Search

Numerous approaches have been reported for different control problems of low-order triangular nonlinear systems. Nevertheless, it shows that the dynamic models of some practical plants are high-order nonlinear systems with odd rational powers. Besides, these systems constantly possess a non-triangular form and suffer from the impact of unmodeled dynamics. The related studies of such systems are very few and more challenging. This work concentrates on adaptive control issue of high-order nonlinear system with odd rational powers, unmodeled dynamics, and non-triangular structure. Based on the small-gain theorem, and by employing the adaptive technique, adding a power integrator method, and neural network method, we successfully construct a new adaptive controller, which greatly decreases the use of parameter estimations in the adaptive control of the considered system. An example highlights that the strategy regulates the studied systems well and performs satisfied system responses. • The robust adaptive control problem of uncertain nonlinear system is considered. • The system has odd powers, unmodeled dynamics, and non-triangular structure. • The work uses adaptive adding a power integrator and neural network method. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of trajectory tracking control of underactuated surface vessels in a quantitative method with only position and attitude available. Combined with high-gain observer, parameter compression algorithm and performance function, an adaptive control scheme with prescribed performance is proposed. The high-gain observer is constructed to estimate the velocities, and the parameter compression algorithm is adopted to address persistent perturbations and model uncertainties in a more concise way. By prescribed performance function, the controller can be designed with prescribed performance. The results about system stability is given and proved by using the Lyapunov direct method. The signals concerning with all the errors converge to a bounded set. Compared with the existing methods, the developed scheme can reduce the number of tuning parameters, and guarantee the tracking errors bounded within the prescribed performance constraints in the transformed coordinate, which means the steady errors, convergence rates and maximum overshoots can be guaranteed by the performance function. Comparison and numerical simulations are given to demonstrate the effectiveness of the proposed scheme. • An adaptive control scheme is proposed with only position and yaw angle available. • Parameter compression algorithm is adopted to reduce the number of tuning parameters. • The proposed scheme can guarantee the tracking errors with prescribed performance. [ABSTRACT FROM AUTHOR]

This paper focuses on the real time intelligent navigation scheme for unmanned surface vehicle (USV) subject to collision avoidance regulations. The proposed approach is formulated as two segments: the real time guidance principle with COLREGS-compliant collision avoidance mechanism and the finite-time adaptive event-triggered control algorithm. The pointed improvement for the artificial potential field (APF) can guarantee USV possess the excellent avoidance function in accordance with COLREGS regulations. Especially, when the obstacle makes dangerous actions, the emergency obstacle avoidance operation (EOAO) is implemented according to the real time position of obstacles. Based on the proposed guidance principle, a finite-time robust neural control algorithm is developed by fusion of the finite-time stability theory and event-triggered mechanism. The intermittent computing and communication can effectively reduce the energy and resource consumption of the control system. Moreover, the finite-time adaptive controller design contributes to smaller computation load and facilitates the implementation of the algorithm in ocean engineering. The semi global finite-time uniformly boundedness (SGFTUB) of the closed-loop system is guaranteed through the Lyapunov theory. Finally, the numerical simulation and comparison experiment are illustrated to verify the guidance and control performance of the proposed algorithm. • This paper proposes a novel real-time intelligent navigation strategy subject to collision avoidance regulations is proposed. • Pointed improvement for APF can guarantee USV possess the excellent avoidance function with COLREGS regulations. • When other vehicle failed to make collision avoidance action, the EOAO mechanism will be implemented that uses relative position. • Finite-time adaptive controller contributes to smaller computation load. [ABSTRACT FROM AUTHOR]

This paper studies a time-varying formation control problem of a swarm of autonomous surface vehicles (ASVs) with prescribed constraints. Every ASV suffers from unavailable velocities, saturated inputs, internal uncertainties, and external disturbances. A distributed finite-time performance-prescribed time-varying formation control method is proposed for ASVs. Specifically, an adaptive fuzzy state observer (AFSO) is firstly proposed to recover the unavailable velocity by using saturated inputs and estimated disturbances from the fuzzy logic system. Secondly, a tunnel prescribed performance (TPP) function is designed to limit tracking errors with a concise form and smaller overshoot. At the kinematic level, with TPP-based error transformation, a C 1 finite-time time-varying guidance law is proposed by using the smooth switch functions and recovered velocities. Next, a finite-time command filter based on Levant differentiator is presented to smooth the guidance signals. At the kinetic level, a C 1 finite-time saturated control law is developed with the estimated velocities and disturbances. Then, it proves that tracking errors of the proposed closed-loop system converge into a small neighborhood around the equilibrium within finite time while evolving under TPP constraints regardless of saturated inputs. Finally, comparison results are provided to demonstrate the effectiveness of the proposed distributed finite-time performance-prescribed time-varying formation control method. • Distributed finite-time performance-prescribed formation control method is proposed for ASVs. • The TPP with a concise form is developed to achieve smaller overshoot in a positive manner. • An adaptive fuzzy state observer is designed to recover unavailable velocities. [ABSTRACT FROM AUTHOR]

This paper investigates a parameterized path-guided following/maneuvering control problem of an autonomous surface vehicle (ASV) subject to actuator faults via a noncooperative game strategy. Unlike the existing path maneuvering control methods, the noncooperative game of this paper includes two sub-games. On one hand, the path update is considered to be against the effort of kinematic control. On the other hand, the kinetic control is considered to be against the total disturbances consisting of actuator faults and external disturbances. A robust adaptive fault-tolerant path maneuvering controller is designed based on a noncooperative game approach. Specifically, we design a kinematic control law using an improved dynamic surface control approach to achieve the geometric objective. An improved neural predictor is constructed, in which a concurrent learning-based adaptation is designed to identify unknown fault coefficients. A path update law and a kinetic control law are calculated to cover noncooperative games by using an adaptive dynamic programming approach. Theoretical analysis shows that the closed-loop system is input-to-state stable, and the proposed method can meet the geometric objective, the dynamic objective, and the fault-tolerant objective. Finally, simulation results demonstrate the efficacy of the proposed robust adaptive fault-tolerant control method for path maneuvering. • A noncooperative game-based variable tuning law is proposed for path update. • A fault-tolerant control method is developed using a noncooperative game strategy. • An improved neural predictor is designed for identifying fault coefficients. [ABSTRACT FROM AUTHOR]

In this paper, we study the performance enhancement of tracking control for a class of nonlinear systems with prescribed performance, by employing a novel pair of prescribed monotone tube boundary functions. Compared to the standard prescribed performance approach, the key merit relies on the capability to assign some quantitative indices (e.g., overshoot, accuracy and convergence time) – which are widely required in industry – in terms of the novel links between these indices and the proposed boundary functions. We provide a constructive solution to achieve the pre-assigned performance, which is characterized by forward invariance and boundedness of the closed loop. [ABSTRACT FROM AUTHOR]

This paper addresses the tracking control problem of unmanned underwater vehicles under the condition of external disturbances and unmodeled dynamics using Port-Hamiltonian theory. By introducing Port-Hamiltonian theory, conventional UUVs dynamics can be transformed into Port-Hamiltonian form with insightful expressions. Then, combined with a structure preserving adaptive method, unmodeled dynamics and unknown disturbances can be addressed simultaneously without destroying the interconnection structure of the Hamiltonian system. Finally, based on interconnection and damping assignment passivity-based control, the controller is designed in an intuitive way. The dissipation property of Port-Hamiltonian theory is utilized to prove the stability. Compared with other methods that come from the view of signal processing, the proposed scheme has the advantages of better potential and physical interpretation of engineering applications. Simulations and comparisons are conducted to demonstrate the effectiveness of the proposed method. • A novel tracking control algorithm is proposed for UUVs using IDA-PBC method and Port-Hamiltonian theory. • The proposed method can address model uncertainties and external perturbations simultaneously without destroying the structure. • The controller is designed in an intuitive way, which can provide explicit physical interpretation from the view of energy. [ABSTRACT FROM AUTHOR]

This paper models a point-mass load tethered to a single autonomous underwater vehicle (AUV) and proposes a quantitative control strategy, which is able to drive the load to track the desired trajectory closely. The AUV-tow-load system is modeled as three connected subsystems: load position subsystem, cable direction subsystem, and AUV attitude subsystem. Improving on the current prescribed performance control methods, where the transient-state and steady-state tracking behaviors cannot be preassigned accurately, a quantitative controller is designed for the point-mass load position, achieving predefined tracking performance without violating user-defined quantitative indices including overshoot, convergence time, and steady-state accuracy. An uncertainties and disturbances estimator (UDE) is designed and incorporated into the control inputs to compensate for the effects of uncertain dynamics and external disturbances. Rigorous theoretical proof is presented, showing that all tracking errors converge to the neighborhood of zero with quantitatively designed tracking behaviors, obtaining uniformly ultimately boundedness. Simulation results are provided to validate the efficiency of the proposed method. • Designing a general framework that transforms the AUV-tow-load system into three connected subsystems. • Providing a design paradigm for the AUV-tow-load system to assign some quantitative indices on tracking performance. • Employing uncertainties and disturbances estimator to online estimate the lumped disturbances for AUV-tow-load system. [ABSTRACT FROM AUTHOR]

Dynamic regressor extension and mixing is a new technique for parameter estimation with guaranteed performance improvement – with respect to classical gradient or least-squares estimators – that has proven instrumental in the solution of several open problems in system identification and adaptive control. In this brief note we give two interpretations of this parameter estimator in terms of the recent extensions, to the cases of nonlinear systems and observation of linear functionals for time-varying systems, of the classical Luenberger’s state observers. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive event-triggered control algorithm for sail-assisted unmanned surface vehicle (USV) under nonlinear modified logic virtual ship (LVS) guidance, which addresses the issues of communication load and input saturation constraint. In guidance module, the reference signal generated by LVS guidance is further processed by nonlinear modification rules. That can effectively remove the abrupt phenomenon of actuators. In the control module, a robust adaptive control technique for sail-assisted USV is proposed by fusing adaptive compensation technology and hybrid threshold event-triggered. Unnecessary transmission is reduced between controller and actuator by constructing hybrid threshold event-triggered mechanism. Different from most existing event-triggered mechanism with fixed threshold parameters, threshold parameters in hybrid threshold event-triggered are dynamically adjusted according to the demands of the controller. Furthermore, the compensation effect of sail on the dynamic mechanism is considered. Adaptive parameters of rudder and propeller are constructed to improve the robustness of the closed-loop system. Finally, the theoretical stability of the proposed method is proved by Lyapunov stability theorem, and the control performance is evaluated by simulation platform, which verifies that the scheme has reliable tracking accuracy under time-varying disturbances. • The nonlinear modified LVS principle is proposed to improve sail acting performance. • The hybrid threshold parameter setting can obtain satisfactory resource efficiency. • It can be effectively released the coupling effect of sail force on power mechanism. • The triggered state variables are semi-globally uniform ultimate bounded stable. [ABSTRACT FROM AUTHOR]

A crucial and challenging issue in path following control of the autonomous surface vehicle (ASV) is input constraint and communication burden. In this paper, an intervened logic virtual ship (ILVS) guidance is developed to generate the reference signal, and further to tackle the input saturation constraint by employing the dynamic feedback evaluation rule. The main superiority of this design is to intervene the logic virtual ship (LVS) guidance principle to avoid the persistent large stabilization error. Associated with ILVS guidance framework, a robust adaptive neural event-triggered algorithm is proposed for the ASV by fusing the adaptive technique and the multiport event-triggered approach. The constrains of the model nonlinear term and the uncertain actuators gain are released by constructing online learning parameters. Furthermore, the channel resource from the sensor to the controller (SC) as well as the controller to the actuator (CA) is reduced for merits of the multiport event-triggered approach. The feedback error signals and the control inputs will be updated while the triggered condition is occurred. That also can eliminate the low frequency disturbance, caused by marine environment, to improve the robustness of the closed-loop system. Through the Lyapunov stability criterion, all state variables in the triggered point and triggered interval are achieved semi-global uniform ultimate bounded (SGUUB) stability. Finally, the control performance of the proposed method is evaluated on simulation platform, showing the satisfied tracking accuracy with the low transmission load. • The robust adaptive neural event-triggered control algorithm is proposed for the autonomous surface vehicle (ASV). • The developed intervened logic virtual ship (ILVS) guidance can generate the reference signal, and further to tackle the input saturation constraint. • The computing resource from the sensor to the controller, the transmission load from the controller to the actuator are reduced simultaneously. • All the state variables in the triggered point and triggered interval are achieved semi-global uniform ultimate bounded (SGUUB) stability. [ABSTRACT FROM AUTHOR]

This paper investigates an improved event-triggered robust adaptive control algorithm for the marine surface vehicle with the fault compensating mechanism. A dynamic event-triggered mechanism is designed with a concise form to reduce the updating frequencies of the system state. Different from the exiting results, the proposed algorithm can ensure that both the controller and the state transmitter are activated synchronously only at the triggering instants. That can ameliorate the communication obstruction in both the channels of sensor-to-controller and controller-to-actuator. To improve the fault-tolerant capability of the theoretical algorithm, gain-related adaptive parameters are derived to compensate for gain uncertainties and possible actuator faults. In addition, a compensating term via the robust neural damping technique is introduced to stabilize the unpredictable perturbation from the model uncertainties and the external disturbance. Through the Lyapunov criterion, the corresponding closed-loop system is with the semi-global uniformly ultimately bounded (SGUUB) stability. The simulation results are illustrated to verify the effectiveness of the algorithm. • An event-triggered robust adaptive fault-tolerant control algorithm is proposed for marine vehicles. • A concise dynamic event-triggered condition is designed to guarantee an effective tradeoff. • The communication burden can be largely reduced in both the channels of the sensor-to-controller and the controller-to-actuator. [ABSTRACT FROM AUTHOR]

This paper investigates a novel robust adaptive fault-tolerant control algorithm for the path-following activity of the unmanned surface vehicle (USV) via the multiplied event-triggered mechanism. An improved multiplied event-triggered condition is designed by employing the estimate model with a concise form. Thus, the burden-some problem is released, especially for the channel from sensors to the controller. Besides, two discrete adaptive parameters are constructed to stabilize the perturbation caused by the gain constraint and actuator faults. The model uncertainties and the practical disturbance are compensated by utilizing neural networks (NNs) approximation, in which the weight updating is reduced with the robust neural damping technique. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system. Finally, both the simulation and the physical vehicle-based experiments are illustrated to verify the effectiveness of the algorithm. • A robust fault-tolerant control algorithm is proposed for unmanned surface vehicle. • The adaptive parameters are updated online to compensate for unknown actuator faults. • An improved multiplied event-triggered condition is designed with a concise form. • It can evade the unnecessary information transmission of the onboard control network. [ABSTRACT FROM AUTHOR]

This paper aims to develop a safe learning scheme of the USV interception mission. A safe Lyapunov boundary deep deterministic policy gradient (SLDDPG) algorithm is presented for the USV interception mission. The uniformly ultimate bounded (UUB) stability of control systems is analyzed under finite safety constraints. A single neuron proportional adaptive control (SNPAC) is applied to pre-train the deep policy network for speeding up the training process. The proposed method is evaluated by a series of simulations of the USVs interception and tracking mission. Compared with the existing results, our method can fast converge to the feasible solution subject to safety constraints and demonstrate a high performance in stability and safety by virtual-reality experiments. • A learning-based method is presented to achieve optimal automatic interception in a complicated dynamic scenario. • The UUB stability of the SLDDPG is analyzed to guarantee the safety and stability of the RL control. • A single neuron proportional adaptive control (SNPAC) method is given to pre-train the policy network. [ABSTRACT FROM AUTHOR]

This note investigates an event-triggered robust adaptive control for path following of the unmanned robot sailboat (URS) by employing the proportional integral sliding mode manifold and the robust neural damping technique in presence of marine practice. The dominant assignment of this note can be summarized into three points: (1) The double-layer virtual guidance is developed to generate the reference signal for three navigation conditions (i.e., the upwind sailing, the downwind sailing and the crosswind sailing). That can enhance the navigating automatism of the URS while the tacking and the gybing. (2) The system uncertainties are approximated by the radial basis function neural networks (RBF-NNs). To further simplify the controller form, the robust neural damping technique has been introduced to evade the updated information of the neural weights. Furthermore, the input saturation of the closed-loop control system is tackled by constructing the auxiliary variable in the control design. (3) The communication burden in the channel from the controller to the actuator can be reduced for merits of the event-triggered rule. Besides, the system chattering, caused by the marine disturbances has also been reduced through the proportional integral sliding mode manifold and gain-related adaptive law. The tracking errors of the closed-loop control system will converge to a compact set, and all the signals satisfy the semi-global uniformly ultimately bounded (SGUUB) stability. Finally, the effectiveness and the robustness of the proposed algorithm have been demonstrated via the numerical simulations under the simulated marine environment. • An event-triggered robust adaptive control is proposed for unmanned robot sailboat. • The corresponding guidance is designed for upwind, crosswind and downwind conditions. • The input saturation is tackled by the auxiliary variable in the control design. • The proposed algorithm is with lower communication burden and no chattering action. [ABSTRACT FROM AUTHOR]

This paper presents a novel event-triggered cooperative dynamic positioning control algorithm for multiple marine surface ships in the presence of actuator faults. In this algorithm, the undirected interaction topology is employed to construct the communication networks among marine surface ships, and the assumption that the information of leader can be available to all followers is relaxed successfully. To avoid the unnecessary information transmission among ships, the event-triggered mechanism is incorporated in control design. And the strict positive lower bound on interevent interval is proved, which indicates no accumulation of triggering instants, e.g., Zeno behavior is eliminated. Only two adaptive parameters are designed to estimate the actuator fault parameters online, they are updated when the event-triggered condition is violated and held otherwise. Considerable effort is made to guarantee the semi-global uniform ultimate bounded (SGUUB) stability by the extension of Lyapunov approach. Finally, numerical simulations are provided to verify the effectiveness and feasibility of the proposed algorithm. • A distributed control strategy is proposed with fault compensation mechanism. • The undirected interaction topology is used to construct the communication networks. • The lower bound on interevent interval is proved, i.e., Zeno behavior is avoided. • The gain uncertainty and actuator faults are compensated by event-based adaptive law. [ABSTRACT FROM AUTHOR]

This paper investigates a performance-guaranteed formation tracking problem of autonomous surface vehicles (ASVs) under uncertain environments. Especially, model uncertainties, external disturbances and actuator faults exist simultaneously. Based on graph theory and adaptive techniques, a novel two-layer robust adaptive fault-tolerant formation scheme is developed. In kinematic layer, a transformation is made to get a performance unconstrained problem and an adaptive control strategy is incorporated to stabilize transformed errors. In kinetic layer, two types of robust adaptive fault-tolerant formation controllers, i.e. the fault-based one and the fault-unknown one, are designed for different fault scenarios. These controllers share the same structure of single-parameter updating laws to adjust adaptive parameters. It is shown that: 1) using the proposed formation scheme with either designed fault-tolerant controller, desired formation configurations of ASVs are achieved if the interaction topology is connected and control parameters satisfy given conditions; 2) prescribed formation tracking performance can be ensured and all error signals are guaranteed to be uniformly ultimately bounded; 3) a trade-off between two designed controllers exists, that is although the assumption for fault-unknown controller is weaker, larger control parameters are required to behave like fault-based one. Simulations and comparisons are provided to illustrate the effectiveness of theoretical results. • A novel formation scheme is designed with an adaptive strategy in kinematic layer. • Two adaptive fault-tolerant formation controllers are designed in proposed scheme. • A general structure of single-parameter adaptive laws is given for both controllers. • Parameter selection is analyzed and a trade-off between two controllers is observed. [ABSTRACT FROM AUTHOR]