Input Saturation Research Articles

Adaptive formation control for obstacle avoidance of USVs with asymmetric input saturation

Due to the complex maritime navigation environment, Unmanned Surface Vessels (USVs) are influenced by unknown nonlinear dynamics arising from external disturbances and internal uncertainties. Achieving effective formation control while maintaining obstacle avoidance performance presents significant challenges. This article proposes a Neural Networks (NNs) adaptive formation Artificial Potential Field (APF) obstacle avoidance control method for multiple USVs. By employing online updates of Radial Basis Function (RBF) NNs technology, the unknown nonlinear dynamics are approximated, thus addressing complex nonlinear dynamics problems. In scenarios involving multiple USVs navigating under high wind and wave conditions, collisions with obstacles frequently occur. To tackle this issue, a leader-follower control strategy is designed that effectively addresses risk assessment and obstacle avoidance under such challenging conditions. Additionally, to account for saturation constraints or potential faults in the controller inputs commonly encountered in engineering applications, it implements an asymmetric auxiliary control system. Furthermore, the Lyapunov stability theorem is utilized to ensure the stability of both the formation control and obstacle avoidance algorithms for multiple USVs. Finally, the effectiveness of the proposed algorithm is validated through simulations.

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

BLF-based neuroadaptive control for full state constrained nonlinear systems with input saturation: A feasibility-condition-free approach

BLF-based neuroadaptive control for full state constrained nonlinear systems with input saturation: A feasibility-condition-free approach

Collaborative attitude stabilization and distributed vibration suppression of satellite with ultra–large flexible antenna

Large flexible radar satellites are widely used in space–based remote sensing for wide–area target observation and high–capacity information transmission. To achieve accurate earth observation and structural stability of an ultra–large flexible satellite in hundred–meter length concurrently, this paper presents two–time–scale collaboration for attitude stabilization and vibration suppression. First, the coupling dynamic model of the satellite with ultra–large flexible antenna is established under space disturbances. Next, singular perturbation with boundary layer correction is employed to decompose attitude and vibration controls in different time scales, respectively. Given the respective decoupling models, the slow subsystem employs an angles–only terminal sliding–mode controller with adaptive switching and input saturation to accomplish high–robustness triaxial attitude stabilization in finite time. For the fast subsystem, distributed piezoelectric actuators with leader–follower consensus and feedback control are applicable to rapid and stable vibration suppression of the ultra–large structure. Finally, simulation results validate the effectiveness and superiority of these control methods on the satellite, where collaborative control effects on multiple key indices including attitude and vibration control accuracy and settling time are significant. This work facilitates high–accuracy and –stability collaborative control of future ultra–large satellites.

Manual steering and robust adaptive constrained control of ship autopilot

This study proposes two schemes for ship autopilot: the first is a follow-up mode, applying manual dynamics to the state-space-based underactuated large merchant ship (ULMS) model, using a steering wheel and joystick to emulate manual operation; the second is a Nav mode developed by path-following control. In this mode, a novel path-following control strategy is developed for ULMS with input saturation and delay, two novel auxiliary systems are employed to stabilize the plant with input constraints. The input delay auxiliary system (IDAS) effectively tackles the design challenges posed by input delay, while the input saturation auxiliary system (ISAS) is dedicated to generating actual control inputs under the constraint of saturation. Furthermore, the dynamic surface control (DSC) technique and the roust neural damping technique are introduced to mitigate the computational burden and simplify the controller structure, thereby enhancing the efficiency and performance of the entire control system. Lyapunov stability analysis proves the ship motion system is semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of these two schemes is verified through turning experiment, comparative simulations and a semi-physical simulation platform that integrates physical models with computer simulation technology.

Sampled-Data Exponential Synchronization of Complex Dynamical Networks with Saturating Actuators.

This paper investigates the problem of exponential synchronization control for complex dynamical systems (CDNs) with input saturation. Considering the effects of transmission delay, a memory sampled-data controller is designed. A modified two-sided looped functional is constructed that takes into account the entire sampling period, which includes both current state information and delayed state information. This functional only needs to be positive definite at the sampling instants. Sufficient criteria and the controller design method are provided to ensure the exponential synchronization of CDNs with input saturation under the influence of transmission delay, as well as the estimation of the basin of attraction. Additionally, an optimization algorithm for enlarging the region of attraction is proposed. Finally, a numerical example is presented to verify the effectiveness of the conclusion.

Adaptive path following control for underactuated surface vessels considering actuator saturation dynamics

ABSTRACT This article studies the path-following problem of underactuated surface vessels with saturated actuator dynamics and unmeasured linear velocities subjected to model uncertainties and unknown environmental disturbances. Firstly, a predictor is designed to estimate linear velocities and an improved predictor-based line-of-sight guidance law is designed to generate a reference heading angle, where the unknown arbitrary sideslip angle is compensated. Secondly, robust adaptive control laws are devised, where auxiliary dynamic systems are introduced to address input saturation and adaptive technique is employed to offset environmental disturbances and model uncertainties. Furthermore, to tackle computational complexity in control design, tracking differentiators are employed. Subsequently, the stability analysis of a closed-loop system is demonstrated by the Lyapunov theory. At last, simulations are applied to verify the validity and superiority of the presented strategy.

Adaptive Predefined-Time Fuzzy Tracking Control for Output Constrained Non-strict Feedback Nonlinear Systems with Input Saturation

Adaptive Predefined-Time Fuzzy Tracking Control for Output Constrained Non-strict Feedback Nonlinear Systems with Input Saturation

Neural Network-based Adaptive Finite-time Control for 2-DOF Helicopter Systems with Prescribed Performance and Input Saturation

In this study, we propose an adaptive neural network (NN) control approach for a 2-DOF helicopter system characterized by finite-time prescribed performance and input saturation. Initially, the NN is utilized to estimate the system’s uncertainty. Subsequently, a novel performance function with finite-time attributes is formulated to ensure that the system’s tracking error converges to a narrow margin within a predefined time span. Furthermore, adaptive parameters are integrated to address the inherent input saturation within the system. The boundedness of the system is then demonstrated through stability analysis employing the Lyapunov function. Finally, the effectiveness of the control strategy delineated in this investigation is validated through simulations and experiments.

Prescribed Performance Formation Tracking Control for Underactuated AUVs under Time-Varying Communication Delays

Achieving formation tracking control of underactuated autonomous underwater vehicles (AUVs) under communication delays presents a significant challenge. To address this challenge, a distributed prescribed performance control protocol based on a real-time state information online predictor (RSIOP) is proposed in this paper. First, we innovatively designed an RSIOP to achieve active compensation for the delayed state information of neighboring AUVs. Next, considering formation performance and safety, a low-complexity and practical nonlinear mapping function was used to implement prescribed performance tracking control for the AUV formation. Additionally, the adverse effects of external disturbance uncertainties and input saturation are also considered. Finally, the simulation tests demonstrated that the proposed formation control protocol can successfully achieve the predetermined formation tracking tasks in the presence of time-varying communication delays and external disturbances, while also enabling real-time changes in formation configuration during the process. Throughout, the protocol maintains input saturation limits, and the actual control inputs remain smooth, with no significant oscillations. Furthermore, comparative simulation tests verified the necessity of the RSIOP developed in this study and quantitatively demonstrated that the proposed control method exhibits superior performance in terms of formation control accuracy, error convergence speed, and transient-state constraints.

Saturation-tolerant prescribed control of MIMO nonlinear systems with actuator faults

This paper addresses the tracking control problem of a class of multiple-input-multiple-output nonlinear systems subject to actuator faults. Achieving a balance between input saturation and performance constraints, rather than conducting isolated analyses, especially in the presence of frequently encountered unknown actuator faults, becomes an interesting yet challenging problem. First, to enhance the tracking performance, Tunnel Prescribed Performance (TPP) is proposed to provide narrow tunnel-shape constraints instead of the common over-relaxed trumpet-shape performance constraints. A pair of non-negative signals produced by an auxiliary system is then integrated into TPP, resulting in Saturation-tolerant Prescribed Performance (SPP) with flexible performance boundaries that account for input saturation situations. Namely, SPP can appropriately relax TPP when needed and decrease the conservatism of control design. With the help of SPP, our developed Saturation-tolerant Prescribed Control (SPC) guarantees finite-time convergence while satisfying both input saturation and performance constraints, even under serious actuator faults. Simulations are conducted to illustrate the effectiveness of the proposed SPC.

Adaptive Tracking Control for Uncertain Unmanned Fire Fighting Robot With Input Saturation and Full-State Constraints

Adaptive Tracking Control for Uncertain Unmanned Fire Fighting Robot With Input Saturation and Full-State Constraints

Fully Distributed Event-Triggered Anti-Windup Consensus of Heterogeneous Systems With Input Saturation and an Active Leader.

This article addresses the event-based fully distributed consensus problem for linear heterogeneous multiagent systems (MASs) subject to input saturation. A leader with unknown but bounded control input is also considered. Based on an adaptive dynamic event-triggered protocol, all the agents can reach output consensus without knowing any global knowledge. Moreover, by applying a multiple-level saturation technique, the input-constrained leader-following consensus control is achieved. The given event-triggered algorithm can be utilized for the directed graph containing a spanning tree with the leader as the root. One distinct feature compared with previous works is that the proposed protocol can achieve saturated control without any a priori condition, instead, the local information is needed. Finally, the numerical simulations are illustrated to verify the performance of the proposed protocol.

PDE-Based Boundary Adaptive Consensus Control of Multiagent Systems With Input Constraints.

The leader-follower adaptive consensus control problem is addressed for partial differential equations (PDEs) multiagent systems (MASs), and these agents are composed of flexible manipulator systems with input nonlinearity, boundary uncertainties, and time-varying disturbances. Because of the spatial variables in the model, the design of adaptive protocols is more difficult than that of ordinary differential equation (ODE) MASs. By designing the Lyapunov function, a novel distributed boundary control (BC) protocol is constructed, which not only ensures the consensus of angular positions but also suppresses the boundary vibration of each agent. The hybrid effects of dead zones and input saturation on flexible manipulator systems are addressed using the approximation properties of neural networks (NNs). In addition, the disturbance adaptive laws are proposed to provide a control solution for bounded and time-varying disturbances. Furthermore, by applying the Lyapunov stability theory, the uniformly bounded stability of the multiflexible manipulator can be ensured. Finally, the feasibility of the presented control approach is verified using numerical examples.

Distributed Leaderless and Leader-following Consensus for Second-order Heterogeneous Multi-agent Systems Under Input Saturation

Distributed Leaderless and Leader-following Consensus for Second-order Heterogeneous Multi-agent Systems Under Input Saturation

Trajectory tracking control based on deep reinforcement learning and ensemble random network distillation for robotic manipulator

Abstract In general, the trajectory tracking of robotic manipulator is exceptionally challenging due to the complex and strongly coupled mechanical architecture. In this paper, precise track control of the robotic manipulator is formulated as a dense reward problem for reinforcement learning(RL). A deep RL(DRL) approach combining the soft actor-critic (SAC) algorithm and ensemble random network distillation (ERND) is proposed to address the tracking control problem for robotic manipulator. Firstly, an ERND model is designed, consisting of a module list of multiple RND models. Each RND model obtains the error by learning the target features and the predicted features of the environment. The resulting error serves as internal rewards that drive the robotic agent to explore unknown and unpredictable environmental states. The ensemble model obtains the total internal reward by summing the internal rewards of each RND model, thereby obtaining more accurately reflecting the characteristics of the manipulator in tracking control tasks and improving control performance. Secondly, combining the SAC algorithm with ERND facilitates more robust exploration capabilities in environments with input saturation and joint angle constraints, thereby enabling faster learning of effective policies and enhancing the performance and efficiency of robotic manipulator tracking control tasks. Finally, the simulation results demonstrate that the robotic manipulator tracking control task is effectively completed in dense reward problems through the combination of the SAC algorithm and ERND.

Global Consensus of Double-Integrator Multiagent Systems With Input Saturation by Fully Distributed Event-Triggered and Self-Triggered Controls

Global Consensus of Double-Integrator Multiagent Systems With Input Saturation by Fully Distributed Event-Triggered and Self-Triggered Controls

Analytic optimal control for multi-satellite assembly using linearized twistor-based model

This paper presents Guidance and Control (G&C) systems for multi-satellite assembly in proximity operations. The systems utilize the twistor model, which is linearized through Taylor’s series. Decentralized control laws, designed using Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC), are employed to track an energy-optimal trajectory generated using the Hamiltonian approach. Data exchange between satellites and their neighbors is represented using graph theory. The decentralized MPC framework is implemented using the CasADi package. To ensure collision avoidance between the satellites, a repulsive control law is designed, considering symmetric input saturation in the actuators. The proposed G&C systems are tested using a high-fidelity nonlinear satellite relative motion model that incorporates orbital perturbations. Numerical simulations are performed in a MATLAB® environment, and the results are visualized using STK®. Furthermore, a comparative study is conducted to evaluate tracking performance and fuel consumption between the two control methods. The results demonstrate that the use of an optimal trajectory reduces fuel consumption for both control algorithms.

Dynamic Event-Triggered Safe Control for Nonlinear Game Systems With Asymmetric Input Saturation.

This article focuses on the Pareto optimal issues of nonlinear game systems with asymmetric input saturation under dynamic event-triggered mechanism (DETM). First, the safe control is guaranteed by transforming the system with safety constraints into the one without state constraints utilizing barrier function. The united cost function integrating nonquadratic utility function is constructed to provide the foundation to achieve the Pareto optimal solutions. Then, the adaptive dynamic programming method with concurrent learning is proposed to approximate the Pareto optimal strategies wherein both current and historical data are utilized. To further lessen the consumptions of computation/communication resources, the DETM is integrated into the adaptive algorithm framework which can avoid Zeno phenomena. All the signals of the closed-loop system are proved to be uniformly ultimately bounded. Finally, the simulation results are given to validate the effectiveness of the proposed method from several aspects.

Improved Data-driven Adaptive Control Structure Against Input and Output Saturation

Improved Data-driven Adaptive Control Structure Against Input and Output Saturation