of its cost functions. The proposed prediction technique aims to overcome these constraints by using a simple cost function and eliminating the need for weighting factors to address stability issues also another study's objective revolves around proposing a PVC controller that achieves an optimal design. The results demonstrate that the newly proposed predictive controller outperforms the conventional control method for the wind generator. The developed controller produces consistently distributed sinusoidal currents with significantly fewer ripples and current harmonics. This fact is verified mathematically as the total harmonic distortion (THD) is reduced to 1.168 % as an average value.

Energy optimization algorithms for multi-residential buildings: A model predictive control application

This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases.The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump, the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%.Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation.

Estimation-based model predictive control with objective prioritization for mutually exclusive objectives: Application to a power plant

This work presents an algorithm for estimation-based model predictive control with objective prioritization such that distinct objectives may be defined for mutually exclusive operational regions. The objective prioritization algorithm is built by using logical conditions that define regions of operation which are incorporated into the objective function, thus allowing smooth transitions between a bank of objectives. The control objective prioritization is cast in the framework of model predictive control that is coupled with an extended Kalman filter for estimation of critical yet unmeasured state variables. The algorithm is applied to the challenging control problem of an industrial superheater (SH)-reheater (RH) system of a natural gas combined cycle plant under load following operation where smooth transitions among various control objectives is desired – operation under nominal conditions, avoidance of spraying to saturation at the inlet of the SH and RH systems, and avoidance of main steam temperature excursions. The results from the estimator framework are compared with the industrial data from an operating power plant. The control algorithm is evaluated by simulating a servo control problem and disturbance rejection scenarios as expected under load-following operation of the power plant. This algorithm is generic and can be applied to accomplish local control policies for safety, economics, quality control, state constraints, and others. Topical HeadingProcess Systems Engineering

Hybrid model predictive control techniques for safety factor profile and stored energy regulation while incorporating NBI constraints

A novel hybrid Model Predictive Control (MPC) algorithm has been designed for simultaneous safety factor (q) profile and stored energy (w) control while incorporating the pulse-width-modulation constraints associated with the neutral beam injection (NBI) system. Regulation of the q-profile has been extensively shown to be a key factor for improved confinement as well as non-inductive sustainment of the plasma current. Simultaneous control of w is necessary to prevent the triggering of pressure-driven magnetohydrodynamic instabilities as the controller shapes the q profile. Conventional MPC schemes proposed for q-profile control have considered the NBI powers as continuous-time signals, ignoring the discrete-time nature of these actuators and leading in some cases to performance loss. The hybrid MPC scheme in this work has the capability of incorporating the discrete-time actuator dynamics as additional constraints. In nonlinear simulations, the proposed hybrid MPC scheme demonstrates improved q-profile+w control performance for NSTX-U operating scenarios.

A Precise Temperature Control Method for Lithium-ion Battery Pack based on the Nonlinear Model Predictive Control Algorithm

Abstract To utilize the maximum performance of the battery while ensuring its thermal safety, a battery thermal management system is used to control the battery maximum temperature within a safe range. This paper centres on the establishment of a temperature prediction model and the development of the nonlinear-based model predictive control (MPC) strategy. First, to address the need of predicting battery temperature, this paper develops a distributed parameter thermal resistance model to predict battery temperature quickly and accurately. Secondly, the open-loop formulation of the nonlinear-based MPC is derived based on the established state space equations. Then the soft and hard constraints of the model are established based on the actual current conditions, pump conditions, temperature difference and temperature rise indexes, so as to establish the objective function of the MPC algorithm. Finally, the established temperature nonlinear MPC algorithm is embedded on the board and the hardware platform of battery liquid cooling system is established. The experiment test result shows that the maximum error of temperature control is less than 0.1°C, and the effectiveness of the temperature control strategy of lithium-ion battery is verified through experiments.

Control-oriented dynamic modeling and GPC for single-tower double-circulation wet flue gas desulfurization system

In China, the current situation of large-scale grid integration of renewable energy generation and the government's introduction of capacity-based electricity pricing policies have compelled coal-fired power plants to gradually enhance their load adjustment capabilities and expand their load adjustment ranges. Simultaneously, the national environmental requirements for these plants have become increasingly stringent. However, the economic control of wet flue gas desulfurization (WFGD) systems is still far from meeting practical requirements. In this paper, starting from practical engineering applications and utilizing historical operational data, considering the actual system operation process, the impact of the absorption tower variable-frequency slurry circulation pump on the concentration of sulfur dioxide (SO2) in flue gas under different slurry pH values is investigated. This led to the establishment of a dynamic model for a single-tower double-circulation wet flue gas desulfurization (SD-WFGD) system. Recognizing the significant delay and inertia characteristics of pH value control in the SO2 absorption tank, a desulfurization system pH value desulfurization capacity-based optimization control scheme for the SD-WFGD system is proposed, termed as the Direct Sulfur Control (DSC) strategy. Based on the established dynamic model, a SD-WFGD system series control system is developed using a step generalized predictive control algorithm (GPC) combined with the DSC strategy. Validation results indicate that, compared to SGPC control schemes and traditional PID control schemes, this approach reduces fluctuations in slurry supply and outlet SO2 concentration, significantly enhancing the control performance of the SD-WFGD system. This ensures the safe, stable, economical, and flexible operation of the desulfurization system against the backdrop of flexible operation in coal-fired power plants.

Smart roofs featuring predictive control: An upgrade for mitigating precipitation extreme-induced pluvial floods

In the face of escalating urban pluvial floods exacerbated by climate change, conventional roof systems fall short of effectively managing precipitation extremes. This paper introduces a smart predictive solution: the Smart Internal Drainage Roof (SIDR) system, which leverages forecasted data to enhance the mitigation of pluvial floods in Central Business District (CBD) areas. Unlike traditional approaches, SIDRs utilize a synergistic combination of Rule-based Control (RBC) and Model Predictive Control (MPC) algorithms, tailored to optimize the operational efficiency of both grey and green roofs. Within the examined 1.3 km2 area in Beijing, China, SIDRs, covering 11% of the site, decreased total flooded areas by 30%–50% and eliminated 60%–100% of high-risk zones during three actual events. Moreover, SIDRs streamlined outflow processes without extending discharge time and reduced flood duration at a high-risk underpass by more than half. The SIDR's distinct features, including a high control resolution of 5 min, integration with existing waterproofs, and advanced 2D dynamic runoff visualization, position it as a scalable and cost-efficient upgrade in urban flood resilience strategies.

Optimal hybrid strategy in adaptive cruise control system for enhanced autonomous vehicle stability and safety

The adaptive cruise control system relies heavily on the vehicle's longitudinal control algorithm. Traditional longitudinal control algorithms typically depend on precise vehicle dynamic modeling or complex controller parameter calibrations. In the research, an optimal hybrid strategy for the control of basic adaptive cruise control system is proposed. The proposed hybrid strategy is the combination of both the Model Predictive Control (MPC) and Coati Optimization Algorithm (COA) and hence it is named as MPCCOA strategy. The fundamental goal of the proposed work is that a vehicle should maintain its current velocity when it approaches another vehicle that is driving at a steady velocity. The inputs for the proposed strategy are vehicle speed and acceleration. The inter-vehicle distance error, velocity error and acceleration are regulated based on the upper and lower limit of acceleration command with the help of proposed strategy. For accomplishing the required steadiness with the limitations in the given input and actual velocity with constant previous vehicle velocity, the proposed controller is utilized. The proposed controller is used to control the traction force of the host vehicle such that the vehicle speed follows the optimal speed trajectory as good as possible while ensuring constraints like distance to a preceding vehicle or speed limits. The performance of the proposed is implemented in the MATLAB platform and compared with existing approaches. The proposed technique demonstrates superior performance metrics, achieving the collision rate, rate of reaching maximum deceleration and ratio of exceeding comfortable deceleration or jerk are 1.27 %, 3.131 % and 9.052 %, respectively.

Neural network model predictive control of core power of Qinshan nuclear power plant based on reinforcement learning

The power change of Pressurized water reactors (PWRs) is a highly complex nonlinear coupling process, which requires the power controller to have a high nonlinear control capability. Since the traditional model predictive control (MPC) algorithm has the problems of insufficient robustness and adaptivity, this study proposes a neural network-based neural network model predictive control method based on model predictive control and optimizes its control parameters in real time by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. The designed control method is validated on the simulation model of the Qinshan nuclear power plant (NPP). Simulation results show that the proposed control method can realize effective control of power under a variety of power changing conditions and dynamic adjustment of control parameters.

Hybrid Advanced Control Strategy for Post-Combustion Carbon Capture Plant by Integrating PI and Model-Based Approaches

Even though the energy penalties and solvent regeneration costs associated with amine-based absorption/stripping systems are important challenges, this technology remains highly recommended for post-combustion decarbonization systems given its proven capture efficacy and technical maturity. This study introduces a novel centralized and decentralized hybrid control strategy for the post-combustion carbon capture plant, aimed at mitigating main disturbances and sustaining high system performance. The strategy is rooted in a comprehensive mathematical model encompassing absorption and desorption columns, heat exchangers and a buffer tank, ensuring smooth operation and energy efficiency. The buffer tank is equipped with three control loops to finely regulate absorber inlet solvent solution parameters, preventing disturbance recirculation from the desorber. Additionally, a model-based controller, utilizing the model predictive control (MPC) algorithm, maintains a carbon capture yield of 90% and stabilizes the reboiler liquid temperature at 394.5 K by manipulating the influent flue gas to the lean solvent flowrates ratio and the heat duty of the reboiler. The hybrid MPC approach reveals efficiency in simultaneously managing targeted variables and handling complex input–output interactions. It consistently maintains the controlled variables at desired setpoints despite CO2 flue gas flow disturbances, achieving reduced settling time and low overshoot results. The hybrid control strategy, benefitting from the constraint handling ability of MPC, succeeds in keeping the carbon capture yield above the preset minimum value of 86% at all times, while the energy performance index remains below the favorable value of 3.1 MJ/kgCO2.

Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty

The rising demands for comfort alongside energy conservation underscore the importance of intelligent air conditioning control systems. Model Predictive Control (MPC) stands out as an advanced control strategy capable of addressing these demands. However, accurate prediction of all relevant variables remains a challenge in practical scenarios, complicating MPC’s ability to devise effective control actions amid prediction inaccuracies. To counteract this issue, this paper introduces an enhanced Double-Layer Model Predictive Control (DLMPC) algorithm. This innovative approach adjusts for discrepancies between forecasted and actual values without the need for additional variables and models, thereby reducing the adverse effects of prediction errors. Additionally, we develop precise models for room temperature simulation and for calculating air conditioning (AC) load and energy consumption, grounded in empirical data from residential settings and AC performance tests. Validation of these models demonstrates their efficacy in enabling MPC to formulate efficacious control strategies. When juxtaposed with a baseline model, the DLMPC algorithm significantly improves temperature regulation accuracy by up to 15.12% and achieves a 10.50% reduction in energy consumption over the heating season.

Event-Triggered Predictive Control Algorithm for Multi-AUV Formation Modeling

Event-Triggered Predictive Control Algorithm for Multi-AUV Formation Modeling

Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.

Convex-Optimization-Based Model Predictive Control for Space Debris Removal Mission Guidance

A convex-optimization-based model predictive control (MPC) algorithm for the guidance of active debris removal missions is proposed in this work. A high-accuracy reference for the convex optimization is obtained through a split-Edelbaum approach that takes the effects of J2, drag, and eclipses into account. When the spacecraft deviates significantly from the reference trajectory, a new reference is calculated through the same method to reach the target debris. When required, phasing is integrated into the transfer. During the mission, the phase of the spacecraft is adjusted to match that of the target debris at the end of the transfer by introducing intermediate waiting times. The robustness of the guidance scheme is tested in a high-fidelity dynamic model that includes thrust errors and misthrust events. The guidance algorithm performs well without requiring successive convex iterations. Monte Carlo simulations are conducted to analyze the impact of these thrust uncertainties on the guidance. Simulation results show that the proposed convex-MPC approach can ensure that the spacecraft can reach its target despite significant uncertainties and long-duration misthrust events.

An improved gain-scheduling robust MPC for path following of autonomous independent-drive electric vehicles with time-varying and uncertainties

This paper proposes a gain-scheduling robust model predictive control (GS-RMPC) algorithm for the path-following problem of autonomous independent-drive electric vehicles (AIDEVs) with consideration of time-varying and uncertainties. Firstly, the polytopic uncertainty method and norm-bounded uncertainty method are introduced to characterise the vehicle dynamics model. Secondly, the infinite predict horizon optimisation process of online GS-RMPC is transformed into a series of linear matrix inequalities (LMIs) by minimising the worst-case objective function while considering all scheduling states in the polytope. Thirdly, an offline solution is also proposed to reduce the computational burden based on asymptotically stable invariant ellipse sets. Then, a hierarchical control structure is proposed to distribute the additional yaw moment, and a multi-step predictor is designed to compensate for the actuator time delay. Finally, the hardware-in-the-loop (HIL) testing is conducted to verify the efficacy of the proposed strategy.

Revolutionizing motor dysfunction treatment: A novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control

Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83 % between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model.

Prediction of residual performance of corroded RC beams from observed surface cracks by 3D RBSM with MPC

Corrosion of reinforcing steel in concrete structures can significantly compromise their performance. Accurately estimating this deterioration for in-service structures is challenging due to uncertainties surrounding the distribution of internal corrosion and cracking patterns, compounded by the limitations of destructive testing. This research proposes a novel approach to predict the remaining load-bearing capacity of corroded reinforced concrete (RC) structures based on the pattern of surface cracks caused by corrosion. The method integrates a model predictive control (MPC) algorithm with a three-dimensional Rigid Body Spring Model (3D RBSM) to estimate internal corrosion levels from observed cracks and a normal 3D RBSM simulation to predict the residual performance. The proposed system is validated through analysis of four corroded beam specimens based on experimental work reported in the literature. In use, the MPC algorithm first estimates a corrosion distribution in a RC beam and generates simultaneous corrosion-induced crack and stress predictions. This information is then used to predict residual load-bearing capacity. The system accurately forecasts the non-uniform corrosion distribution, flexural capacity, and load-displacement curves of a specimen, offering insight into the performance differences among the specimens and demonstrating its potential to assess the serviceability of existing RC structures with corrosion issues.