Search Results (435)

The ground mobile platform pointing system (GMPPS) suffers from poor stability and low pointing accuracy under uncertain disturbances due to nonlinear rigid–flexible coupling and structural uncertainties. A UDO-LQR control strategy is proposed, achieving superior control performance compared with the conventional PID control. In this work, a rigid–flexible coupled nonlinear dynamic model of the GMPPS with disturbances is established using Lagrange’s equations of the second kind. Based on the torque compensation equivalence principle, the electromechanical coupling equations are derived. An uncertainty disturbance observer (UDO) is designed to estimate the state variables corresponding to unmodeled disturbances, and a linear quadratic regulator (LQR) is employed to effectively suppress uncertain disturbances, achieving attitude stabilization of the pointing system (PS). The proposed UDO-LQR (UDO-augmented-LQR) strategy delivers an 83.79% performance improvement in azimuth and a 70.45% improvement in elevation in terms of RMSE compared with PID under standard road profile excitations, making it suitable for stable control of the PS under field road conditions compared to the PID control scheme. Full article

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

The growing integration of distributed renewable energy resources interfaced with current power systems through electronic converters presents significant challenges for system monitoring, stability, control and protection. Grid impedance plays a critical role in the operation and stability assessment of grid-connected inverter systems. The proposed technique in this manuscript presents a real-time grid impedance estimation method based on the discrete Fourier transform and recursive least squares. The proposed method is integrated with the Advanced Angle Estimation Kalman Filter using a Linear Quadratic Regulator current controller (AAEKF-LQR) that uses the estimated grid impedance to evaluate an instantaneous grid phase angle. The simulation results in this paper confirm that the proposed impedance estimation method interacts effectively with the AAEKF-LQR controller, maintaining stable system performance under weak grid conditions. The approach also demonstrates the ability to deliver fast and accurate impedance estimation during operational variations in grid conditions, thereby supporting stable inverter operation. Additionally, the method exhibits strong robustness against external oscillation disturbances originating from a synchronous machine interfaced through the point of common coupling in a grid-following configuration with weak grid conditions to achieve significant improvements in oscillation damping. Full article

This paper presents an optimization-based framework for active vibration control of rotor bearing systems using external surface-bonded piezoelectric patches. The rotor bearing system is modelled using the Finite Element Method (FEM), enabling the coupling between the shaft and the flexible piezoelectric actuators. A Linear Quadratic Regulator (LQR) is adopted to achieve optimal feedback control considering the balance between vibration reduction and control effort. The central contribution of this work is a comprehensive actuator placement optimization of the axial and angular position of the piezoelectric patches along the shaft. Firstly, axial positions are selected by maximizing a multimodal weighted Modal Strain Energy (MSE) criterion over a selected number of bending modes. In the second stage, which constitutes the main novelty of this work, the angular position of each pair of bonded piezoelectric patches is optimized. Each piezoelectric pair generates control moments at each extremity of the patch. The influence of the angular separation between independent piezoelectric pairs bonded at different axial locations is investigated through an iterative optimization framework. The optimized actuator placements are subsequently employed within an LQR-based active vibration control framework. The parameters of the controller are selected using a Genetic Algorithm (GA). Numerical simulations are performed on a bi-disk flexible rotor bearing system. The results of the numerical simulations demonstrate that the combined axial-circumferential optimization significantly enhances the controllability of the rotor system and improves the multimodal vibration suppression capability, achieving an improvement of approximately 93%. The proposed methodology offers a physically meaningful and computationally efficient framework, guaranteeing symmetric and effective vibration control. Full article

As renewable energy source-based distributed generation is more widely connected to the grid, stable current control and power quality improvement in grid-connected inverters (GCIs) become more important. To satisfy increasing power demand, multi-inverter systems connected to the grid in parallel are being widely adopted. However, parallel operation may degrade current quality and stability because of inverter interactions under harsh grid conditions. In particular, grid voltage harmonics, voltage imbalance, and frequency variations can also impair current control performance and system stability. To address these concerns, a frequency-adaptive current controller integrated with a Kalman filter (KF)-based observer is developed to ensure a stable operation of multiple GCIs. Moreover, a stability evaluation is presented for multi-inverter systems by using admittance-based stability analysis. A Kalman filter-based state observer is applied to improve the estimation accuracy under noisy measurement conditions. In addition, a moving average filter-based phase-locked loop (MAF-PLL) is applied to improve the detection accuracy and reliability of the grid frequency and phase angle under harsh grid conditions to ensure an effective frequency-adaptive control design. The effectiveness and performance of the proposed current controller are assessed through the PSIM simulations. The simulation results show that the MAF-PLL reduces the maximum frequency fluctuation from $\pm$7 Hz to $\pm$1.1 Hz. In addition, the KF-based observer reduces the RMS estimation error to 0.0001 A. On the other hand, those values are 1.3 A with the conventional observer and 0.0003 A with the LQR-based observer, respectively. The practicality of the proposed scheme is also confirmed experimentally using 2 kW parallel multiple GCI prototype systems under harsh grid conditions. Full article

This paper presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques, such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it a promising candidate for future FPGA and embedded implementations. Overall, the proposed optimal control-based beamforming approach provides a flexible, robust, and computationally efficient solution for next-generation antenna systems in 5G, beyond-5G (B5G), and radar applications. Full article

A Large Language Model-based search for controller synthesis can yield UAV controllers with strong trajectory-tracking performance. However, low tracking error does not necessarily demonstrate closed-loop stability. This study presents a Lyapunov stability assessment of an automatically generated UAV controller produced through a Large Language Model-based search process. The closed-loop system is numerically linearised about the hover equilibrium, yielding a local closed-loop state matrix $A_d$.Eigenvalue analysis is then used to determine whether $A_d$ is Schur stable, corresponding to all eigenvalues lying inside the unit circle $\rho \left(A_d\right)<1$. A quadratic Lyapunov function is constructed by solving the discrete-time Lyapunov equation $A_d^{T}P{A}_d-P=-Q$. The positive definiteness of the resulting matrix provides a local Lyapunov certificate for the linearised closed-loop system. To connect this local certificate to dynamic flight behaviour, the Lyapunov function is evaluated along trajectory-tracking logs using the tracking-error state. The mean Lyapunov value, maximum Lyapunov value, discrete Lyapunov difference, and mean squared error are used to compare the generated controller with PID, LQR, and PID + DOB baselines. The results show that the generated controller satisfies local Lyapunov stability conditions near hover. Our findings demonstrate that established Lyapunov tools can be applied post hoc to a search-generated UAV controller, providing evidence of local stability. Full article

This study enhanced the handling stability and driving safety of a special vehicle by developing a vehicle dynamics model using TruckSim 2019. An ideal two-degree-of-freedom vehicle model was established using Simulink. The reference yaw rate and vehicle sideslip angle were derived from the reference model. Fluent simulations were performed on the vehicle to obtain the aerodynamic coefficients as functions of the relative inflow angle. These relationships were fitted to functional expressions and integrated into the aerodynamic module of TruckSim, replacing the default coefficient curves and improving the accuracy of the subsequent simulations. To improve steering performance, an active front steering (AFS) controller based on the linear quadratic regulator (LQR) algorithm was designed, and an AFS control strategy based on sensor feedback was implemented using MATLAB/Simulink 2021b. Finally, simulations were performed to validate the effectiveness of the controller, which showed that under continuous sinusoidal steering, the controller regulated the vehicle. By applying a front-wheel steering angle computed using the LQR algorithm, the actual yaw rate and vehicle sideslip angle closely tracked the reference values. Using the LQR algorithm, the vehicle achieved improved steering performance and a stable body attitude under crosswinds. Thus, the LQR algorithm enhanced the handling stability and driving safety of the vehicle. Full article

Four-wheel independent steering (4WIS) vehicles can improve low-speed maneuverability and high-speed stability by independently regulating the steering angles of all four wheels. However, under large-curvature trajectories, parameter perturbations, and external disturbances, inconsistent coordination among the four-wheel steering angles may increase tire lateral slip, yaw response deviation, and trajectory tracking errors. To address the difficulty of conventional trajectory tracking methods in simultaneously ensuring geometric consistency, tracking accuracy, and robustness, this paper proposes an adaptive sliding mode trajectory tracking control method based on instantaneous center of rotation (ICR) constraints. First, the tire instantaneous turning center (TTC) of each wheel is derived using rigid-body spatial kinematics, and the TTCs are mapped onto a unified vehicle-body reference plane based on the SAE J670 coordinate system to obtain a real-time vehicle-level ICR estimation. Second, a lateral–yaw dynamic model and a trajectory tracking error model are established. The yaw rate and sideslip angle are corrected using ICR geometric information, and an adaptive sliding mode controller is designed with an equivalent control term, adaptive switching gain, adaptive boundary layer, and sideslip suppression term. The uniform ultimate boundedness of the sliding variable and closed-loop tracking errors is proven using Lyapunov theory. Finally, MATLAB (2023a)2024/CarSim (2019) co-simulations are conducted under small-curvature sinusoidal, double-lane-change, large-curvature sinusoidal, low-adhesion, and mass-perturbation conditions. The results show that the proposed ICR-SMC method significantly reduces lateral and heading errors compared with U-LQR and U-SMC, especially under large-curvature and low-adhesion conditions, demonstrating improved tracking accuracy and robustness for 4WIS vehicles. Full article

Lane-changing and overtaking constitute a typical complex driving manoeuvre for intelligent vehicles operating on structured roads; this task demands that the vehicle not only plan a safe and smooth lane-change trajectory but also requires the control system to maintain high tracking accuracy and lateral stability. Addressing the challenges of real-time path planning and stable tracking control inherent in lane-changing and overtaking scenarios, this paper proposes a trajectory planning and control method that integrates an improved artificial potential field (APF) approach with a lateral–longitudinal cooperative controller. Regarding path planning, the proposed method constructs attractive and repulsive fields based on the APF framework, while introducing virtual target points, elliptical obstacle models, and velocity-dependent repulsive fields to mitigate the risk of local minima and enhance dynamic obstacle avoidance capabilities. To ensure trajectory continuity and trackability, a fifth-order polynomial is employed to smooth the planned path. Regarding control, the method utilises a Linear Quadratic Regulator (LQR)—optimised via a genetic algorithm—for lateral control; this is coupled with a dual-PID longitudinal controller that generates throttle and braking commands based on vehicle speed errors, thereby establishing a cooperative lateral–longitudinal tracking control strategy. The proposed method is validated using a CarSim–MATLAB/Simulink co-simulation platform. Simulation results demonstrate that the proposed method significantly improves trajectory-tracking accuracy and vehicle stability during lane-changing and overtaking manoeuvres. In a single lane-change scenario, the maximum lateral error is reduced from approximately 0.62 m to 0.22 m, and the heading angle error decreases from about 0.058 rad to 0.01 rad; in a continuous lane-changing scenario, the maximum lateral error drops from approximately 0.30 m to 0.04 m, while the heading angle error falls from about 0.016 rad to 0.005 rad. Furthermore, the yaw rate, sideslip angle, and lateral acceleration are reduced by 39.1%, 22.2%, and 28.9%, respectively. These results confirm that, under the specified simulation conditions, the proposed method exhibits superior tracking performance and stability. Future research could further explore more complex driving scenarios, such as curved roads, multi-vehicle interactions, sensor uncertainties, actuator delays, and real-vehicle field experiments. Full article

Modern logistics and manufacturing environments simultaneously demand mobility platforms that are compact enough to navigate narrow aisles and powerful enough to transport oversized or heavy components. We previously developed a compact Steering–Drive–Suspension (SDS) module that integrates steering, in-wheel drive, and suspension within a single wheel envelope, achieving $\pm {90}^{\circ }$ wide-angle steering with a single actuator. The present paper extends that hardware-centric work by treating the two-wheel (2WD) configuration assembled from two SDS modules as the unit module of the platform, building a four-wheel (4WD) operation by coupling two such 2WD units, and developing a unified balance and impedance-based control scheme. We derive a cart–pole inverted-pendulum model for the 2WD configuration and a planar 2-DOF bicycle model for the coupled and cooperative configurations, with full controllability proof and quantitative LQR robustness margins. Three Python 3.12 based scenarios validate the framework: (i) a 2WD inverted-pendulum tracking task, (ii) a forward and lateral relocation maneuver compared across SDS Crab, Ackermann, and four-wheel-steering modes, and (iii) cooperative transport of a $100\mathrm{kg}$ steel plate by two impedance-coupled 2WD units. Across all scenarios the proposed controllers achieve sub-centimetre tracking gap, pitch deviation within $\pm 2^{\circ }$, and well-damped cooperative behavior without payload sloshing. The results substantiate the central design claim that the SDS module’s compactness enables a single hardware platform to act simultaneously as an autonomous small-payload mover, a building block of a 4WD platform, and a cooperative agent for oversized loads. Full article

In order to improve the speed regulation accuracy, dynamic response and operation robustness of an automatic guided vehicle (AGV) in a complex road disturbance environment, this paper studies an adaptive AGV speed regulation system based on EKF state estimation on the basis of AGV dynamics modeling and adaptive control. Firstly, through the electrical-mechanical coupling modeling of the AGV drive system, state space construction and external unknown disturbance equivalent design, a unified electromechanical coupling simulation and physical verification environment is built, which lays a model foundation for the research of the speed control algorithm. Secondly, based on the optimal control model of PID and LQR with first-order lead compensation, an EKF adaptive speed regulation model is constructed by combining the extended Kalman filter and adaptive control to realize the online estimation and dynamic compensation of unknown disturbances. Finally, based on MATLAB/Simulink simulation platform and the STM32 embedded experimental platform, the rationality and robustness of the proposed speed control strategy are verified by speed-mutation conditions, load-disturbance condition and a physical verification experiment. The results show that the overshoot of the EKF adaptive control strategy is only 1.8%, which is 84.1% lower than that of PID control and 61.7% lower than that of LQR control. The rise time is 42% shorter than PID and 23% shorter than LQR. The recovery time under load disturbance is 58% shorter than that of PID and 31% shorter than that of LQR. EKF adaptive control is significantly better than PID and LQR in overshoot, rise time and control stability. The disturbance rejection ability and dynamic recovery speed are greatly improved, which can ensure the high robustness and smooth operation of the AGV speed control system under complex working conditions, effectively enhance the response and compensation ability of the system to sudden disturbances, and better meet the actual needs of AGV speed control in complex engineering scenarios. Full article

This paper presents a methodology for synthesizing static state feedback controllers utilizing a Fractional-Order Performance Index. Linear Quadratic Regulators are designed using integer-order integral weighting functions. In the proposed approach, fractional-order calculus is utilized to introduce an additional degree of freedom in controller synthesis, enabling enhanced shaping of the plant’s dynamic properties. The controller gains are obtained by solving a fractional Riccati-like equation, through which the temporal weighting properties inherent to fractional integration are embedded into a static feedback matrix. This formulation is a minimalist control structure suitable for implementation on resource-constrained hardware. The proposed method is validated via rapid control prototyping on an industrial NI PXIe platform and an analog third-order plant. Performance evaluation using Integral Absolute Error and Integral Absolute Control metrics demonstrates that the fractional order serves as a flexible tuning parameter, providing an alternative trade-off between settling time and control effort. Furthermore, frequency domain sensitivity analysis demonstrates the absence of resonant peaks and inherent attenuation of high-frequency measurement noise. As a result, the presented framework bridges fractional-order optimization techniques with industrial control platforms. Full article

This paper investigates uncertainty-resilient stabilization of an inverted pendulum on a cart (IPOC) using an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model and an LQR-based control framework. The IPOC dynamics are represented as a weighted combination of local linear subsystems, where interval firing strengths derived from upper and lower membership functions capture modeling uncertainties. An LQR state-feedback controller is designed for each subsystem, and the final control input is obtained by blending the local controllers according to the normalized firing strengths. To analyze stability, an LMI-based verification condition is established as a sufficient condition for the subsystem LQR controllers. Simulation results show that this condition is satisfied only in a limited operating region, while the closed-loop system can still remain stable even when the condition is violated, demonstrating the reduced conservatism and flexibility of the proposed approach. Furthermore, comparisons with the conventional PDC structure confirm that the proposed method provides greater design flexibility and enables a trade-off between robustness and transient-state performance. 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