Advanced Control for Applications最新文献

This paper presents the complete design, control, and experimental validation of a low-cost Stewart platform prototype developed as an affordable yet capable robotic testbed for research and education. The platform combines off-the-shelf components with 3D-printed and custom-fabricated parts to deliver full six degrees-of-freedom (6-DoF) motions using six linear actuators connecting a moving platform to a fixed base. The system software integrates dynamic modeling, data acquisition, and real-time control within a unified framework. A robust trajectory tracking controller based on feedback linearization, augmented with an LQR scheme, compensates for the platform's nonlinear dynamics to achieve precise motion control. In parallel, an Extended Kalman Filter fuses IMU and actuator encoder feedback to provide accurate and reliable state estimation under sensor noise and external disturbances. Unlike prior efforts that emphasize only isolated aspects such as modeling or control, this work delivers a complete hardware–software platform validated through both simulation and experiments on static and dynamic trajectories. Results demonstrate effective trajectory tracking and real-time state estimation, highlighting the platform's potential as a cost-effective and versatile tool for advanced research and educational applications.

To address the issue of determining and quantifying the safety level of vehicle following on icy and snow-covered roads in cold regions, this study focuses on intelligent vehicles operating in such environments and aims to identify their safety states. Initially, key indicators for determining the safety state of intelligent vehicles are identified by analyzing the vehicle-following operational conditions on icy and snow-covered roads. Subsequently, a safety state identification model for intelligent vehicle operation in following scenarios on icy roads is constructed based on these indicators. Finally, a simulation scenario model is developed using the co-simulation platform of CarSim and Simulink to perform simulation analysis and validation of the proposed safety state identification model. The results demonstrate that the constructed model integrates three key indicators: Time-to-Collision (TTC), Time-to-Stop (TTS), and Time Headway (TH). To unify the evaluation of different indicators, a dimensionless parameter termed the safety degree is introduced, which synthesizes the influence of TTC, TTS, and TH into a single criterion for distinguishing safe states from hazardous ones. The following safety degree of vehicles increases with the increase of TTC and TH and decreases with the increase of TTS. During vehicle-following operations, the safety degree decreases from 1 to 0.3 when the leading vehicle decelerates, indicating a risky operational state, while it increases gradually when the leading vehicle accelerates. After 11 s, the leading vehicle's speed exceeds its initial value, and the safety degree of the following vehicle rises almost linearly, reflecting an improvement in the evaluated safety state. These results confirm that the proposed model satisfies the requirements for judging and predicting the operational safety state of intelligent vehicles in following scenarios on icy and snowy roads, and it provides theoretical support for the safety assessment of intelligent driving under complex low-adhesion conditions.

This article presents the design of a proportional-integral-derivative (PID) controller for a renewable energy (RE) integrated power system using model order reduction (MOR). A test case of an 11th-order RE-integrated power system from the literature has been used to validate the applied method. The reduction of the 11th-order Renewable Energy (RE) integrated power system model to a 2nd-order model is achieved using a combination of Pade approximation (PA) and direct truncation (DT) (PA-DT) method. Further, the grey wolf optimization (GWO) technique is employed to determine the optimal parameters for the PID controller for the reduced-order model. The designed controller is then directly applied to the high-order system (HOS). This proposed PID design method via PA-DT MOR for the RE integrated power system is a novel technique because, at this stage, no publications related to this combined method are available. The suggested approach is supported by the time and frequency responses. Time domain specifications (TDSs) and performance error indices (PEIs), and statistical analysis are also provided to demonstrate the efficacy of the suggested method.

This paper presents a method for improving the performance of a vehicle suspension system using an adaptive fuzzy dual PID controller optimized with a genetic algorithm. The fuzzy dual PID controller utilizes fuzzy logic to adapt to changing conditions and improve control, while the genetic algorithm optimizes the controller parameters to further enhance performance. The study uses velocity and position PID controllers because velocity PID controls acceleration well and position PID controls position well, and the incorporation of an adaptive fuzzy combination of two controllers ensures optimal performance of the suspension system under all operating conditions. To avoid the issue of suspension distance narrowing and to prevent instability in the controller, the low-pass filtered displacement response of unsprung mass is utilized as the reference for the position PID controller. Quantitatively, according to the ISO-8608 road entry for the goal function, the dual PID achieved a 53.35% improvement over the passive state, 6.57% better than dual PD, 33.06% over the Velocity PID, and 32.93% over the Position PID. These significant, quantifiable results confirm that the proposed adaptive fuzzy dual PID structure offers a robust and highly effective solution for advancing active vehicle suspension control technology.

Coaxial rotor 6-DOF UAVs can find use in several defence and civilian tasks. In this article, two different control methods are proposed for the control of this type of drones: (i) nonlinear optimal control and (ii) multi-loop flatness-based control. The dynamic model of the coaxial rotor drone is formulated and differential flatness properties are proven about it. To apply the nonlinear optimal control method, the dynamic model of the coaxial rotor drone undergoes approximate linearization with first-order Taylor-series expansion and through the computation of the associated Jacobian matrices. To apply multi-loop flatness-based control, the dynamic model of the UAV is decomposed in two subsystems connected in chained form. This means that the state vector of the second subsystem becomes virtual control input to the first subsystem, while the virtual control input of the first subsystem becomes a setpoints vector for the second subsystem. The two proposed control schemes ensure stabilization and precise flight-path tracking for the coaxial rotor UAV. Both control methods avoid changes of state variables and complicated state-space model transformations.

This study presents an analytical method for tuning PI controllers in first-order with time delay (FOTD) systems, leveraging the Lambert W function. The Lambert W function enables exact pole placement, yielding direct analytical expressions for PI gains. The proposed approach identifies a critical condition that achieves a step response without overshoot and with minimum settling time, while also providing explicit tuning rules for systems where controlled overshoot is specified. The method demonstrates strong agreement with established empirical Chien-Hrones-Reswick tuning rules for both non-overshooting and overshooting cases, bridging the gap between theoretical analysis and empirical results.

This paper presents a hybrid control strategy for a non-inverting Buck–Boost DC–DC converter used in photovoltaic energy management. The converter employs two independent Buck and Boost stages, enabling decoupled control loops and fast tracking of rapidly varying reference signals. A combined PI–SMC framework is proposed, where the outer loop regulates the output voltage using a PI controller, and the inner loop controls the inductor current via a smooth Sliding Mode Control (SMC) law based on an arctan(s) switching function. This smooth SMC formulation effectively reduces chattering and high-frequency oscillations while improving tracking accuracy and robustness. Simulation results show short settling time, fast recovery under abrupt input and load variations, and stable performance across the full Buck–Boost operating range. Quantitative NRMSE (Normalized Root Mean Square Error) analysis confirms high tracking quality, with the output voltage exceeding 90% and the inductor current reaching about 98%. Experimental implementation on a TMS320F28379 DSP further validates the proposed method. The results demonstrate improved disturbance rejection and reduced sensitivity to parameter uncertainties, making the approach well suited for practical renewable-energy power converters.

Considering the limited prediction accuracy of the existing time prediction model, we propose a bus arrival time prediction model based on improved SVM and H∞ filter technique. The NN-SVM which had two input features was used to predict the baseline of bus running time from historical trip data. Applying the real-time running information and combining it with baseline time, we use H∞ filter to predict bus arrival time dynamically. Bus arrival time forecasted by the proposed model was assessed with the data of transit route number 142 in Changsha in China. Results demonstrate that the proposed method achieved a Mean Absolute Error (MAE) of 19.47 s, Root Mean Square Error (RMSE) of 23.42 s, representing a 65.0% reduction in MAE and a 74.5% reduction in RMSE compared to standard SVM. and PBIAS of −2.4%, indicating high accuracy with minimal underestimation bias. Compared with conventional SVM and Kalman-based models, the proposed model reduced RMSE by 19.4% and improved robustness in dynamic traffic scenarios.

In this article, a novel mathematical model of a quadrotor UAV (Unmanned Aerial Vehicle) suffering from one type of structural fault, angle deflection in a motor and the corresponding mechanical link, has been derived. Such faults add additional nonlinear terms to the differential equations of motion and change the 3D configuration of the UAV. These nonlinear terms rely significantly on the unknown fault angles, which are estimated via a Radial Basis Function Neural Network (RBFNN). Moreover, a Non-singular Fast Terminal Sliding Mode Control (NFTSMC) scheme optimized by a Genetic Algorithm (GANFTSMC) has been designed for trajectory tracking and reduction of control effort. Simulation results for the entire closed-loop system, using three different types of reference signals and fault angles, demonstrate the significant performance of the proposed controller in the presence of structural faults and external disturbances. Furthermore, the settling time of error dynamics of the system states to the origin has been finite, and the superior performance of our proposed control strategy has been validated via comparison with other robust nonlinear control techniques implemented in the literature.