Wiley: Optimal Control Applications and Methods: Table of Contents

An Efficient Optimal Control Framework for Robotic Systems Using Mittag‐Leffler Polynomial Techniques

Arezoo Ghasempour, Yadollah Ordokhani, Mohsen Razzaghi — Mon, 08 Jun 2026 02:34:14 -0700

A Mittag-Leffler polynomial-based numerical method is developed for fractional optimal control problems. The proposed approach efficiently computes accurate optimal state and control solutions for engineering applications.

ABSTRACT

This article presents a comprehensive overview of optimization-based modeling and control strategies in robotics, highlighting their evolution over the past three decades. Robotic systems are increasingly designed and controlled through optimization frameworks that integrate dynamic and kinematic analyses, enabling efficient performance across diverse tasks. A key focus is formulating optimal control problems that incorporate nonlinear dynamics, contact interactions, and a wide range of system constraints. The general optimal control problem is formalized using differential-algebraic equations to capture both continuous dynamics and discrete constraints, providing a unified structure for motion planning. To address the problem, we establish an explicit expression for the Riemann-Liouville fractional integral operational matrix of Mittag-Leffler polynomials for the first time, utilizing the Fourier Transform. By utilizing the operational matrix in conjunction with the Galerkin method, the problem is converted into a system of algebraic equations. Ultimately, the effectiveness and precision of the proposed numerical algorithm are demonstrated through several illustrative case studies.

Chlamydia Infection Modeling via Optimal Control and Advanced Numerical Simulation

Fri, 05 Jun 2026 20:07:43 -0700

Graphical representation of sensitivity index values for each model parameter and influence of parameters β ₁, β ₂, and η on R0$$ {\mathcal{R}}_0 $$.

ABSTRACT

This work provides a mathematical model that allows us to evaluate the dynamics of Chlamydia trachomatis infections and examine the optimal strategies to control this disease. The model classifies the population of humans into four classes: susceptible, exposed, infected and recovered, and includes biological and behavioral dynamics. Stability analysis for the proposed model is performed. This analysis shows that the disease-free equilibrium is locally and globally asymptotically stable when R0<1$$ {\mathcal{R}}_0<1 $$, while an endemic equilibrium occurs when R0>1$$ {\mathcal{R}}_0>1 $$. Recruitment rate and transmission parameters have been identified via sensitivity analysis as the most important factors for spreading disease, while recovery and mortality rates significantly decrease the potential of transmitting the disease. A framework for optimal control has been developed that considers three different types of control (time-dependent) measures: promoting safe sexual practices, periodic screening, and effective treatment. The maximum principle of Pontryagin and forward-backward sweep computational techniques are used to analyze and evaluate multiple intervention strategies. The findings show that using all three controls leads to a larger reduction of persons either exposed or infected than the comparison of paired interventions. Among the partial strategies, the combination of screening and treatment is the most successful method of controlling the disease; however, the combination will never equal the total control method. A full cost-effectiveness analysis using multiple different measures (e.g., the number of infections that have been avoided and the incremental cost effectiveness ratio) shows that the three-control method, although higher cost is extremely cost-effective. Furthermore, Strategy III (prevention + treatment) dominates Strategy IV (prevention + screening), indicating that screening without treatment is economically inefficient. Numerical simulations confirm that comprehensive, simultaneous application of all controls significantly mitigates infection prevalence and accelerates disease reduction.

Stochastic Maximum Principle for Mean‐Field Delayed Backward Doubly Stochastic Differential Equations

Hui Min, Qiongxia Wen, Wei Zhang — Fri, 15 May 2026 19:04:35 -0700

This paper investigates mean-field delayed backward doubly stochastic optimal control systems. We establish the existence and uniqueness of solutions for mean-field delayed and anticipated backward doubly stochastic differential equations by the contraction mapping principle. Under convexity conditions, a stochastic maximum principle is derived via variational methods and duality analysis. The results are applied to meanfield delayed linear-quadratic optimal control problems.

ABSTRACT

In this paper, we focus on a kind of mean-field doubly stochastic optimal control problems described by mean-field delayed backward doubly stochastic differential equations. To begin with, we obtain the existence and uniqueness result of the solution for mean-field delayed backward doubly stochastic differential equations (MFDBDSDEs, for short) and mean-field anticipated backward doubly stochastic differential equations (MFABDSDEs, for short) by the contraction mapping principle, respectively. Then, under the convexity condition, we deduce the stochastic maximum principle of the mean-field delayed backward doubly stochastic control systems by utilizing the classical variational theory. After that, we give the sufficient conditions for the stochastic control systems by using the duality relation between the state equations and the corresponding adjoint equations. Finally, we give the application to the mean-field delayed doubly stochastic linear quadratic optimal control problems.

A Discrete‐Time Integral Sliding Mode Predictive Control for Electronic Throttle With Experiments and Comparison

Yun Long, Rongjia Lin, Chong Yao, Jiahui Jiang, Enzhe Song, Yun Ke — Fri, 15 May 2026 04:12:39 -0700

Discrete-time integral sliding mode predictive control for electronic throttle.

ABSTRACT

This article introduces a novel discrete-time integral sliding mode predictive controller (DISMPC) for high-precision and reliable tracking control of electronic throttle (ET) systems. In this control framework, a discrete-time error dynamics model of the ET system is first established using the Euler discretization method while accounting for parameter uncertainties and external disturbances. Subsequently, a novel composite control strategy is developed by integrating discrete-time higher-order sliding mode theory with receding horizon optimization, thereby alleviating chattering and handling input constraints. Specifically, the incorporation of predictive control enables the actual sliding mode variable to track the reference trajectory, thereby addressing the challenge of obtaining the equivalent control term in sliding mode control. Moreover, the adoption of the adaptive reaching law and exponential reaching law significantly improves the dynamic response characteristics and steady-state performance of the system. Rigorous theoretical analysis demonstrates that both the width of the real sliding mode band and the tracking error are effectively reduced. Simulation and experimental results further verify that the proposed control approach significantly reduces system output deviation while enhancing the robustness of the ET system.

Linear‐Quadratic Mixed Leadership Stochastic Differential Games With Overlapping Information

Zuopeng Hu, Yanlong Yang, Shuwen Xiang — Thu, 14 May 2026 18:23:17 -0700

This paper studies linear-quadratic mixed leadership stochastic differential games with overlapping information. We derive open-loop Stackelberg–Nash equilibria via the maximum principle with partial information and completion of the square technique, and obtain state feedback representations. An application to continuous-time principal-agent problems with overlapping information is also presented.

ABSTRACT

This article studies a linear-quadratic mixed leadership stochastic differential game with overlapping information, where each player simultaneously acts as the follower in one strategy and the leader in the other. A distinctive feature of this paper is that the information available to the two players overlaps only partially, with neither being a subset of the other. At the follower layer, the players engage in a linear-quadratic non-zero-sum stochastic differential Nash game with overlapping information. At the leader layer, they participate in a similar game, whose state dynamics are governed by a conditional mean-field forward-backward stochastic differential equation. By applying the maximum principle with partial information and the completion of the square technique, this paper derives the open-loop Stackelberg–Nash equilibrium with overlapping information. It is shown that this equilibrium admits a state feedback representation, provided that an associated system of Riccati equations is solvable. As an application, this model is used to study a continuous-time principal-agent problem.

Issue Information

Mon, 11 May 2026 18:36:13 -0700

Optimal Control Applications and Methods, Volume 47, Issue 3, Page 705-705, May/June 2026.

Composite Anti‐Disturbance Memory Consensus Control of Nonlinear Multi‐agent Systems Under Periodic DoS Attacks

Mani Mounika Devi, Natarajan Sakthivel, Coimbatore Annamalai Suruthi Sri — Mon, 11 May 2026 18:36:13 -0700

This work investigates the consensus problem for a class of nonlinear multi-agent systems through memory state feedback control subjected to periodic denial-of-service attacks. In precise, two distinct disturbances are described with disturbance observer-based technique and ℋ∞ performance. By utilizing Lyapunov-Krasovskii functional, advanced algebraic graph theory, and Wirtinger-based integral inequality, the sufficient consensus criteria are obtained. The results of this work ensures that the disturbance is effectively estimated and rejected in control input. Ultimately, the effectiveness of proposed control technique is established through consensus of nonlinear multi-agent systems.

ABSTRACT

In this article, a new composite consensus strategy for a class of nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks with two different kinds of disturbance has been presented, based on ℋ∞ performance approach and a disturbance observer-based method. In this case, one of the disturbances is typically norm-bound, but the other is caused by an exogenous system acting via the input channel. The attacks are considered to occur on a periodic basis, with the data being hacked by the attackers in the communication channel. The main goal of this article is to evaluate the disturbance in the input signal, even in the midst of the attack, where the output is incorporated with the memory feedback control rule. Moreover, the information regarding the system's past and present states is used to design the control strategy. A sufficient condition is derived by introducing the Lyapunov–Krasovskii functional (LKF) based stability theory, ensuring consensus for the resulting closed-loop system. The superiority and reliability of the established theoretical results are demonstrated by two numerical examples.

Design and Implementation of Wireless Charger for EV With High Alignment Tolerance: Gannet Optimization Algorithm (GOA)

A. Singaravelan, B. Gunapriya, S. Sujitha, Velappagari Sekhar — Mon, 11 May 2026 18:36:13 -0700

This graphical abstract illustrates an optimized Wireless Power Transfer (WPT) system designed specifically for Electric Vehicle (EV) charging applications. The core of the system is governed by the Gannet Optimization Algorithm (GOA), which is employed to achieve three primary objectives: maximizing power transfer efficiency, ensuring stable voltage regulation, and minimizing system losses.

ABSTRACT

This manuscript proposes an optimization approach for an electric vehicle (EV) battery charging-driven wireless power transfer (WPT) system. The Gannet Optimization Algorithm (GOA) is the proposed approach. First, the great superiority of unevenly spaced transmitter coils is illustrated. The primary side DC/AC inverter is controlled by using the proposed method. The two compensation topologies examined are double-sided Inductor Capacitor-Capacitor (LCC) and series-series (S-S). For the purpose of charging electric vehicles, a highly-effective WPT system that depends on magnetic resonant coupling is created. In terms of the distribution of the magnetic field (MF) produced by the enhanced transmitter coil, the receiver coil with a variable radius size at each turn is also introduced. The GOA technique is performed and the performance is evaluated utilizing current procedures in the MATLAB platform. The proposed approach displays better results than existing approaches like the Heap-Based Optimizer (HBO), Wild Horse Optimizer (WHO), and Circle Search Algorithm (CSA). From the results, it is concluded that the GOA method achieves higher efficiency compared to existing techniques.

Solving for Blameless and Optimal Control Under Prioritized Safety Constraints

Natalia Pavlasek, Behçet Açıkmeşe, Meeko Oishi, Claus Danielson — Mon, 11 May 2026 18:36:13 -0700

Summary of the proposed method for solving for blameless and optimal control sequences.

ABSTRACT

In many safety-critical optimal control problems, users may request multiple safety constraints that are jointly infeasible due to external factors such as subsystem failures, unexpected disturbances, or fuel limitations. In this manuscript, we employ the concept of blameless optimality to characterize control actions that (a) satisfy the highest priority safety constraints that are feasible, and (b) remain optimal with respect to a mission objective. For a general optimal control problem with jointly infeasible safety constraints, we prove that there may not be a single optimization problem that solves for a blamelessly optimal controller. Instead, finding blamelessly optimal control actions requires sequentially solving at least two optimal control problems: one to determine the highest priority level of constraints that is feasible and another to determine the optimal control action with respect to these constraints. We show how to formulate two optimal control problems such that the resulting control sequence is guaranteed to be blamelessly optimal. We show that given convex, strictly nested safety constraints, blamelessly optimal control can be found by solving two convex optimization problems. In particular, we outline how to solve for blamelessly optimal control sequences when the prioritized safety sets can be represented by polynomials or hyperspheres. We present the results of numerical examples comparing our proposed algorithm to two alternative algorithms.

A Two‐Stage Auxiliary Model Gradient Algorithm With for Parameter Estimation of Nonlinear Fractional‐Order Model

Naishuo Yan, Yan Ji, Jin Ni — Mon, 11 May 2026 18:36:13 -0700

This study proposes a two -stage auxiliary model gradient descent (2S-AM-GD) algorithm for the parameter identification of fractional -order nonlinear systems driven by colored noise. By utilizing the hierarchical identification principle, the 2SAM -GD approach decomposes the system, significantly reducing computational complexity compared to the standard AM -GD algorithm. The proposed framework is successfully applied to model the complex electrochemical dynamics of Proton Exchange Membrane Fuel Cells (PEMFC).

ABSTRACT

This article focuses on the identification of parameters for the fractional-order nonlinear system driven by colored noise. An auxiliary model gradient descent (AM-GD) algorithm is derived to address the identification problem of unknown inputs in the system. For the purpose of increasing the performance of the algorithm, we divide the system into two subsystems, a two-stage auxiliary model gradient descent (2S-AM-GD) algorithm is derived by implementing the hierarchical identification principle, which decomposes the identification model into two subsystems. In comparison to the AM-GD algorithm, the 2S-AM-GD algorithm shows a notable reduction in computational complexity. Finally, the proposed algorithm is evaluated through a series of simulation examples to assess its effectiveness.

Optimal Cooperative Control of Multi‐Modular Robot Manipulator Based on Adaptive Dynamic Programming With Experience Replay in Nonzero‐Sum Games

Bo Dong, Yu Cao, Bing Ma, Jingkai Liang, Tianjiao An — Mon, 11 May 2026 18:36:13 -0700

This paper proposes an adaptive dynamic programming (ADP) method based on nonzero-sum (NZS) game theory for the optimal cooperative control of a multi-modular robot manipulator (MMRM). First, the system dynamics are established via the Newton-Euler iterative algorithm, and load distribution is adopted to allocate driving forces to each module while maintaining force balance. The optimal cooperative control problem is then formulated as an NZS game, with each joint of the modular manipulators treated as a player. A radial basis function neural network (RBFNN)-based state observer is constructed to estimate model unknowns. Moreover, a novel critic neural network weight-adjustment rule that incorporates experience replay is proposed to relax the persistent excitation (PE) condition. Finally, Lyapunov theory proves the ultimate uniform boundedness (UUB) of the closed-loop system errors, and experiments verify the method's effectiveness.

ABSTRACT

An EKF‐Based MADDPG Algorithm Design for Multi‐UAV Collaborative Encirclement

Chenyu Zhang, Fang Wang — Mon, 11 May 2026 18:36:13 -0700

The overall framework of the EKF-MADDPG algorithm proposed for multi-UAV encirclement and obstacle avoidance is that it includes the EKF-based prediction, the hunting-point allocation, and MADDPG algorithm. The pseudocode shows the more detailed structure of the proposed method. Simulation result demonstrates that the proposed algorithm successfully accomplishes the encirclement task and avoids obstacles simultaneously.

ABSTRACT

This paper focuses on the multi-UAV encirclement problem in the presence of obstacles by proposing an improved method that integrates the extended Kalman filter (EKF) into the multi-agent deep deterministic policy gradient (MADDPG) algorithm. Firstly, the EKF is employed to accurately estimate the target position, providing position information for the subsequent encirclement strategy. Then, based on the estimated target position, the hunting points are calculated and determined. Subsequently, the hunting points are allocated to each UAV in a reasonable manner, ensuring that the UAVs can arrive at the estimated positions efficiently and simultaneously in the shortest time. Moreover, a composite reward function is designed to guide the UAVs to make optimal decisions in the encirclement task, where a segmented reward function is used to train the UAV to perform smooth obstacle avoidance. Through extensive training experiments, the convergence and effectiveness of the proposed improved algorithm are significantly verified, providing strong technical support for the efficient execution of the UAV encirclement task.

Double Stage Controller Optimized for Improved Frequency Regulation in Hybrid Shipboard Power System

Shilpam Malik, Sathans Suhag — Mon, 11 May 2026 18:36:13 -0700

Hybrid Shipboard Power System.

ABSTRACT

Increasing marine pollution resulting from ship electrification via conventional shipboard power system (SPS) has necessitated the integration of renewable energy sources (RESs) to the SPS making it hybrid SPS (HSPS). However, there are associated challenges in HSPS due to intermittent nature of RESs. This study, thus, considers a HSPS and designs a novel marine predator algorithm (MPA) tuned double stage/cascaded fractional order controller—fuzzy proportional derivative coupled with fractional order tilt integral (CFPD-FOTI) control—for establishing power balance and maintaining frequency stability in HSPS against generation-load uncertainties. The HSPS comprises the photovoltaic (PV) system, sea wave energy converter (SWEC), fuel cell (FC), flywheel energy storage system (FESS), battery energy storage system (BESS), and the ship diesel generators (SDGs). The proposed cascaded control framework significantly improves frequency regulation, dynamic performance, and system stability, thus constituting a meaningful technical contribution. Evaluation under parameter variations further enhances the practical relevance and robustness of the proposed cascaded control approach. Furthermore, comparative analysis of performance of the proposed controller against various other controllers establishes the proposition a dependable and efficient strategy. The proposed controller is also assessed for stability via eigenvalue analysis and bode plots. Simulations are executed using MATLAB.

Terminal Sliding Mode Control—Neural Network for Arm Movement With Online Optimization Algorithm to Generate Optimal Joint Paths

Mon, 11 May 2026 18:36:13 -0700

Schematic representation of the hybrid TSMC-RNN control system and online path planning algorithm.

ABSTRACT

The research here focuses on regulating the motion of a three-link human arm model in a plane with strong performance against disturbances from the external environment, unmodeled dynamics, and system uncertainty. A continuous terminal sliding mode controller (TSMC) is the adaptive and robust method for achieving this objective. Though the TSMC offers finite-time convergence of the tracking error to zero, it does not eliminate the chattering effect of sliding mode control. In order to counter this limitation, a hybrid method is proposed where the TSMC is integrated with a boundary layer around the sliding surface and a recurrent neural network with one hidden layer. Apart from this integration, joint-space paths cannot be uniquely predetermined due to the arm model's kinematic redundancy. An online routing algorithm is thus integrated with the hybrid method for generating optimal paths in real-time during purposeful arm movement toward the target object. Simulation experiment results show that the proposed hybrid method with the integration of the online routing algorithm suppresses chattering considerably, offers accurate joint-space trajectory tracking, and has a negligible error in following the prescribed workspace paths of the end-effector.

Optimal Control Strategies for a Fractional Leprosy Model With Environmental Bacterial Load

Elhoussine Azroul, Sara Bouda — Mon, 11 May 2026 18:36:13 -0700

A fractional SEIR model with an environmental bacterial load compartment P is developed to study leprosy transmission dynamics. Sensitivity analysis and numerical simulations identify key parameters influencing disease spread. An optimal control strategy including awareness programs and medical treatment is proposed to control leprosy transmission. The findings reveal that awareness-raising is more effective than treatment alone, as it promotes early diagnosis and limits further transmission.

ABSTRACT

In this study, we introduce a mathematical model to analyze the dynamics of leprosy transmission, incorporating a novel SEIR framework extended by an additional compartment that accounts for the bacterial load in the environment. The model employs the Caputo–Fabrizio (CF) fractional derivative to better represent memory effects in the disease transmission process. We establish the existence and uniqueness of the solution using the Banach fixed-point theorem and analyze both the local and global stability of the equilibrium states. A comprehensive sensitivity analysis is conducted to identify the key parameters influencing the spread of leprosy. Numerical simulations are performed to demonstrate the model's ability to capture the complex dynamics of leprosy transmission. Additionally, an optimal control strategy is proposed, involving two control variables: raising awareness and administering medical treatment to reduce the number of infected individuals. Results reveal that awareness-raising is more effective than treatment alone, as it promotes early diagnosis and limits further transmission. Simulations confirm that the fractional order serves as a control parameter influencing convergence to equilibrium and infection persistence. Overall, the findings provide valuable insights into leprosy management, highlighting the importance of environmental factors and public health interventions.

Guaranteed Cost Boundary Control for Uncertain Markov Jump Delay Reaction‐Diffusion Neural Networks

Xiao‐Teng Yang, Xiao‐Zhen Liu, Kai‐Ning Wu — Mon, 11 May 2026 18:36:13 -0700

Guaranteed cost boundary control for uncertain MDRDNNs.

ABSTRACT

The paper investigates the guaranteed cost boundary control (GCBC) for uncertain Markov jump reaction-diffusion neural networks (UMJRDNNs) with constant delay and time-varying delay. Firstly, for UMJRDNNs with constant delay, a sufficient criterion is established to achieve the GCBC with a completely known transition rate matrix (TRM) employing the Lyapunov functional method and inequality techniques. An upper bound of the given cost function is confirmed by the obtained sufficient criterion. Secondly, the uncertain TRM is investigated, and a sufficient criterion is also presented to achieve the guaranteed cost under the designed boundary control for UMJRDNNs with time-varying delay. Moreover, an upper bound can be obtained for the cost function. Finally, two numerical examples are presented to verify the validity of the theoretical results.

Disturbance‐Observer‐Based Tube Model Predictive Control for Constrained Systems

Mon, 11 May 2026 18:36:13 -0700

This paper proposes a disturbance-observer-based tube model predictive control framework for addressing the regulation problem of linear systems. By estimating and compensating for disturbances and incorporating the bounds of the estimation error into the MPC design, the proposed approach effectively improves the system performance.

ABSTRACT

This paper proposes a disturbance-observer-based tube model predictive control (DTMPC) strategy to address the regulation problem of continuous-time linear systems with additive bounded disturbances. The strategy integrates two elements: disturbance compensation and optimal control inputs. The former is designed using the estimation information from the disturbance observer to actively compensate for disturbances. The latter employs the estimation error bound to calculate the disturbance invariant set, which is then incorporated into the DTMPC design to determine the optimal control input. By compensating for the disturbances, the system's uncertainty and steady-state error are minimized. As the estimation error bound decreases and stabilizes, the disturbance invariant set reduces, thereby expanding the feasible set of the nominal state. Finally, recursive feasibility and robust stability of the system are analyzed. The performance of the proposed DTMPC is verified by applying it to a self-balancing vehicle system and comparing it with the TMPC.

Optimized Fuzzy Supertwist‐Based Control for Boosted Stability in Electric Vehicles

Saeed Amiri, Saleh Mobayen, Khoshnam Shojaei, Elahe Moradi — Mon, 04 May 2026 17:06:10 -0700

Optimized Fuzzy Super-Twist Control for EV stability.

ABSTRACT

This article investigates an advanced control method called the fuzzy adaptive super-twist integral sliding mode (FASISM) controller. It combines adaptive sliding mode control with fuzzy logic to improve stability and effectively handle external disturbances. The uncertainties in the system are integrated into a unified framework, enabling them to be effectively addressed using an adaptive control approach. This controller aims to achieve stabilization and optimal performance in electric vehicles by addressing system uncertainties and mitigating the impact of disturbances, thereby ensuring reliable and efficient operation under varying conditions. To realize efficient control, the EV's battery voltage is utilized as the control input, while the EV speed serves as the system output; both variables are restricted to maintain optimal performance and stability. The proposed strategy combines the Takagi-Sugeno (TS) fuzzy model with a parallel distributed compensation fuzzy controller, incorporating an LMI-based optimal super-twist SMC adaptive scheme. The closed-loop system is proven to attain uniform ultimate boundedness with the implementation of the proposed sliding mode controller. Simulation results in MATLAB demonstrate the robust performance of the FASISM controller, achieving rapid stabilization of EV speed even with the existence of uncertainties and disturbances.

Whale Optimization Algorithm‐Based Adaptive Controller for Grid‐Following Inverters: Conceptualization, Simulation, and Experimental Validation

Fri, 01 May 2026 23:11:21 -0700

Whale optimization algorithm-based adaptive controller for grid-following inverters.

ABSTRACT

Grid-tied inverters are widely used to integrate renewable energy sources into the electrical grid. In these systems, LCL filters are often utilized as the interface between the inverter and the grid. However, LCL filters present a resonance peak that may turn the system unstable. In addition, as the grid weakens, lower is the frequency of this peak, posing relevant challenges for the control system. In this sense, adaptive controllers emerge as feasible solutions once they can adjust their gains in response to any perturbation. However, the design of an adaptive controller requires plenty of experience due to the high quantity of parameters. This work proposes a systematic parametrization for a robust model reference adaptive proportional integral controller using the whale optimization algorithm, considering controller performance and stability constraints. The optimized controller is applied on a grid-tied inverter with an LCL filter. Simulation results indicate the feasibility of using the proposed automatic procedure to design the controller, obtaining fast current tracking and high robustness to unmodeled dynamics and exogenous disturbances. In addition, significant parametric variations (more than 30 times the nominal value of grid inductance) are imposed during the experiment to evaluate the robustness of the optimized controller, which is able to maintain the closed-loop globally stable and properly controlled even in the face of all adversities. Processor-in-the loop experiments are presented to validate the feasibility and satisfactory performance of the optimized controller. Experimental results are provided to prove the controller's feasibility in real-world application, demonstrating that grid-injected currents comply with the IEEE 1547 standard.

Delay‐Compensated Adaptive Vector Filter Phase‐Locked Loop Integrated With GRA‐Based Model Predictive Torque Control for Sensorless IPMSM Drives

Mon, 27 Apr 2026 22:01:54 -0700

Graphical abstract of the proposed work.

ABSTRACT

Increasing system reliability and reducing costs for electric vehicle (EV) applications requires reducing sensor dependency in permanent magnet synchronous motors (IPMSMs). Therefore, sensorless operation remains a significant challenge, particularly over a wide speed range. Adaptive vector filters (AVF) and phase-locked loops (PLL) are integrated with a grey relational analysis (GRA)-based model predictive torque control (MPTC) strategy for IPMSMs in the proposed sensorless control framework. The proposed method incorporates an amplitude and phase offset to counteract feedback delay-induced amplitude and phase distortions in the back electromotive force (BEMF) filtered by the AVF. The ideal PLL is implemented using a differential calculation structure that preserves closed-loop dynamics. In comparison, the steady-state position estimation error is effectively eliminated using an open-loop deviation compensator. Incorporating GRA-based optimization enables online adaptation of the weighting factors, achieving an effective trade-off among torque ripple, flux distortion, and switching frequency. An experimental validation of the proposed algorithm is carried out on a Speedgoat real-time hardware-in-the-loop (HIL) platform. The obtained results confirm that the method significantly improves sensorless estimation accuracy, torque response, and robustness against parameter variations, demonstrating its suitability for rail transit applications.

An Approximate LQR Law With Range Feedback for State and Control Constrained Problems

Somnath Buriuly, Vivek Yogi — Mon, 27 Apr 2026 01:50:30 -0700

Illustration for controller caution and safe steering.

ABSTRACT

An optimal control problem with state and control constraints is addressed using a barrier function approach adapted from an interior-point method. A closed-form approximate Linear Quadratic Regulator (LQR) control law has been derived to achieve stabilizing behavior in a state and control constrained environment. The closed-form control law provides automatic controller caution and safe steering. The controller caution is achieved by auto-tuning Q$$ Q $$ and R$$ R $$ to adjust the system's transient response, and the safe steering is achieved by an automatic shift in the target point to avoid converging on an infeasible state. This work demonstrates the results on linear and non-linear systems in comparison with the widely used Artificial Potential Field (APF) Method and the state-of-the-art Control Barrier Function (CBF) method, both paired with the stabilizing LQR law. The proposed law also attains the guidance attribute in the proposed law by integrating range sensors for state constraint feedback. This is supported by the ROS-Gazebo simulation results on a linear omnidirectional autonomous mobile robot with range sensor feedback for navigation in a structured environment.