ABSTRACT This article presents the adaptive sliding mode method for the gun elevation system of the tank operating under model uncertainties and external disturbances. The proposed controller combines optimal control design, the adaptive compensation mechanism using the radial basis function (RBF) neural network, while integrating the sliding mode control (SMC) law to enhance robustness and trajectory tracking accuracy. The RBF network is used to estimate and compensate for unknown nonlinear components and dynamic uncertainties in real time, and the SMC law is incorporated to ensure robustness and force the system output to accurately follow the desired trajectory. The control strategy is synthesized to meet strict performance requirements under complex real‐world operating conditions. Simulation studies conducted in Matlab evaluate the controller's effectiveness. The results demonstrate that the proposed method achieves accurate trajectory tracking, strong disturbance rejection, and improved robustness, confirming its potential for practical military applications.

We study the problem of smoothing traffic flow in a mixed traffic scenario during the cruising phase of vehicles. We consider a connected vehicle system (CVS) composed of multiple human driving vehicles (HDVs) and multiple autonomous vehicles (AVs). For the HDVs, the human driver behavior is modeled by the widely adopted optimal velocity model. The role of the AV is to guide the traffic flow through regulating its own motion based on available traffic information. To avoid network congestion caused by heavy network resource utilization, each AV intermittently communicates with other vehicles. The intermittent vehicle-to-vehicle communication mechanism (I2CM) is adopted to qualitatively reduce the communication resources occupation. The optimal design of the cruise control for the AVs under I2CM is formulated as an optimal state feedback control problem with a random sparse structure constraint (RSSC). We derive the first analytical expression for the gradient of the cost function with respect to the control law with RSSC. We develop an algorithm that distributively estimates the gradient based on available data. We further design a gradient-based distributed cruise control strategy for the smoothing traffic flow problem under I2CM. We conduct simulations on a CVS system comprising 20 vehicles to evaluate the effectiveness of the proposed cruise control strategy. The results reveal that, on average, each AV contributes to a 15% improvement in the driving smoothness of HDVs relative to the scenario without any AVs. Funding: This research is supported in part by the National Natural Science Foundation of China [Grants 62303131, 72101198, and 62073273], in part by the Science Center Program of the National Natural Science Foundation of China [Grant 62188101], in part by the General Research Fund [Grant 14200720] of the Hong Kong University Grants Committee, and in part by the National Research Foundation Singapore through its Medium-Sized Center for Advanced Robotics Technology Innovation [Grant WP2.7].

Abstract To mitigate the frequency stability challenges induced by random power fluctuations and alterations in Load Frequency Control (LFC) parameters during large-scale wind power integration, an optimal PID controller design method based on an intelligent optimization algorithm is presented in this paper. Firstly, a PID-controlled closed-loop LFC model for interconnected power systems incorporating wind generation is established by using static output feedback control method. Secondly, the minimum value of the integral of time multiplied absolute error (ITAE) index of the time - domain output response is selected as the optimization target function, and the optimal PID controller settings are obtained by using the sine cosine algorithm (SCA) with excellent performance. Finally, simulation results confirm that the suggested LFC method has remarkable robustness, even under condions of step load and random load disturbances, wind power deviation effects, and system parameter variations. Additionally, it facilitates greater wind power penetration into the grid.

The paper considers the problem of analytical design of optimal controllers (ADOC) in the Letov-Kalman formulation for stable single-channel high-order objects whose motion is described by a system of differential equations with continuous nonlinearities from the phase coordinates of the object with a linear entry of the control signal. The studied class of control objects is relatively wide for applications, for example, it includes most electromechanical devices. The proposed method for synthesizing optimal controllers for objects of the specified class is based on the use of a known optimal control algorithm for a first-order nonlinear object. For this purpose, the initial description of a high-order object is transformed to a conditionally equivalent model of a first-order object using the so-called aggregated variable (macrovariable) of the object (the terminology of A. A. Kolesnikov is used), which is a certain function of the state vector of the original object. For the adequacy of the object models, this function must satisfy the corresponding linear partial differential equation, the solution of which can be found by known methods. The admissible set of such functions determines a whole set of simply calculated, analytical control algorithms for the original object. Methods for determining the macrovariable are proposed, ensuring the stability of the closed control system and its optimality according to the corresponding quality functional. For linear subobjects of the class under consideration, it is established that the solution of the partial differential equation describing the conditional adequacy of object models is equivalent to solving the well-known problem of determining the eigenvalues and eigenvectors of the transposed matrix of the object model with its state vector. The conditionally adequate first-order model obtained by these standard matrix calculations ensures the optimality of a high-order control system according to the corresponding quadratic quality functional.

The growing penetration of renewable energy sources requires resilient microgrids capable of providing stable and continuous operation. Hybrid energy storage systems (HESS), which integrate hydrogen-based storage systems (HBSS), battery storage systems (BSS), and supercapacitor banks (SCB), are essential to ensuring the flexibility and robustness of these microgrids. Accurate modelling of these microgrids is crucial for analysis, controller design, and performance optimization, but the complexity of HESS poses a significant challenge: simplified linear models fail to capture the inherent nonlinear dynamics, while nonlinear approaches often require excessive computational effort for real-time control applications. To address this challenge, this study presents a novel state space model with linear variable parameters (LPV), which effectively balances accuracy in capturing the nonlinear dynamics of the microgrid and computational efficiency. The research focuses on a high-voltage DC bus microgrid architecture, in which the BSS and SCB are connected directly in parallel to provide passive DC bus stabilization, a configuration that improves system resilience but has received limited attention in the existing literature. The proposed LPV framework employs recursive linearisation around variable operating points, generating a time-varying linear representation that accurately captures the nonlinear behaviour of the system. By relying exclusively on directly measurable state variables, the model eliminates the need for observers, facilitating its practical implementation. The developed model has been compared with a reference model validated in the literature, and the results have been excellent, with average errors, MAE, RAE and RMSE values remaining below 1.2% for all critical variables, including state-of-charge, DC bus voltage, and hydrogen level. At the same time, the model maintains remarkable computational efficiency, completing a 24-h simulation in just 1.49 s, more than twice as fast as its benchmark counterpart. This optimal combination of precision and efficiency makes the developed LPV model particularly suitable for advanced model-based control strategies, including real-time energy management systems (EMS) that use model predictive control (MPC). The developed model represents a significant advance in microgrid modelling, as it provides a general control-oriented approach that enables the design and operation of more resilient, efficient, and scalable renewable energy microgrids.

An online clustering-based optimal distributed damping controller design

This article addresses the distributed prescribed-time convex optimization (DPTCO) problem for high-order nonlinear multiagent systems (MASs) under undirected connected graphs. A cascade design framework is proposed that divides the DPTCO implementation into distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Using changing Lyapunov functions and time-varying state transformations with sufficient conditions, we establish criteria for prescribed-time stabilization and prove the boundedness of internal signals in closed-loop MASs. The framework addresses robust DPTCO for chain-integrator MASs with disturbances through the introduction of novel sliding-mode variables and time-varying gains. It also solves adaptive DPTCO for strict-feedback MASs with parameter uncertainty via backstepping method and descending power state transformation. Two numerical examples verify the theoretical results.

This study presents the development and experimental validation of a dynamic simulation model for a transcritical CO2 heat pump system coupled with a stratified water tank, with particular focus on strong transient behavior and detailed heat exchanger characteristics. Due to the unique thermophysical properties of CO2 under transcritical conditions, conventional modeling approaches are insufficient. The model was validated against experimental results under a range of operating conditions. It accurately predicted outlet water temperatures within ±3.2 °C and system COP within ±6.8% deviation from measurements. In contrast to previous models, this approach offers improved accuracy in capturing dynamic system responses, including startup transients, and demonstrates high adaptability across varying ambient temperatures and load profiles. Importantly, the model also considers the vertical installation layout of components, enabling analysis of gravitational effects on system dynamics and offering insights into optimal configuration strategies. The validated model serves as a powerful tool for system optimization and advanced control design in residential CO2 heat pump applications.

Controller design and weight optimization for discrete-time distributed secondary voltage control of AC microgrids

The increased unsprung mass in In-Wheel Motor (IWM) driven semi-active suspension systems leads to degraded handling stability and ride comfort. In this paper, an output feedback H ∞ / generalized H 2 ( GH 2 ) control strategy is proposed for semi-active suspensions equipped with Magneto-Rheological (MR) dampers to attenuate vertical vibration. The H ∞ norm is used to evaluate the closed-loop performance, while the GH 2 norm is applied to limit hard constraints of the system. A major challenge arises from the dissipative characteristic of the MR damper, which introduces nonlinear constraints that complicate optimal control design and limit performance improvements. To address this issue, the allowable damping force range of the MR damper is identified through MTS850 testbed experiments. Subsequently, a piecewise controller is designed to approximate the nonlinear constraint as piecewise constant bounds. The effectiveness of the proposed control strategy has been validated by simulation results.

Optimal robust controller design for MIMO system

In the present study, a novel multi-level fuzzy control scheme is introduced and optimized by a multi-objective algorithm to determine Pareto fronts of conflicting objective functions. Against traditional techniques, a fuzzy controller is designed to stabilize the system, while a supervisory controller is employed to handle the uncertainties. In order to challenge the performance of the suggested idea, it is applied for stabilization of a nonlinear under-actuated two degree-of-freedom inverted pendulum system. The minimized objective functions are the normalized error of the pendulum angle as well as the normalized error of the cart position. Results on the found non-dominated solutions and system states clearly illustrate the superiority of the proposed optimization algorithm and designed control method in comparison with the well-known approaches introduced in literature.

Multi-objective optimal design of an optimal fuzzy fractional order PID controller for fractional order hydraulic turbine regulating system

A framework for the design of optimal state-feedback controllers for asynchronous sequential machines (clockless digital systems) is developed. Optimisation is with respect to cost functions that may represent a variety of optimisation criteria, including maximal operating speed, minimal switching, minimal energy expenditures, minimal communication expenditures in distributed machines, as well as other optimisation criteria. The optimisation framework is applied in the paper to the design of optimal state-feedback controllers that guide a controlled machine to match a specified model. Yet this optimisation framework can also be utilised to derive optimal controllers for other objectives in the control of asynchronous sequential machines. All controllers derived in the paper uphold fundamental mode operation of the closed-loop machine, thus assuring reliable and deterministic outcomes.

Non-destructive concrete strength prediction faces limitations in validation scope, methodological comparison, and interpretability that constrain deployment in safety-critical construction applications. This study presents a machine learning framework integrating polynomial feature engineering, AdaBoost ensemble regression, and Bayesian optimization to achieve both predictive accuracy and physics-consistent interpretability. Eight state-of-the-art methods were evaluated across 4420 concrete samples, including statistical significance testing, scenario-based assessment, and robustness analysis under measurement uncertainty. The proposed PolyBayes-ABR methodology achieves R2 = 0.9957 (RMSE = 0.643 MPa), showing statistical equivalence to leading ensemble methods, including XGBoost (p = 0.734) and Random Forest (p = 0.888), while outperforming traditional approaches (p < 0.001). Scenario-based validation across four engineering applications confirms robust performance (R2 > 0.93 in all cases). SHAP analysis reveals that polynomial features capture physics-consistent interactions, with the Curing_age × Er interaction achieving dominant importance (SHAP value: 4.2337), aligning with established hydration–microstructure relationships. When accuracy differences fall within measurement uncertainty ranges, the framework provides practical advantages through enhanced uncertainty quantification (±1.260 MPa vs. ±1.338 MPa baseline) and actionable engineering insights for quality control and mix design optimization. This approach addresses the interpretability challenge in concrete engineering applications where both predictive performance and scientific understanding are essential for safe deployment.

ABSTRACTThis paper investigates the inverse optimal control design problem for a class of uncertain switching nonlinear multi‐input multi‐output (MIMO) systems with asymmetric output error constraints and event‐triggered control. Compared to existing work, this study enables the controlled system to achieve optimal control stabilization and guarantees the convergence of the output tracking error to the intended control accuracy range. The unknown nonlinear dynamics are modeled using a neural network, upon which a neural network observer is built. A nonlinear transformation function is designed to deal with the output asymmetric constraint, and an event‐triggering mechanism consisting of an observer‐controller channel is developed. An adaptive neural network event‐triggered output feedback inverse optimization control system is designed inside the backstepping control framework. The primary objectives are to maintain the semi‐global uniform ultimate boundedness (SGUUB) of the closed‐loop system and to achieve the optimum control aim. A simulation example is used to validate the effectiveness of the control strategy.

Direct Synthesis-based optimal PIDD2 controller design for enhanced load frequency control in microgrid systems with fuel cells and diesel generators

Advanced Optimal Control Design for a Buck-Boost Converter in Photovoltaic Systems

This study addresses the optimization of control performance for automatic voltage regulator systems by proposing a fractional-order PIDD2 (FOPIDD2) controller design method based on the hybrid Black-winged Kite Algorithm (BWOA). To overcome the challenges of complex parameter tuning and adaptability to high-dimensional nonlinear optimization in PID controllers, the BWOA integrates the precise search mechanism of the Black-winged Kite Algorithm (BKA) with the spiral encircling strategy of the Whale Optimization Algorithm (WOA). By dividing high-fitness individuals into subgroups for parallel optimization, combined with an elitism preservation mechanism and Levy flight perturbation, the BWOA effectively balances global exploration and local exploitation capabilities, preventing premature convergence. Furthermore, a multi-factor objective function is adopted to optimize the six parameters of the FOPIDD2 controller. Numerical simulations in MATLAB evaluate the controller’s performance across multiple dimensions, including transient response, frequency-domain stability, trajectory tracking, parameter uncertainty, and disturbance rejection, with comparisons to other recent controllers. Simulation results demonstrate that the BWOA-FOPIDD2 controller achieves superior performance in most metrics. Therefore, the proposed method provides an efficient hybrid optimization framework for AVR system controller design.

Output feedback control design for a class of reaction-diffusion equations with Dirichlet anti-collocated sensing and actuation subject to in-domain disturbances is addressed. Within this setting, we design a finite-dimensional dynamic output feedback controller ensuring closed-loop exponential stability and input-output stability with an explicit estimate of the input-output gain. The approach is based on the spectral decomposition of the open-loop infinite-dimensional system and on the use of a suitable Lyapunov functional candidate. Sufficient conditions in the form of matrix inequalities are given to ensure closed-loop stability. These conditions are shown to be always feasible and are employed to devise an optimal controller design algorithm based on the solutions to some linear matrix inequalities.