Search Results (268)

Multi-robot collaboration and marine robotics constitute key research directions in intelligent autonomous systems. In this context, multi-ROV cooperative operations are increasingly deployed for sunken ship search missions. A central technical challenge in such applications is to ensure efficient, non-redundant coverage while maintaining accurate formation tracking. This scenario confronts two principal difficulties. First, overlapping operational regions among multiple ROVs tend to produce both redundant coverage and search blind zones. Second, trajectory tracking accuracy is significantly degraded by the combined effects of hydrodynamic disturbances and inherent actuator constraints in ROVs. To address these challenges, an improved dynamic window approach (DWA), incorporating a search distance penalty mechanism, is proposed for multi-ROV trajectory planning. Concurrently, a cascaded tracking control architecture is constructed, wherein a model predictive kinematic controller generates constrained velocity references, while an adaptive sliding mode dynamic controller augmented with an extended state observer provides robust disturbance rejection. Collaborative search is conducted using a three-ROV leader–follower formation. Simulation results indicate that regional search coverage is effectively improved and areas of repeated detection are significantly reduced by the proposed planning algorithm. Real-time trajectory tracking is achieved by the designed controller under two typical extreme strong disturbance conditions, namely, time-varying disturbances and abrupt disturbances, on the premise of satisfying thruster thrust constraints. The proposed scheme enables all three ROVs to successfully complete the tracking task under time-varying disturbances while reducing the frequency of thrust saturation events by up to seven times. In contrast, under the conventional MPC–ASMC controller, one ROV deviates from the formation and fails to complete the tracking task. Under abrupt disturbances, the proposed approach reduces the trajectory tracking error by up to six times and decreases the frequency of thrust saturation events by up to four times. Full article

Lead zirconate titanate (PZT) is a widely used material for applications in microsensors, actuators, and transducers. Due to its high piezoelectric coefficient, large dielectric constant, and strong polarization capability near the morphotropic phase boundary (Zr/Ti ≈ 52/48), it is considered one of the most attractive materials for micro-electromechanical systems (MEMS). These advantageous material properties strongly depend on the PZT layer’s microstructure and crystallinity, which are primarily determined by the choice of seed layer, deposition conditions, and the post-deposition annealing treatment that promotes the formation of the PZT’s perovskite phase. In this contribution, sputter-deposited PZT thin films were crystallized by conventional furnace annealing (CFA) to evaluate the effect of heating/cooling rates (1 °C·min⁻¹–7 °C·min⁻¹) within a temperature range of 450 °C to 700 °C on structural, electrical, and ferroelectric properties, with consideration of the seed layer preparation. We characterized the materials’ properties by X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM), and measurements of the ferroelectric hysteresis, capacitance, and leakage current. All samples annealed at temperatures of at least 500 °C fully crystallized into the perovskite phase, independently of the heating/cooling rate. The best ferroelectric performance was achieved at 550 °C with a 1 °C·min⁻¹ heating/cooling rate, yielding a saturation polarization of 82.8 µC·cm⁻² and a remnant polarization of 36.9 µC·cm⁻² under a maximum applied field of 300 kV·cm⁻¹. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

This paper investigates the cooperative formation control problem in unmanned surface vehicles (USVs) with prescribed performance constraints under complex marine conditions including external disturbances, model uncertainties, actuator faults, and input saturation. A novel fault-tolerant control (FTC) algorithm is developed by integrating cooperative learning neural networks (NNs), distributed disturbance observers, and the backstepping technique. Specifically, the learning NNs adaptively approximate system uncertainties, and the learned weight information is shared among vehicles to enhance cooperative cognition. Additionally, an auxiliary dynamic system and an actuator configuration matrix are designed to compensate for input saturation and propeller failures. Theoretical analysis based on the Lyapunov method proves that all signals in the closed-loop system are bounded, and the formation tracking errors strictly remain within the predefined transient and steady-state performance bounds. Finally, simulation experiments involving a group of four USVs validate the proposed algorithm. The results demonstrate that the USVs can rapidly converge to and maintain the desired quadrilateral formation shape despite time-varying disturbances and actuator efficiency loss. Furthermore, comparative simulation results indicate that the proposed cooperative learning FTC scheme significantly reduces velocity tracking error oscillations compared to traditional non-learning methods, explicitly verifying its superior robustness and fault-tolerant capabilities. Full article

This review presents a comprehensive study of adaptive control techniques for nonlinear systems influenced by complex nonlinearities and system faults. Nonlinear systems are categorized into general, stochastic, and switched classes, with a focus on their modeling and control challenges. Common nonlinearities such as input saturation, dead-zone, and backlash-like hysteresis, along with actuator and sensor faults, are examined due to their critical impact on system performance. Fuzzy logic systems and neural networks are explored as effective function approximators capable of handling system uncertainties and complex dynamics. Their design methodologies, advantages, and implementation issues are discussed in detail. The review also highlights recent developments in fault-tolerant adaptive control using these intelligent approximators. Finally, the paper outlines open challenges and future research directions, including the integration of adaptive learning frameworks with real-time control and enhanced fault detection strategies for practical nonlinear systems. Full article

Proportional–integral–derivative (PID) controllers are always a preferred choice of control strategy in industrial and biomedical systems due to their simplicity, reliability, and easy implementation. However, the systematic tuning of PID parameters for nonlinear, constrained, and safety-critical systems remains challenging, particularly in the presence of disturbances and actuator limitations. This paper presents a unified surrogate-based optimization framework for tuning PID controllers for linear and nonlinear dynamical systems. The tuning problem is formulated as a constrained optimization task, where performance objectives and safety requirements are explicitly incorporated into the cost function. A surrogate-based optimization via clustering (SBOC) approachis employed to efficiently explore the PID parameter space while reducing the number of expensive closedloop simulations. The proposed framework is first applied to the first- and second-order linear time-invariant systems to check its feasibility and then to the nonlinear systems to demonstrate its robustness under nonlinearity and saturation. The approach is further applied to safety-critical systems considering the case of glucose regulation in type 1 diabetes under realistic meal disturbances and insulin delivery constraints. The simulation results show that the surrogate-optimized PID controller achieves stable regulation with improved tracking performance while strictly satisfying safety requirements, including control effort penalties to limit actuator wear and the avoidance of hypoglycemia and hyperglycemia in glucose regulation problems. Full article

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers ($v\le 0.3$ m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking ($v\ge 1.0$ m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers. Full article

The FOCUS mission is an integrative project developed at the Universidad Nacional de San Martín (UNSAM), Argentina, featuring a constellation of small satellites equipped with X-band Synthetic Aperture Radar (SAR) sensors. Designed with autonomous orbit control, the mission enables Interferometric SAR (InSAR) applications for critical infrastructure monitoring, providing scalable and cost-effective global observation capabilities. This paper presents the modeling, design, and numerical evaluation of the Attitude and Orbit Determination and Control System (AODCS) for the FOCUS mission. The analysis incorporates realistic constraints, including actuator saturation, sensor noise, underactuation effects, and hardware limitations—specifically regarding magnetorquer magnetic moments, reaction wheel capacities, and propulsion unit impulse bounds. Utilizing the NASA 42 attitude and orbit simulator, numerical simulations were conducted to assess stability, pointing accuracy, and agile maneuver tracking through specialized guidance laws. The results confirm that the proposed AODCS architecture achieves stable, responsive performance and supports continuous orbit maintenance, ensuring adequate target acquisition per orbit. Additionally, the selection of star trackers allows achieving a secondary objective through the detection of Resident Space Objects. Full article

This paper investigates a finite-time prescribed performance control (PPC) problem for a class of nonlinear engineering systems subject to actuator input saturation. By introducing a performance transformation framework, the tracking error is constrained within predefined bounds with guaranteed finite-time convergence. To address the strict-feedback structure and input constraints simultaneously, a dynamic surface control (DSC) technique is employed to avoid the explosion of complexity. A novel saturation-compensated control law is constructed to ensure closed-loop stability without requiring the persistence of excitation or exact knowledge of system dynamics. Lyapunov-based analysis rigorously proves that all closed-loop signals are bounded and the prescribed performance is achieved within a finite time. The simulation results demonstrate the effectiveness of the proposed approach under actuator saturation, highlighting its applicability to constrained engineering process control systems. Full article

This paper presents a comprehensive framework for the identification, state estimation, and robust control of a bistable Duffing–Holmes oscillator, validated through an experimental setup. First, to address parametric uncertainty, a Recursive Least Squares Method (RLSM) with a forgetting factor is applied to a filtered model representation, enabling accurate parameter convergence from noisy measurements. Subsequently, a Nonlinear Integral Extended State Observer (NIESO) is designed to reconstruct unmeasured states and estimate total disturbances. A key theoretical contribution is the derivation of explicit gain conditions that guarantee the observer’s stability, overcoming limitations of previous designs. For trajectory tracking, an observer-based backstepping controller is synthesized. Crucially, to bridge the gap between theory and practice, a drift-free integration scheme is implemented to generate feasible position commands for the shake table, preventing actuator saturation. Experimental results confirm the framework’s effectiveness, achieving a 3.7-fold reduction in RMS tracking error compared to open-loop operation, with the tracking error rapidly converging to a small neighborhood within approximately $0.2$ $\mathrm{s}$. Furthermore, the closed-loop system demonstrates superior energy efficiency, requiring significantly lower actuator voltage to sustain stable interwell oscillations. Full article

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%. Full article

This paper addresses the multi-phase homing and docking missions of over-actuated autonomous underwater vehicles (AUVs), where switching among forward cruising, reverse braking, and hovering can induce actuator saturation, rate limit violations, and undesirable transients. We propose a unified framework that couples supervisory mode management with mode-driven constrained control allocation solved by a warm-started sequential quadratic programming (SQP) routine. The controllable wrench is modeled by a mode-dependent differentiable map constructed from the actuator models, and the allocator enforces amplitude bounds and per-cycle increment limits while trading off wrench tracking and actuator usage through mode-scheduled weights. To mitigate switching transients, a continuous transition factor is introduced to interpolate the desired wrench and dominant cost weights, and an integrator alignment reset is applied at switching instants to keep the outer-loop proportional–integral–derivative (PID) output continuous. The allocator is further warm-started by projecting the previous solution onto the post-switch constraint box. The framework is integrated into the Mission-Oriented Operating Suite–Interval Programming (MOOS-IvP) autonomy middleware with adaptive line-of-sight (ALOS) guidance and adaptive PID motion control and is validated on the TS-100 AUV in water tank experiments. Comparative results against a PID-only baseline without control allocation and a variant without bumpless switching show reduced roll transients during the reverse-to-hover transition and improved hover-mode depth station keeping while maintaining feasible actuator commands under constraints. Full article

This paper studies constrained model reference adaptive control (MRAC) for a planar unmanned ground vehicle (UGV) subject to actuator limits and safety requirements. First, we establish a double-integrator model by applying dynamic feedback linearization to a nonholonomic kinematic model with acceleration input, while simultaneously accounting for external disturbances. A constrained MRAC scheme is developed that enforces constraints at two levels: (i) actuator constraints are guaranteed by saturating the physical inputs after mapping the adaptive virtual control through the inverse kinematic transformation, and (ii) safety constraints are enforced via componentwise control barrier function (CBF) on the tracking error, which induces explicit bounds on the plant state. A projection-based adaptive law is introduced to keep parameter estimates bounded and to ensure well-posedness under saturation-induced mismatch. Moreover, we propose a sufficient feasibility condition that explicitly relates safety margins, disturbance bounds, and available actuator authority, thereby forming a guideline for feasible region design. Simulation studies demonstrate that the proposed method achieves constraint-satisfying tracking under bounded disturbances while respecting physical actuator constraints. Full article

High-agility spacecraft require time-efficient attitude maneuvers under strict actuator- and system-driven saturation limits on angular rate and angular acceleration. Analytical methods for attitude profile generation are attractive for on-board use because of their deterministic structure and low computational burden; however, depending on boundary conditions and sequential constraint-enforcement logic, they may yield either infeasible commands that violate constraints or overly conservative commands that underutilize available authority and unnecessarily prolong maneuver time. In contrast, numerical optimization-based methods can produce (near-)minimum-time solutions but are often too iterative and tuning-sensitive for real-time deployment. The proposed method produces an iteratively refined closed-form solution. The inner loop yields a closed-form solution for a given set of parameters, while the outer loop updates the parameter set via an iterative rescale step. The resulting finite-jerk (jerk-limited) profiles are intended for use in a feedforward–feedback architecture to mitigate terminal mismatch induced by quaternion-kinematics linearization and acceleration-related variable mappings. Numerical studies evaluate the proposed method using representative single-case examples and Monte Carlo simulations with comparisons against a baseline analytical method and a numerical optimization-based method. These results indicate that the proposed approach substantially improves feasibility and optimality such that it achieves maneuver times close to those of numerically optimized solutions, while maintaining a semi-closed-form structure. Full article

To address the trajectory tracking problem of underwater manipulators operating in complex marine environments with strong multi-degree-of-freedom coupling, pronounced nonlinearities, and actuator saturation constraints, this paper proposes a super-twisting sliding mode control scheme integrated with an extended state observer and an anti-saturation auxiliary system. A dynamic model of the underwater manipulator incorporating major hydrodynamic effects (added mass and drag) is first established. Based on this model, a super-twisting sliding mode controller is designed to achieve fast convergence of the tracking errors while effectively alleviating the chattering phenomenon associated with conventional sliding mode control. An improved extended state observer is then introduced to estimate unmodeled dynamics and external time-varying disturbances in real time, providing feedforward compensation to enhance system robustness. To explicitly handle actuator output limitations, an anti-saturation auxiliary system is further developed to dynamically regulate the control input and mitigate the adverse effects of saturation. Comparative simulation studies conducted on the Oberon7 underwater manipulator demonstrate that the proposed control strategy achieves higher trajectory tracking accuracy, improved disturbance rejection capability, and faster recovery after saturation release compared with conventional control methods. These results indicate that the proposed approach offers an effective and reliable solution for high-precision trajectory tracking control of underwater manipulators under input saturation constraints. Full article