Semi-globally Uniformly Ultimately Bounded Research Articles

A novel adaptive fuzzy reinforcement learning controller for a platoon of off-axle hitching tractor-trailers with a prescribed performance and path curvature compensation

This paper addresses an artificial intelligence-based platoon control (AIPC) problem of autonomous off-axle hitching tractor-trailer wheeled mobile robots (TTWMRs) in a planar motion. Towards this end, a novel saturated nonlinear interval type-2 neuro-fuzzy reinforcement learning controller is proposed to construct a convoy-like motion of TTWMRs. To overcome the cutting-corner phenomenon in the platoon motion, the curvature of the reference trajectory is estimated and the cutting-corner behaviour is prevented. In order to deal with the situation where the velocity and acceleration signals are not measurable or corrupted by the measurement noise, a high-gain observer (HGO) is employed. The prescribed performance control (PPC) is also engaged in the control design procedure to guarantee inter-vehicular communication preservation, collision prevention between each consecutive pair in the platoon, singularity cancellation, and some prespecified desired characteristics of the convoy formation tracking errors such as maximum undershoot/overshoot, convergence rate, and final tracking precision. The Lyapunov-based stability analysis reveals semi-global uniform ultimate boundedness (SGUUB) convergence of tracking errors for the entire platoon system. With the suggested control framework, simulations and comparisons are conducted in which the control performance is considerably improved compared with the existing literature.

Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems

In this article, an event-triggered adaptive neural command-filter-based dynamic surface control (CFDSC) is discussed for states constrained nonstrict-feedback nonlinear systems with dynamic uncertainties. The order-reduced compensation signals are designed to compensate the tracking error of the filter in original dynamic surface control (DSC). The hyperbolic tangent function is adopted as the invertible mapping to deal with the full state constraints. An auxiliary signal is employed to estimate the dynamical uncertainties produced by the unmodeled dynamics. The unknown smooth nonlinear terms are approximated by radial basis function neural networks (RBFNNs) at recursive each step. An event-triggered input is constructed in this novel CFDSC framework. With the help of the defined compact set in the stability analysis, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the adaptive closed-loop system is proved. Moreover, each state can be strictly limited within the time-varying state conditions. Two simulation verifications are employed to verify and clarify the theoretical findings.

Finite-time congestion tracking control for TCP/AWM network systems employing event-triggered mechanism

This paper addresses the tracking control problem of TCP/AWM network systems in presence of nonresponsive data flows of category user datagram protocol (UDP) flows. Firstly, a modified network system model is established by a certain suitable variable transformation, and then a fuzzy logic system (FLS) emulator is used to approximate the nonlinear terms in the network dynamics representation system. Secondly, inspired by the idea of the prescribed performance control (PPC), a novel finite-time performance function (NFTPF) is proposed. In turn, an adaptive finite-time congestion control strategy is designed by compatible usage as appropriate of a barrier Lyapunov function (BLF), the backstepping control synthesis, and an event-triggered mechanism. The proposed control strategy can not only make the tracking error to satisfy the pre-assigned transient and steady-state performance, but also ensure that all the closed-loop signals remain semi-globally uniformly ultimately bounded (SGUUB). In addition, the designed congestion control strategy eliminates potential occurrence of Zeno behavior. A set of simulation results are presented to clarify the feasibility and effectiveness of proposed methodological approach and the designed congestion controller.

Dynamic Memory Event-Triggered Adaptive Control for a Class of Strict-Feedback Nonlinear Systems

This brief presents a dynamic memory event-triggered mechanism based adaptive control strategy for a class of strict-feedback nonlinear systems. Firstly, the dynamic memory event-triggered mechanism (DMETM) is established, rigorous proof demonstrates that the triggering intervals of the proposed DMETM are larger than that of the memoryless dynamic event-triggered mechanism. Furthermore, the adaptive control strategy is designed via the dynamic surface control, by which the “explosion of complexity” in the backstepping design process is avoided. Additionally, the proposed DMETM based adaptive dynamic surface controller can guarantee the closed loop system to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the simulation results illustrate the validity of the proposed control strategy.

Adaptive Fuzzy Control for Flexible Robotic Manipulator with a Fixed Sampled Period

In this paper, a backstepping sampled data control method is developed for a flexible robotic manipulator whose internal dynamic is completely unknown. To address the internal uncertainties, the fuzzy logical system (FLS) is considered. Moreover, considering the limited network bandwidth, the designed controller and adaptive laws only contain the sampled data with a fixed sampled period. By invoking the Lyapunov stability theory, all signals of the flexible robotic manipulator are semi-global uniformly ultimately bounded (SGUUB). Ultimately, an application to a flexible robotic manipulator is given to verify the validity of the sampled data controller.

Neural network feedback linearization target tracking control of underactuated autonomous underwater vehicles with a guaranteed performance

In this paper, an adaptive neural network-based feedback-linearizing controller is proposed to force an underactuated autonomous underwater vehicle (AUV) to follow a target with desired relative distance and angles in three-dimensional space. Towards this end, a new input-output model of the AUV is developed by defining a proper set of look-ahead position variables in a cone-shaped area in the front of the vehicle. Then, a nonlinear saturated PID-type controller is designed such that target tracking errors converge to a neighborhood of the zero while the prescribed transient and steady-state performance characteristics including desirable overshoot/undershoot, convergence rate and steady-state accuracy are guaranteed. This technique effectively prevents undesirable transient state behaviour of the vehicle while reducing the risk of actuators saturation. A combination of multi-layer neural networks (NNs) and adaptive robust control (ARC) techniques is used to handle the compensation of model uncertainties including unknown parameters, time-varying environmental disturbances induced by waves and ocean currents and NN approximation errors. A Lyapunov-based stability synthesis is presented to ensure the semi-global uniform ultimate boundedness of the proposed closed-loop control system with a guaranteed prescribed performance. Finally, simulation results are given to show the effective performance of the AUV for practical applications.

Fuzzy-Approximation Adaptive Prescribed Performance Output Regulation for Uncertain Nonlinear Systems

This article studies the output regulation problem (ORP) for nonlinear systems based on prescribed performance control (PPC). The items with the partial derivative of the virtual controller are combined together by using backstepping, and then the fuzzy logic systems (FLSs) are used to approximate these combined items, so that the designed virtual controller does not have the partial derivative of the previous virtual controllers. Therefore, this method not only reduces the calculation burden in the backstepping method, but also avoids the disadvantages of dynamic surface control (DSC). Finally, a function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Theta $ </tex-math></inline-formula> is constructed such that the overall performance (dynamic performance and steady-state performance) of the tracking error (OPTE) is constrained by PP functions. The proposed control algorithm ensures that all the signals are semi-globally uniformly ultimately bounded (SGUUB), and the tracking error achieves the PPC. Simulation examples are provided to illustrate the effectiveness of the proposed method.

Adaptive Neural Fault-Tolerant Control for USV With the Output-Based Triggering Approach

This paper presents an adaptive neural fault-tolerant control algorithm for the path-following activity of the underactuated surface vehicle (USV) using the novel output-based triggering approach. In the algorithm, the event-triggered mechanism is designed utilizing the attitude states from the kinematics aspect of USV. Both the control inputs and the related calculation thread are implemented only at the trigger instants. Furthermore, neural networks (NNs) are employed to remodel the model uncertainties, and the adaptive observer is developed to estimate and compensate for the effect of the unknown actuator faults. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system in aspects of both the trigger instant and the continuous interval. The comparison experiment has been illustrated to verify the effectiveness of the proposed algorithm, which can effectively improve the information transmission performance and the fault-tolerant capability.

Task-space control for industrial robot manipulators with unknown inner loop control architecture

The operational space control of a robot manipulator using external sensors requires stabilizing the compound system {external sensors - outer controller - inner controller - robot manipulator}. The user must access the inner controller to reshape it to achieve this stabilization. Due to intellectual property protection purposes, most industrial robots have an unknown or inaccessible inner controller. Therefore, it is tricky to design a stable control scheme. To solve this problem, an adaptive radial basis function neural network (RBF NN) outer controller is proposed, which approximates the inner controller’s dynamics to eliminate its effect in the closed-loop. An inherent property for RBF NN is used to reduce the number of adaptive parameters. Since this technique introduces approximation errors, it is included in the control scheme, a term that constrains the system to converge rapidly to the performances prescribed by the user. It is proved that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB) through Lyapunov theory. The effectiveness of the proposed approach is verified through simulation comparisons and experimental studies.

Prescribed performance dynamic surface control for nonlinear systems subject to partial and full state constraints

This paper discusses prescribed performance dynamic surface control (DSC) problems for nonlinear systems subject to state constraints. Incorporating the prescribed performance control (PPC) with DSC methods, we present a neural adaptive control method that characterizes the steady-state tracking error and its convergence rate. By constructing a type of funnel error variables, the error variables can be limited in the boundary of funnel functions, and the violations of the conditions of partial and full state constraints are shunned. In the latter case, a new assumption is proposed to handle the circular design problem in the design procedure. Under the control scheme designed, the considered nonlinear systems can be stabilized with the Lyapunov stability theory, and it can be guaranteed that all variables are semi-globally uniformly ultimately bounded (SGUUB). Finally, simulation results verify that the proposed prescribed performance DSC-based control scheme can ensure stability and prescribed performance metrics in the existence of partial and full-state constraints.

Dynamic surface control for tracking of unmanned surface vessel with prescribed performance and asymmetric time-varying full state constraints

In this paper, a novel prescribed performance and full state constraints dynamic surface control (PP&FSC-DSC) scheme is proposed for trajectory tracking of unmanned surface vessel (USV) with model uncertainties, unknown nonlinear disturbances and full state and input constraints. Firstly, the restricted system with inequality constraints is transformed into the system with equality constraints by the modified asymmetric barrier Lyapunov function (MABLF) and then combined with the prescribed performance function, the vessel position tracking error meets the prescribed performance. Secondly, owing to use the dynamic surface technique and introduce an auxiliary dynamic system (ADS), the double advantages are established, i.e., the negative effects of the differential operation for the intermediate control law and input saturation of the intermediate control law are avoided. Thirdly, the model uncertainties are approximated by the neural network (NN), and the NN approximation error and unknown nonlinear disturbances is adaptively estimated. Finally, according to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded (SGUUB). The trajectory can be maintained with prescribed tracking performance and the full state can be constrained. The effectiveness of the proposed control protocol is validated by the simulation results.

Composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism

This paper focus on the novel composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism. In the proposed algorithm, the system uncertainties are tackled with by incorporating the dynamic surface control (DSC) and the robust neural damping techniques. Besides, the serial–parallel estimation model (SPEM) is developed to produce the prediction errors, which could be employed to derive the corresponding composite adaptive law. And the unknown actuator faults and gain uncertainties are effectively compensated for merits of the composite intelligent learning method. Furthermore, the idea of relative threshold strategy is utilized to construct the event-triggered mechanism. The event-triggered input could reduce the communication burden in the channel from controller to actuators. Through the direct Lyapunov theory, the parameters setting can be derived to guarantee the semi-global uniformly ultimately bounded (SGUUB) stability of all error signals in the closed-loop system. Finally, the effectiveness of the proposed algorithm is validated through the simulation experiments.

NN adaptive optimal tracking control for a class of uncertain nonstrict feedback nonlinear systems

A novel reinforcement learning-based adaptive optimal controller is proposed to obtain the desired tracking performance for a class of nonlinear nonstrict feedback systems with uncertain dynamics in this paper. The main feature is that the proposed control scheme can handle the control problem that traditional reinforcement learning-based algorithm cannot deal with. To achieve the optimal control of the high-order system, the virtual and the actual control of the system are optimized by using reinforcement learning method. Radial basis function neural networks are employed to approximate the uncertain system dynamics, the optimal cost function and the optimal control law, respectively. According to Lyapunov stability theorem, it is proved that all the error signals in the closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB) while the desired tracking control performance can be obtained. Simulation results are given to illustrate the effectiveness of the proposed algorithm.

Dynamic control for LNG carrier with output constraints

This paper addresses constrained robust control for dynamic positioning of the Liquefied Natural Gas (LNG) Carrier in side-by-side offloading operations subject to input time delay. During the LNG offloading operation, the relative motions of the multibody system are connected by the LNG offloading arm. To ensure the operation safety, robust control with asymmetric Barrier Lyapunov Function is proposed considering predefined output constraints under the unknown dynamics and external disturbances. In offshore engineering, due to the mechanical effects, the thrusters of the LNG carrier in the presence of time-delay should be investigated to guide the design practice. The predictor-based method is employed to deal with the time delay induced by the control inputs. It is proven that the proposed scheme can guarantee semi-global uniform ultimate boundedness. The performance and feasibility of the proposed control are further verified by simulation studies.

Robust position/force control of nonholonomic mobile manipulator forconstrained motion on surface in task space

In this paper, a robust controller is developed for a mobile manipulator (MM) to track reference position/force trajectories. Nonholonomic and holonomic constraints are considered for the mobile platform and manipulator, respectively. Additionally, the control design considers the uncertainties in parameters of the dynamics of the mobile manipulator with a bounded time varying additive disturbance (unmodelled effects, external disturbances). A Lyapunov-based stability analysis is used to prove semiglobal uniform ultimate boundedness of the tracking error signals and the position/force of the system track to an arbitrarily small neighborhood of the reference trajectories. Numerical results for a mobile manipulator, which is formed from a differential drive mobile platform and 2 DOF Revolute-Revolute (RR) manipulator, show that the position of the end-effector and the applied force track the desired position and the force trajectories, respectively.

Robust adaptive fault-tolerant control for unmanned surface vehicle via the multiplied event-triggered mechanism

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.

Robust Composite Dynamic Event-Triggered Control for Multiple USVs with DLLOS Guidance

In this paper, a robust composite dynamic event-triggered formation control scheme is proposed for multiple underactuated surface vehicles (USVs) from two aspects, i.e., guidance and control. In the guidance module, a novel dual-layer line-of-sight (DLLOS) guidance principle is incorporated into the leader–follower framework to generate the reference path. To overcome the problem of unavailable leader velocity information, an adaptive speed controller is designed to adjust the navigational speed of followers. As for the control part, by utilizing the dynamic event-triggered method, the operational frequency of actuators can be reduced in a flexible manner. That can effectively avoid the excessive wear and chattering phenomenon of actuators. Furthermore, by the fusing of the radial basis function neural networks (RBF NNs) and the robust neural damping technique, the model uncertainty, environmental disturbances and some unknown parameters can be remodeled, and only two gain-related adaptive laws need to be updated online. The serial–parallel estimation model (SPEM) is established to predict the velocity variables, and the approximation performance of NNs can be enhanced by virtue of the derived prediction error. Through the Lyapunov stable theorem, all control signals in the closed-loop system are guaranteed semi-globally uniformly ultimately bounded (SGUUB) stability. Finally, digital simulations are illustrated to verify the effectiveness and superiority of the proposed algorithm.

Observer-based adaptive neural network control for stabilized platform in rotary steerable system with unknown input dead-zone

The core task of the stabilized platform in the rotary steerable system is to control the toolface angle, so that the trajectory of the whole bit can move forward to the set direction. Due to many downhole interference factors, the uncertainties of parameters related to internal friction and interference torque of stabilized platform always exist, which poses great challenges to model establishment and controller design. Moreover, the actuator dead-zone nonlinearity makes the control more complicated. Hence, a dynamic model of stabilized platform considering a variety of nonlinear factors is established, and an observer-based adaptive neural network (NN)-control law is proposed. An NN state observer is developed to cope with the uncertain states composed of unknown friction parametric uncertainties and unmodeled disturbances, the dead-zone inverse is constructed to compensate for a dead-zone, and the dynamic surface control (DSC) strategy is used to solve the “differential explosion,” all signals in the system are proved to be semi-global uniformly ultimately bounded (SGUUB) by the Lyapunov function. Finally, the MATLAB simulation is set up, and the accurate tracking effect of the system is proved by the simulation in the presence of friction, modeling error, and dead-zone nonlinearity. The validity and the superior performance of the proposed control method under the downhole harsh environment and parameter perturbation is verified by the comparison simulation experiments.

Adaptive Neural Digital Control of Hysteretic Systems With Implicit Inverse Compensator and Its Application on Magnetostrictive Actuator.

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

Fixed-Time Backstepping Fractional-Order Sliding Mode Excitation Control for Performance Improvement of Power System

In this paper, a fixed-time backstepping fractional-order sliding mode excitation controller (FTBFOSMEC) is proposed to enhance the performance of multimachine power system. The FTBFOSMEC is designed to achieve semi-global uniform ultimate boundedness of the multimachine power system within an upper limited convergence time irrelated to the initial operating states. The FTBFOSMEC can achieve the attractive qualities of sliding mode control with strong robustness and fast dynamic response, and the merits of backstepping control in globally asymptotic stability for parametric uncertainty. In addition, the FTBFOSMEC can avoid the singularity problem of conventional SMC. Furthermore, the nonlinear first-order filter is carried out to avoid the “explosion complexity” issue of traditional backstepping control. The fractional-order controller is designed to alleviate the chattering phenomenon of the conventional SMC. Meanwhile, the FTBFOSMEC is in a continuous description, which can further eliminate the chattering phenomenon. The Lyapunov function is taken to analyze the stabilization of the multimachine power system under the control of the FTBFOSMEC, and to estimate the upper limit of convergence time. Finally, comparative results, based on the New England 10-machine 39-bus power system, indicate that the power system under the proposed FTBFOSMEC can obtain effective, robust and superior performances compared with the existing control strategies under different operating circumstances.