Advanced Control for Applications最新文献

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.

The increasing penetration of nonlinear loads and power electronic devices in distribution networks has significantly intensified power quality (PQ) challenges, including harmonic distortion, voltage sags/swells, and reactive power imbalance. This paper proposes advanced intelligent control strategies for a Distributed Static Compensator (DSTATCOM) integrated with a Permanent Magnet Synchronous Generator (PMSG)-based Wind Energy Conversion System (WECS) and Battery Storage System (BSS). The proposed schemes include (1) a hybrid fuzzy-sliding mode control (HFSMC), (2) an artificial neural network-based PI controller (ANN-PI), and (3) a Butterfly Generator-based particle swarm optimization PI controller (BG-PSO-PI) for Maximum Power Point Tracking (MPPT) and real-time voltage/current compensation. Simulation results under nonlinear load and fault conditions demonstrate that the proposed BG-PSO-PI controller achieves a minimum source current Total Harmonic Distortion (THD) of 3.51%, outperforming ANN-PI (3.57%), Fuzzy-SMC (3.72%), and conventional PI (7.03%) controllers. Furthermore, the BG-PSO-PI controller maintains DC-link voltage deviations within ±2%, compared to ±5% in traditional schemes, and achieves fault recovery in less than 0.05 s during grid disturbances. These results confirm that the proposed control architecture significantly improves PQ indices, enhances voltage stability, and ensures rapid dynamic response, making it suitable for modern grid-integrated renewable energy systems.

The existing spatial motion control methods for industrial production robotic arms suffer from sluggish convergence efficiency and inadequate control accuracy. With the aim of resolving this matter, the research proposes an optimization method for spatial motion control of robotic arms based on modeling analysis and the fast extended random tree algorithm. The study first models and analyzes the spatial motion of the robotic arm, and combines the Monte Carlo method for random sampling to create a significant amount of feasible paths. The difference between the feasible path and the planned path is that the feasible path is optimized through multiple iterations, gradually approaching the optimal solution, while the planned path is a predetermined fixed path. Afterwards, the improved fast expansion random tree algorithm is used to optimize the path and achieve efficient planning of the robotic arm path. Finally, the proportional integral derivative control method and the recursive least squares method are employed to flexibly control the spatial motion end of the robotic arm, improving the motion accuracy and stability of the robotic arm. The experimental outcomes reveal that the node utilization rate and planned path length of the proposed control method in simulation analysis are 20.71% and 111.61 cm, respectively, and the fitting degree between its control trajectory and the ideal trajectory exceeds 0.98. In practical applications, the average time required for the robotic arm using the proposed method is only 12.5 s, and the quality pass rate remains above 98%. In addition, the completion rate of its winding task is 99.5%. The proposed control optimization approach can effectively improve the control accuracy and production quality of industrial production robotic arms.

This study presents a robust control strategy for unstable cascade processes with time delays, integrating Particle Swarm Optimization (PSO) and the Internal Model Control (IMC) framework. The proposed methodology employs a dual-controller architecture, where the secondary controller is systematically tuned using the IMC approach, while the primary controller is optimized via PSO to enhance closed-loop stability and performance. Extensive simulation studies were conducted across various unstable cascade processes to evaluate the effectiveness of the proposed approach in both servo and regulatory tasks. Comparative analysis with state-of-the-art control methodologies demonstrates that the proposed strategy achieves superior closed-loop performance, particularly in handling system uncertainties. A comprehensive numerical assessment using multiple performance indices indicates a substantial reduction in total error by 65% and control effort by 43%. The evaluation metrics include error minimization, total variation, rise time, and overshoot percentage, affirming the efficacy and robustness of the proposed control scheme.