Optimal Control Algorithm Research Articles

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

Off‐policy reinforcement learning algorithm for robust optimal control of uncertain nonlinear systems

AbstractIn this article an optimal control scheme is proposed to solve robust control problem for matched and unmatched system. In the proposed optimal approach the value functions are designed such that the obtained optimal control law guarantees asymptotic stability of the uncertain nonlinear system. Since the proposed robust optimal control problem is not straightforward to solve, an off‐policy reinforcement‐learning algorithm based on neural networks approximation is developed to obtain robust optimal control law iteratively. The robust control law for matched uncertain systems can be achieved via proposed off‐policy learning algorithm without requiring exact knowledge of system's dynamics. The advantages of the proposed robust optimal controller are verified by comparative simulations on an uncertain model of a car suspension system and a mathematical nonlinear model.

Boundary Optimal Control for Parabolic Distributed Parameter Systems With Value Iteration.

A reinforcement learning-based boundary optimal control algorithm for parabolic distributed parameter systems is developed in this article. First, a spatial Riccati-like equation and an integral optimal controller are derived in infinite-time horizon based on the principle of the variational method, which avoids the complex semigroups and operator theories. Using state data along the system trajectory, a value iteration algorithm via the Bellman optimality principle is proposed to obtain the solution of the spatial Riccati-like equation and the optimal control law. The convergence of the value iteration algorithm is proved. Subsequently, an approximation scheme based on weighted residuals is developed to implement the value iteration algorithm, where radial basis functions are chosen as the basic functions to approximate the solution of the spatial Riccati-like equation. Simulations on the diffusion-reaction process demonstrate the effectiveness of the developed method.

Recent advances in the precision control strategy of artificial pancreas.

The scientific diagnosis and treatment of patients with diabetes require frequent blood glucose testing and insulin delivery to normoglycemia. Therefore, an artificial pancreas with a continuous blood glucose (BG) monitoring function is an urgent research target in the medical industry. The problem of closed-loop algorithmic control of the BG with a time delay is a key and difficult issue that needs to be overcome in the development of an artificial pancreas. Firstly, the composition, structure, and control characteristics of the artificial pancreas are introduced. Subsequently, the research progress of artificial pancreas control algorithms is reviewed, and the characteristics, advantages, and disadvantages of proportional-integral-differential control, model predictive control, and artificial intelligence control are compared and analyzed to determine whether they are suitable for the practical application of the artificial pancreas. Additionally, key advancements in areas such as blood glucose data monitoring, adaptive models, wearable devices, and fully automated artificial pancreas systems are also reviewed. Finally, this review highlights that meal prediction, control safety, integration, streamlining the optimization of control algorithms, constant temperature preservation of insulin, and dual-hormone artificial pancreas are issues that require further attention in the future.

Adaptive optimal fault-tolerant vibration control and sensitivity analysis of semi-active suspension based on a self-powered magneto-rheological damper

To reuse the energy dissipated by vehicle suspension, a semi-active suspension with a self-powered magneto-rheological damper is proposed. An electromechanical coupling model of self-powered semi-active suspension is established. The energy conversion efficiency is defined and investigated by changing the electrical parameters. By considering unmodeled dynamics and perturbation values, an adaptive optimal fault-tolerant control algorithm is proposed to ensure the vibration-isolation performance. The robust index of the adaptive optimal fault-tolerant control algorithm is constructed using the Lyapunov equation and evaluated by changing the key parameters. The sensitivity of the key parameters to the damping force is investigated using a grey relation analysis approach. Furthermore, multi-objective optimization between the vibration-isolation capability and energy harvesting is conducted. Via analysis, the proposed suspension can harvest more energy near the second resonance range. Compared to passive control and self-powered mode, the adaptive optimal control algorithm mitigates vibration more significantly in the time and frequency domains, respectively, under stochastic excitation. The robust index is most sensitive to inductance and the diameter of the magnetism cylinder. The length of the damping channel and the diameter of the magnetism cylinder influence the sensitivity of key parameters to the damping force most obviously.

Drag-based analytical optimal de-orbiting guidance from low earth orbit via Deep Neural Networks

Controlled de-orbiting is crucial for a low earth orbiting (LEO) satellite or capsule intending to land in a desired location and to prevent damage to people and property on the ground caused by debris. Drag modulation is one possible mechanism to control de-orbiting, exploiting atmospheric drag variation to reduce the necessary orbital energy from the initial conditions to the re-entry interface. In this context, this paper proposes a novel targeted de-orbiting Artificial Neural Network (ANN) and drag-based guidance algorithm for a LEO artificial satellite. It relies on the combination of empirical and analytical relations with Deep Neural Networks (DNNs) to estimate the main parameters of the optimal control law in real time. The training set is composed of several optimal control solutions generated with a previously developed optimal control algorithm, exploiting the formulation in equinoctial modified orbital parameters to manage the large time scale of the problem and the computational cost. An innovative procedure for the training set generation led to the achievement of general results with a limited number of samples. The successful outcome of the guidance algorithm on over 1000 cases demonstrates its robustness and generalization of results. To conclude, a Linear Quadratic Regulator (LQR) feedback control is applied to one case to deal with a more realistic and uncertain density model.

Alternative Performance Indices-Based Control Technique for a Unity Power Factor Three-Phase Rectifier

The implementation of a unity power factor (UPF) three-phase rectifier has the potential to enhance the power factor (PF). However, the PF and total harmonic distortion (THD) experience degradation in low-output regions due to the utilization of a “critical input inductor” under rated load conditions. In this study, an analysis of the operation principle of a UPF three-phase rectifier is conducted and a reference compensation current technique based on alternative performance indices is proposed. The optimal control algorithm is utilized to calculate the single harmonic phase delay of each phase and determine the compensator gain, taking into account factors such as the single harmonic distortion limit, THD limit, PF, and active power consumption. The output of the current compensator serves as the desired current source, which in turn drives the bidirectional switches. The simulation and experimental results demonstrate that the proposed control technique can significantly mitigate power supply current distortion, and the input power factor has been effectively improved within a wider load range.

On the Inherent Distributional Robustness of Stochastic and Nominal Model Predictive Control

We define a notion of distributional robustness, via the Wasserstein metric, for closed-loop systems subject to errors in the disturbance distribution used to construct the controller. We then establish sufficient conditions for stochastic model predictive control (SMPC) to satisfy this definition of distributional robustness and establish a similar notion of distributional robustness for economic applications of SMPC. These results address incorrectly or unmodeled disturbances, demonstrate the efficacy of scenario optimization as a means to approximate and solve the SMPC problem, and unify the descriptions of robustness for stochastic and nominal model predictive control. This definition of distributional robustness for closed-loop systems is general and can be applied to other stochastic optimal control algorithms and, potentially, the developing field of distributionally robust control.

Comprehensive Review of Metaheuristic Algorithms (MAs) for Optimal Control (OCl) Improvement

Comprehensive Review of Metaheuristic Algorithms (MAs) for Optimal Control (OCl) Improvement

Optimization of basic PID control algorithm based on genetic algorithm and Matlab

This paper investigates how to optimize the basic PID algorithm by using population genetic forms. "In traditional PID control, the tuning of PID parameters mostly relies on the experience of the tuner. The main purpose of this paper is to use an algorithm that can automatically tune its parameters to self-tune a PID controller for an actual model." Based on the principle of genetic algorithm, this paper compares the traditional PID controller tuning methods to study the trend of the PID parameters during the iteration process as well as the trend of the PID controller adaptability. Through the principle of the algorithm and the iterative output, as well as in the code set the weights occupied by different performance indicators, it can not only flexibly adjust the degree of adaptation and make the algorithm more flexible, but also prove that the use of genetic algorithms for the rectification of the PID is more adaptable and convenient, with promising research prospects

Development and performance evaluation of intelligent algorithm for optimal control of a hybrid heat pump system during the cooling season

The purpose of this study is to develop a control method for a hybrid heat pump system based on an artificial neural network (ANN) to reduce energy use and create a more comfortable thermal environment. The proposed optimal control method uses an ANN-based predictive model to predict the heat storage tank and indoor temperature during the cooling period and controls the flow rate of the circulation pump on the heat source and load side of the system. The performance of the predictive model for the heat storage tank temperature (R2 = 0.9988; coefficient of variation of the root mean square error [CV(RMSE)] = 1.06 %; normalized mean bias error [NMBE] = 0.16 %; and mean absolute error [MAE] = 0.09℃) and the indoor temperature (R2 = 0.9893; CV(RMSE) = 1.66 %; NMBE = 0.16 %; and MAE = 0.15℃) was excellent. The temperature control of the heat storage tank using the optimal algorithm exhibited an improvement of 18.39 % for the CV(RMSE), 3.10 % for the NMBE, and 1.31 °C for MAE compared with rule-based. For the indoor temperature, the optimal algorithm improved the CV(RMSE) by 1.30 %, NMBE by 0.42 %, and MAE by 0.29℃ compared to rule-based. The energy use was reduced by 52.85 % for the entire system using the optimal control method compared with the existing control strategy under similar outdoor conditions. Using the proposed control method, it is thus possible to improve thermal comfort and reduce carbon emissions in the building sector by improving the control and energy performance of hybrid heat pump systems.

Experimental study of ion beam interaction with target surface aimed at developing a contactless method for space debris removal by an ion beam

Research and development in the field of space debris (SD) removal technologies is one of the most important and urgent tasks facing all spacefaring nations. By now, a large number of different concepts of removal have already been proposed. One of them is the SD removal by ion beam generated by an ion source installed on board a service spacecraft (SSC). A detailed understanding of the process of ion beam interaction with the SD surface is required to develop an optimal control algorithm for the virtual SSC-SD mated configuration in order to improve this method of SD removal. In addition, when designing the SSC, it is necessary to take into account back flows of the material sputtered from the surface of removed SD by the ion beam, which can contaminate the SSC operating surfaces during the removal maneuver. Within the framework of the presented work, experimental studies were performed to determine the coefficient of accommodation of the ion impulse during the ion beam interaction with the surface of the target simulating the external surface of the removed object. Besides, the flow density of the sputtered target material was experimentally evaluated during its exposure to the ion beam. To do this, we used the radio-frequency ion source (injector) with a beam diameter of 160 mm, which is supposed to be used as an actuating unit on board the SSC designed for transporting the SD objects to the disposal orbit or to the orbits with limited life. When measuring the ion beam accommodation coefficient, the presence of additional force arising on the target due to the flow of sputtered material leaving its surface, and the ion-electron emission process on the target, which could distort the experimental data, were taken into account. To estimate the magnitude of the flows of sputtered target material under the influence of the ion beam, a special experimental design was developed for simulating the presence of a point source of sputtered material. The results obtained in this work will be used for modeling the impact of the ion beam on the surface of real SD objects with complex geometry using the finite element method, for refining the control algorithm of the virtual SSC-SDO mated configuration and for developing proposals on the SSC layout, which should reduce or completely eliminate the negative impact associated with the material sputtering from the SDO surface onto the SSC elements. Successful implementation of the developed method of SDO removal will contribute to improving the safety of space activities for all countries and to the development of the rocket-and-space industry in the Russian Federation, in particular.

Mitigation of Power Quality Issue in Hybrid Renewable Energy Source by UPQC with Optimization Techniques

This paper presents a comprehensive approach to mitigate power quality issues in hybrid renewable energy systems through the utilization of Unified Power Quality Conditioner (UPQC) integrated with optimization techniques. As renewable energy sources like wind and solar play an increasingly significant role in power generation, ensuring high-quality power supply becomes imperative. The UPQC, comprising series and shunt active power filters, is strategically deployed to address voltage fluctuations, harmonics, and other disturbances. To enhance its efficacy, advanced optimization techniques such as control algorithm optimization and parameter tuning are employed. These techniques enable the UPQC to adapt to varying operating conditions and optimize its performance in real-time. Integration with hybrid renewable energy sources is seamless, facilitating coordinated operation for optimal power quality enhancement. The proposed approach emphasizes real-time monitoring and control, enabling proactive response to power quality fluctuations and ensuring stable and reliable electricity supply. Through rigorous testing, validation, and maintenance protocols, the effectiveness and reliability of the integrated system are demonstrated. This holistic approach contribute to the stability and sustainability of modern power grids, fostering the widespread adoption of hybrid renewable energy systems.

Hyper-parameter optimization of iterative control algorithms for adaptive optical systems

Hyper-parameter optimization of iterative control algorithms for adaptive optical systems

Reinforcement Learning Based Urban Traffic Signal Control and Its Impact Assessment on Environmental Pollution

To address the growing complexity of urban traffic congestion and its associated environmental impacts, this study presents a pioneering application of the Gaussian plume model to investigate the carbon dioxide emission reduction efficacy of various reinforcement learning algorithms within a traffic signal control framework. By employing an insightful fusion of the traditional environmental science tool with contemporary reinforcement learning strategies - specifically Independent Partially Observable Policy Optimization (IPPO), Independent Delay Q-Network (IDQN), and MPLight - this research marks a novel intersection of methodologies. By quantitatively simulating and analyzing the diffusion dynamics of carbon dioxide pollutants under different traffic signal control scenarios, the study not only highlights the innovative use of the Gaussian plume model to assess the environmental impact of traffic signal control, but also provides critical insights into the selection and optimization of traffic signal control algorithms for improved urban environmental sustainability.

Optimal Control of Brushless Doubly Fed Wind Power Generator under Zero-Voltage Ride-Through

In the grid-connected operation dynamics of brushless doubly fed generators (BDFGs), a dip in the grid voltage is equivalent to suddenly adding a reverse voltage source at the parallel node. By deriving the expressions of the transient current of power winding (PW), control winding (CW), and rotor winding (RW) of a BDFG in the complex frequency domain under a natural state, it was concluded that the overshoot and oscillation time are affected by the CW voltage, the drop degree and phase of the grid voltage, and the rotor speed. Therefore, an optimal control strategy is proposed. A state model with the CW current as the state variable was constructed using the Pontryagin minimum principle. The finite-time integral value of the square of the electromagnetic torque was set as the objective function to achieve the minimum value that could suppress the overshoot and oscillation of the electromagnetic torque, and the optimal CW voltage command value was directly solved to accelerate the convergence of the BDFG’s physical quantities, thereby reducing the amplitude. Finally, the feasibility of the optimal control algorithm was verified using tests on an experimental platform.

An Adaptive Impedance Matching System With Fast Optimization Control Algorithm for Wireless Power Transfer via Magnetic Coupling Resonance

An Adaptive Impedance Matching System With Fast Optimization Control Algorithm for Wireless Power Transfer via Magnetic Coupling Resonance

Дифференциальная игра «преследование-уклонение» на основе обучения с подкреплением

The paper discusses optimal control algorithms based on actor/critic reinforcement learning (RL) schemes. The algorithms are used to solve pursuit-evasion (PE) problems of differential games. The work focuses on the implementation of agent policy decisions in accordance with the concept of adaptive dynamic programming. The essence of solving the PE game problem is to obtain the control policy of each agent (pursuer and evader) on both sides of the game. The paper proposes an adaptive dynamic programming (ADP) method for solving Nash equilibrium policies in differential pursuit-evasion games for two players. The cost function approximation method is used to calculate the parameters of a neural network (NN) without directly solving the Hamilton-Jacoby equation.

Quasi Time-Fuel Optimal Control Strategy for Dynamic Target Tracking

Time and fuel balance is an important topic in the dynamic tracking problem of unmanned systems. In this paper, we propose a quasi time-fuel optimal control strategy (QTFOC) to solve the dynamic tracking problem of a double integrator system, which is capable of multi-target switching tracking tasks, such as the multi-target strike of weapons and the rapid multi-target grabbing of robots on industrial assembly lines. Compared with the previous optimal control algorithms, the proposed controller retains the time-fuel optimal characteristic when switching among multiple dynamic targets, and overcomes the high-frequency oscillation problem by incorporating local linear control region and two nonlinear buffer areas. That is, when switching among multiple dynamic targets, the proposed control strategy enables the corresponding system to achieve desired transient performance and satisfactory steady-state performance simultaneously. In addition, the asymmetry of friction load is further explored, which affects the dynamic performance of the actual system. Extensive experiments based on the visual tracking turntable demonstrate the superiority and feasibility of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the problem of multi-target switching tracking, such as the multi-target strike of weapons with vision sensors and the rapid multi-target grabbing of robots on industrial assembly lines. In recent years, various algorithms have been developed for trajectory planning and tracking. However, it is still challenging for unmanned servo system to capture dynamic targets quickly due to the constrains of motion performance, energy, and computational power. In order to reduce the computational burden and obtain ideal response under various physical constraints, a quasi time-fuel optimal control strategy (QTFOC) with analytical solutions is proposed in this paper. First, the static target is extended to the dynamic target by further investigating the traditional time-fuel optimal control theory for double integrator system. Second, to deal with the oscillation problem caused by system disturbances, we incorporate buffer areas and local linear control region into the control algorithm, which makes the system more robust. Third, the system performance is further improved by analyzing the frictional load asymmetry. This algorithm requires small computational resources and can be implemented directly on microcontrollers such as STM32. The experimental results demonstrate the superiority of our proposed method in handling the multi-target switching tracking problem, which can also adjust the response speed weight, making the unmanned system perform better under different operating conditions. Furthermore, the proposed QTFOC can be extended to other unmanned systems, such as trajectory planning and motion control for unmanned vehicles and bionic robots.

Comparative Analysis of Optimal Control Strategies: LQR, PID, and Sliding Mode Control for DC Motor Position Performance

This study applies these control methods to the DC motor system to examine the robustness and performance of four optimal control methods. Optimal controllers aim to control the system to minimize a selected performance index. These control methods offer advantages such as improving energy efficiency, reducing costs, and enhancing system security. The Linear Quadratic Regulator (LQR) based controller is the primary optimal control method. Two well-known traditional control techniques include the Proportional-Integral-Derivative (PID) and Integral Sliding Mode Controller (ISMC). However, they do not usually contain optimal properties. In this study, the optimal control algorithms, defined by obtaining controller parameters through the Riccati equation, are applied to achieve accurate position-tracking control in a DC motor system using Matlab/Simulink. The integral term-based algorithms seem to be robust and eliminate steady-state errors. The optimal PID controller could not provide the minimum performance index, unlike the other controllers in the study. LQR and optimal ISMC algorithms could allow the performance index to be a minimum. An illustrative comparison of the performances of all optimal control algorithms has been presented through graphical representation, along with corresponding interpretations.