Search Results (448)

With advances in electric drive technology, electric tracked vehicles (ETVs) have emerged as a promising solution for high-mobility ground vehicles. However, under high-speed steering conditions, the equivalent motor load inertia varies significantly, introducing strong nonlinear and time-varying characteristics into the ETV that may induce lateral instability and even rollover. To address this issue, a novel augmented deep Koopman operator-based model predictive control (ADK-MPC) method is proposed. First, a high-order sliding-mode (HOSM) observer is designed to estimate the lumped load disturbances associated with the time-varying equivalent motor load inertia. Then, the estimated disturbances are introduced as an augmented state into the DK operator to construct a data-driven augmented model. The proposed model transforms the nonlinear dynamics into a lifted linear time-invariant representation in the augmented-state space while capturing the dominant nonlinear characteristics. Based on the ADK model, an ADK-MPC controller is developed to convert the nonlinear optimization problem into a quadratic programming problem, thereby improving steering stability and reducing computational complexity. Simulation results under steering conditions indicate that the proposed method achieves better yaw rate tracking and lower computational cost than nonlinear MPC. The yaw rate tracking error is reduced by 45.5%, while the average solving time is shortened by 11.7%. Full article

This paper investigates the linear–quadratic optimal stabilization problem in the so-called critical case, that is, the situation in which the spectrum of the system matrix contains purely imaginary eigenvalues or the standard positive-definiteness conditions on the weight matrices of the objective functional are violated. To address these challenges, new regularization methods for critical problems via perturbation of the system matrices and the functional are studied, and novel algorithms for decomposing multidimensional problems into a collection of one-dimensional canonical systems are developed. The main contribution of this work lies in providing a systematic framework for critical cases where standard methods fail. The results are of practical significance for the construction of optimal synthesis in various engineering and applied systems; in particular, they are applicable to the stabilization of unmanned aerial vehicles, robotic complexes, and intelligent power grids. Full article

This paper investigates the optimal control problem of time-delay backward doubly stochastic systems under partial information. Partial information widely exists in practical control systems due to monitoring constraints, communication delays, and data acquisition costs. Combined with inherent system time delays, it greatly complicates state estimation and decision-making, which requires research. A new type of anticipated backward doubly stochastic differential equations is introduced to describe the system dynamics. Using stochastic analysis and the variational methods, the corresponding maximum principle for optimal control is derived. Furthermore, a verification theorem is established that provides rigorous sufficient optimality conditions: any admissible control satisfying the necessary conditions, along with reasonable convexity assumptions, indeed optimizes the cost functional, thereby bridging the gap between necessary and sufficient optimality criteria. As an application, we solve a time-delay linear-quadratic optimal control problem and obtain explicit analytical expressions; the results demonstrate the validity of the established theoretical framework. Full article

This study compared a self-induced anaerobic fermented product (SIAFP), prepared with Bacillus subtilis M6 and Lactiplantibacillus plantarum R101, with dry-form (DTFP) and wet-form (WTFP) two-stage fermented products in broilers. Three trials were conducted: Trial 1 evaluated the physicochemical properties and growth performance; Trial 2 assessed nutrient composition and apparent total tract digestibility; and Trial 3 determined the optimal dietary inclusion level of SIAFP. In Trial 1, SIAFP exhibited the lowest pH and the highest Lactiplantibacillus-like counts (p < 0.05), and all fermented product groups showed higher body weight gain and performance efficiency factor compared to the unfermented control (p < 0.05). In Trial 2, SIAFP contained higher crude protein and total amino acid contents, concomitant with improved hemicellulose digestibility (p < 0.05). In Trial 3, incremental dietary inclusion of SIAFP (0–3.75%) exerted linear or quadratic effects on body weight gain and feed conversion ratio (p < 0.05), with optimal performance observed within the range of 1.25–2.5%. In conclusion, SIAFP showed comparable growth-promoting effects to DTFP and WTFP, suggesting its potential as a practical alternative fermented feed product. Dietary inclusion at 1.25–2.5% effectively enhanced growth performance, which may be attributed to improved nutrient composition and digestibility in broilers. Full article

This paper investigates the optimal control of a stochastic delayed system with infinite delay of general functional type. By introducing a non-anticipative path derivative and its infinite-window dual operator, we formulate the infinitely anticipated backward stochastic evolution equation (IABSEE) as the adjoint equation and establish both necessary and sufficient maximum principles. As applications, we investigate two optimal control problems featuring infinite delay. For both the classical linear-quadratic (LQ) problem and the nonlinear emission control model, the optimal controls are derived explicitly. Full article

Traditional credit scoring models treat lending decisions as static classification, ignoring the dynamic evolution of borrower risk and long-term profit optimisation. This paper reinterprets credit risk management as a discrete-time stochastic optimal control problem and integrates the Hamilton–Jacobi–Bellman (HJB) framework with deep reinforcement learning. Theoretically, we establish the equivalence between a discrete Markov decision process and the HJB equation, prove the existence and uniqueness of the optimal value function, derive the closed-form Riccati solution under linear-quadratic assumptions, and provide a convergence analysis of neural network value iteration. Empirically, using LendingClub loan data (2016–2018), we implement a PPO-based dynamic credit policy. The proposed model achieves an average reward of 1.6726 and a total reward of 867,613, significantly outperforming static baselines as well as a DQN baseline. Ablation experiments show that replacing the policy network with a linear mapping reduces the average reward by 40.8%, confirming the necessity of nonlinear function approximation. Sensitivity analysis and statistical tests (p < 0.001) confirm the robustness and significance of the gains. This work provides a rigorous mathematical foundation and empirical evidence for shifting credit scoring from static classification to dynamic optimisation. Full article

Traditional credit scoring treats lending as static classification and lacks the ability to adjust risk preferences dynamically. This paper develops a dynamic credit decision framework based on the entropy-regularized Hamilton–Jacobi–Bellman (ER-HJB) equation. Theoretically, we prove the existence and uniqueness of a solution to the ER-HJB equation, show that under exact tabular assumptions the soft policy iteration underlying Soft Actor-Critic (SAC) converges to this solution, and derive a closed-form analytical solution under linear-quadratic conditions. Empirically, using LendingClub loan panel data (2016–2018), we show that a single entropy coefficient continuously modulates the risk–return trade-off. As this coefficient increases from 0.01 to 1.00, tail risk (CVaR 95%) steadily improves, while the Sortino ratio peaks near 0.20. The dynamic SAC model outperforms static baselines (logistic regression, XGBoost, LightGBM) in average reward and, by tuning the entropy coefficient, achieves significant downside risk reduction without retraining. This framework transforms credit scoring into dynamic optimal control with continuously adjustable and interpretable risk preferences, offering a theoretically grounded tool for refined risk management. Full article

Many engineering problems must account for the non-cooperative decisions and actions of multiple players. These problems can be modeled within a game-theoretic framework. The approach herein is to model such problems as mathematical games, convert them to semi-infinite programs, and utilize a semi-infinite program solver whose output is provably an $ϵ$-optimal Nash equilibrium. The approach is successfully benchmarked on two low-dimensional problems. Two types of higher-dimensional linear quadratic dynamic games are then investigated: ones where each player’s problem is convex and ones where at least one player’s problem is nonconvex. Within each type, variations based on information structure, control constraints, number of players, and semi-infinite objective are considered. The algorithm is tested with different internal solvers, and it successfully solves all test problems using MATLAB’s fmincon. The numerical solutions approximate analytical solutions (when they are known) within approximately one percent. For a three-player game with input saturation constraints, hundreds of variables, and no analytical solution, the computational time is approximately five minutes. Full article

This paper proposes an optimal tracking control framework for a permanent magnet synchronous motor (PMSM) drive integrated with a quasi-Z-source (QZS) inverter for all-electric aircraft applications. Two tracking control strategies are developed: (i) an online adaptive optimal control (OAC) method for tracking motor speed and (ii) a linear quadratic integral (LQI) controller for regulating the peak DC-link voltage (PDV) of the QZS. Due to the nonlinear characteristics, parameter uncertainties, and external disturbances inherent in PMSM systems, achieving accurate speed tracking and stable DC-link voltage (DCV) regulation using a PDV control strategy under varying power flow conditions remains a significant challenge. In this study, the PMSM model is represented as a nonlinear system with strict feedback. Augmented feedforward control signals are incorporated to restructure the conventional cascade control architecture into a novel optimal control framework. Based on this formulation, a saturated adaptive optimal control law is proposed, relying on a near-optimal solution to the Hamilton–Jacobi–Isaacs (HJI) equation. This solution is approximated using an online approximator combined with an integral reinforcement learning technique. Meanwhile, an LQI controller is employed to regulate the PDV and suppress voltage fluctuations in the QZS. Simulation results demonstrate that the proposed approach significantly improves speed tracking accuracy, DCV stability, and disturbance rejection capability while improving the overall performance and reliability of PMSM drive systems. The simulation results demonstrate that the proposed control strategies have strong potential for effective application in all-electric aircraft systems, meeting the requirements of high performance and energy efficiency. Full article

After a severe outage occurs, restoring a distribution network can take from several hours to days, making the secure and stable operation of restoration areas (RAs) critical. During a post-disaster partitioned operation, asymmetric controllable distributed generator (CDG) regulation capacity, non-controllable distributed generator (NDG) fluctuation risks, and concentrated high-value loads cause significant inter-area power imbalances. Soft open points bridge this resource gap by integrating electric vehicle charging directly into soft open points via vehicle-to-grid (V2G) technology; the resulting electric vehicle-embedded soft open points (EV-SOPs) acquire storage-like energy transfer capability. This paper proposes a boundary–node coordinated optimization strategy for post-disaster RA operation, which integrates CDGs, NDGs, smart switches, and EV-SOPs. Firstly, the boundary dynamic updating model with a multi-homogeneity indicator—load importance, NDG fluctuation risk, and CDG flexibility—enables adaptive resource allocation. Secondly, the optimal operational model of RA is formulated considering the various characteristics of facilities and topology constraints. Thirdly, EV-SOP uncertainties in response reliability, discharge power, and energy capacity are characterized by Bernoulli, log-normal, and truncated normal distributions, reformulated into a tractable mixed-integer quadratically constrained programming via chance-constraint interval linear transformation, and solved by a sequential weight-based priority search with hot-start strategy. Case studies on the IEEE 123-bus system verify the effectiveness of the proposed method. Specifically, the dynamic boundary strategy reduces the comprehensive weighted index by up to 29.10%; physical feasibility truncation reduces EV-driven load loss from 3.2073 MW to 3.1038 MW; and the sequential weight-based priority search with hot-start strategy achieves a cone constraint satisfaction measure of 9.3175 × 10⁻⁷, confirming robust convergence. Full article

This study presents a novel hierarchical hybrid control architecture for the attitude stabilization of a 3-Degree-of-Freedom (3-DOF) hover system. Operating on a linearized state-space model, a Linear Quadratic Regulator (LQR) is deployed as the optimal inner-loop core to guarantee baseline multi-variable stability. To dramatically improve transient performance and suppress high-frequency oscillations, Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC) are incorporated not as conventional additive inputs, but as dynamic reference-reshaping supervisory mechanisms in the outer loop. This structural decoupling preserves the optimal characteristics of the LQR while effectively attenuating chattering, thereby preventing physical actuator fatigue. Furthermore, the Big Bang–Big Crunch (BB-BC) metaheuristic algorithm is employed to systematically optimize the design parameters of the supervisory layers, enabling effective steady-state error reduction with a remarkably low computational cost. Comparative evaluations demonstrate that the proposed LQR-STSMC framework significantly accelerates system responsiveness, reducing rise times by approximately 80% to 90% and consistently lowering settling times across all operational axes while achieving a reduction of up to two orders of magnitude in overall tracking errors (ITAE) relative to the baseline LQR. Although evaluations involving Model Predictive Control (MPC) demonstrate improvements in transient response and a reduction in total error compared to the standard LQR, the proposed LQR-STSMC architecture exhibits significantly better overall performance and superior disturbance rejection capabilities. Simulation results under continuous aerodynamic perturbations (wind disturbances) confirm that the proposed hierarchical methodology effectively eliminates steady-state offsets, fundamentally outperforming both classical LQR and MPC in terms of robustness, precision, and ultra-fast transient performance. Full article

This study aimed to evaluate the extra-phosphoric effect of increasing doses of bacterial phytase (RONOZYME HiPhos) in corn- and soybean meal-based diets on performance, carcass yield, and meat quality in pigs during the nursery, growing, and finishing phases (GT). Two hundred and fifty pigs, castrated males and females, with an initial weight of 6.08 ± 0.748 kg and 21 days of age, were allocated to a randomized complete block design based on initial body weight, with five treatments and ten replicates per treatment: PC: positive control diets, supplemented with inorganic phosphorus (P) and calcium (Ca), meeting their full nutritional requirements; NC: negative control diets, with reduced available phosphorus (−0.18%) and calcium (−0.16%); 1000 FYT: NC + 1000 phytase units (FYT)/kg of feed; 2000 FYT: NC + 2000 FYT/kg of feed; 3000 FYT: NC + 3000 FYT/kg of feed. Average daily gain (ADG) in the nursery phase did not differ between the groups supplemented with 1000, 2000 and 3000 FYT/kg and PC, but was higher (p < 0.05) than NC. Feed conversion ratio (FCR) in the same phase was similar between PC and the groups supplemented with phytase, all being better (p < 0.05) than NC. The quadratic effect for phytase was verified for FCR in the phase, with the best inclusion of 2320 FYT/kg of feed. In the GF phases and in the overall experimental period (21 to 156 days), the results for average daily feed intake (ADFI), ADG and FCR favored PC and the groups supplemented with phytase compared to the NC (p < 0.05). A quadratic effect was observed for FCR considering the entire GF phase, with the best inclusion of 1923 FYT/kg of feed. Groups supplemented with phytase and PC obtained better carcass results compared to NC (p < 0.05). Linear effects were observed to percentage and quantity of lean meat in the carcass. There was no difference between treatments for meat quality. Supplementation with phytase in corn- and soybean meal-based diets with severely reduced inorganic P and Ca improved pig performance at all stages, with optimized inclusion values of approximately 2200 FYT/kg of feed, and dose-dependent benefits on carcass characteristics. Full article

A new class of modified minimum state error variance (MSEV) state-space based optimal linear quadratic Gaussian (LQG) regulators for closed-loop structures with estimated feedback has been proposed in this article. The negative feedback path is designed as the cascade of the digital LQG regulator and discrete Kalman state observer. The proposed design enables tracking of a time-varying reference input using the predictive control approach. Moreover, the proposed tracking method utilizes a multivariable continuous-time Cauchy state-space model of nonlinear, nonstationary dynamic systems. The resulting control strategy is approximately optimal, as the optimality of the LQG design holds locally for each linearized model around the respective operating point and does not extend to the global nonlinear system. In this sense, starting from the prespecified nominal state trajectory to be tracked, a numerical optimization procedure minimizing the squared tracking error at each step by using the Nelder–Mead direct search simplex algorithm under the required constraints on the input signal has been developed. The LQG regulator and Kalman state observer are designed by utilizing the linear discrete-time state variable models that properly approximate the nonlinear system dynamics across the nominal state trajectory. The performance of the proposed design is validated by simulating a six-degree-of-freedom nonlinear aircraft model across typical flight regimes. Full article

This study presents a virtual disturbance feedforward controller (VDFC) to mitigate motion sickness in vehicles equipped with active suspension systems. Because feedforward control is difficult to implement in practice owing to the limited availability of measurable or estimable road-disturbance information, a half-sine virtual disturbance (HSVD) corresponding to a bump input is introduced and incorporated into the feedforward controller design. The proposed VDFC is integrated with a feedback controller developed from quarter-car and half-car models using linear quadratic static output feedback (LQ SOF) control. Furthermore, to enhance the motion-sickness-mitigation performance of the VDFC, a simulation-based optimization framework is formulated and solved using a heuristic optimization technique. Simulations with bump inputs are carried out in a vehicle dynamics simulation environment using the LQ SOF controller together with the optimized VDFCs. A sensitivity analysis is also performed for the parameters of the optimized virtual disturbance. The results indicate that, under the bump-like excitation conditions considered, the proposed method can improve ride comfort and reduce motion-sickness-related response measures. Full article

To address the challenge of stable tracking of moving maritime targets by unmanned aerial vehicle(UAV) swarm in environments with threat zones and platform failure risks, this paper proposes a cooperative tracking and guidance strategy integrating Distributed Model Predictive Control (DMPC) with Sequential Quadratic Programming (SQP). A cooperative tracking model is developed incorporating UAV kinematics, environmental threats, stereo-vision positioning, and field-of-view constraints. Two original strategies are introduced within the DMPC framework: an altitude-cooperative target recapture strategy reduces target total loss duration by approximately 7 s compared to fixed-altitude baselines, while a distributed formation reconfiguration strategy restores stable tracking within 10 s after member failure and ensures safe inter-UAV separation. A multi-constraint trajectory tracking controller based on DMPC-SQP achieves real-time co-optimization of threat avoidance, formation maintenance, and tracking accuracy. Simulation results in dense threat environments demonstrate a 93.4% Quadratic Programming feasibility rate, with mean tracking error reduced by 25.4% over fixed-altitude DMPC and 48.7% over methods based on the Linear Quadratic Regulator (LQR), while maintaining robust performance under 300 ms communication delay, sensor noise, and moderate wind disturbance. Full article