Dynamic Surface Control Technique Research Articles

State-observer-based asymptotic tracking control for electro-hydraulic actuator systems with uncertainties and unmeasurable velocity

In this paper, a velocity estimation-based adaptive dynamic surface asymptotic tracking control strategy for electro-hydraulic actuator (EHA) systems subjected to uncertainties and unmeasurable velocity is proposed. Firstly, an accurate velocity signal is estimated by constructing a new state observer, reducing the influence of measurement noise, system cost, and installation complexity. Moreover, the observer's robust gain is updated by an adaptive law, reducing the conservativeness of its selection. Then, a continuous friction model based on the desired velocity signal is used for feedforward compensation to alleviate the negative effect of estimation noise. Moreover, the adaptive dynamic surface control (ADSC) technique is incorporated to avoid “complexity explosion” and eliminate filtering errors via adaptive terms. Finally, the asymptotical stability of the closed-loop system is theoretically proved via Lyapunov analysis, while the efficacy of the proposed control strategy is experimentally validated and numerically verified.

Distributed adaptive control for multiple unmanned aerial vehicles with state constraints and input quantization

AbstractThis paper investigates the distributed fault‐tolerant tracking control (FTTC) problem for multiple unmanned aerial vehicles (UAVs) in the presence of state constraints, input quantization and actuator failures. The unified barrier function (UBF) is first developed to convert the original constrained system model into an unconstrained one, which accomplishes the dynamic constraints of all states. Moreover, a cerebellar model neural network (CMNN) is introduced to estimate the lumped effect of unknown disturbances and actuator failures. Then, a distributed formation flight fault‐tolerant controller (FTC) is designed to guide the UAVs along the desired trajectories based on sliding mode manifold and dynamic surface control technique. Meanwhile, the unknown input chattering and hysteresis quantizer errors in the UAVs system are simultaneously handled by adding the compensation term of FTC. Furthermore, the finite time stability analysis is implemented for all tracking errors and synchronization errors of the cooperative flight control system (CFCS). Finally, a comparative simulation is conducted to demonstrate the effectiveness of the FTTC scheme proposed in this study.

Disturbance Observer-Based Safe Tracking Control for Unmanned Helicopters with Partial State Constraints and Disturbances

In this paper, a disturbance observer-based safe tracking control scheme is proposed for a medium-scale unmanned helicopter with rotor flapping dynamics in the presence of partial state constraints and unknown external disturbances. A safety protection algorithm is proposed to keep the constrained states within the given safe-set. A second-order disturbance observer technique is utilized to estimate the external disturbances. It is shown that the desired tracking performance of the controlled unmanned helicopter can be achieved with the application of the backstepping approach, dynamic surface control technique, and Lyapunov method. Finally, the availability of the proposed control scheme has been shown by simulation results.

Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems

A fuzzy adaptive tracking control scheme is studied for a family of uncertain systems with immeasurable system states. The controller takes up few computation and transmission resources to achieve prescribed boundaries of the dynamic and steady-state performance indicators. Compared with the existing schemes, the low computational complexity is reflected in the following two points: (1) a fuzzy state observer is introduced, where only the estimation of states are incorporated into the input space of fuzzy logic systems (FLSs). (2) The problem of complexity explosion can be avoided without utilizing additional command filters or auxiliary dynamic surface control techniques. In addition, using the event-triggered control scheme, the data in the transmission is significantly reduced. Finally, the effectiveness of the scheme is fully verified by simulation.

Extended Sliding Mode Observer-Based Output Feedback Control for Motion Tracking of Electro-Hydrostatic Actuators

This paper develops a novel output feedback control scheme for the motion-tracking problem of an electro-hydrostatic actuator (EHA) in the presence of model uncertainties and external disturbances. Firstly, a simplified third-order system model of the studied EHA is established using theoretical methods. For the first time, an extended sliding mode observer (ESMO) is introduced to simultaneously account for the shortage of unknown system states and modeling imperfections. Based on this, a robust nonlinear controller is developed using the backstepping control framework to stabilize the closed-loop system. This controller integrates estimates of immeasurable system states and lumped disturbances to deal with their adverse impacts. Moreover, the dynamic surface control (DSC) technique is employed to effectively mitigate the computational burden of the traditional backstepping framework. An ultimately uniformly bounded (UUB) performance is assured by using the recommended method. Furthermore, the stability of not only the observer but also the closed-loop system is concretely analyzed by using the Lyapunov theory. Finally, experiment results under various working scenarios are given to convincingly demonstrate the advantage of the suggested method in comparison with some reference control approaches.

Switching Event-Triggered Adaptive Resilient Dynamic Surface Control for Stochastic Nonlinear CPSs With Unknown Deception Attacks.

This work concentrates on the adaptive resilient dynamic surface controller design problem for uncertain nonlinear lower triangular stochastic cyber-physical systems (CPSs) subject to unknown deception attacks based on a switching threshold event-triggered mechanism. The adverse effect of deception attacks on the stochastic CPSs is that the exact system state variables become unavailable. Furthermore, it should be emphasized that the coexistence of unknown nonlinearities, stochastic perturbations, and unknown sensor and actuator attacks makes it a very difficult and challenging event to implement the control design. To get the desired controller, radial basis function (RBF) neural networks (NNs) are introduced so that the design obstacle caused from the unknown nonlinearities can be easily solved. On this basis, in order to save resources and effectively transmit, the event-triggered control scheme based on a switching threshold strategy is further considered. In the backstepping design process, the dynamic surface control (DSC) technique is presented to deal with the issue of "explosion of complexity." By skillfully designing a new coordinate transformation and the attack compensators, the problem of unknown deception attacks is successfully handled. Under our proposed control scheme, all the closed-loop signals are bounded in probability and the stabilization errors converge to an adjustable neighborhood of the origin in probability. Finally, the simulation results on the double chemical reactor show the validity of the proposed design scheme.

Adaptive neural dynamic surface control of load/grid connected voltage source inverters with LC/LCL filters

Utilizing passive filters such as L, LC and LCL is preferred to cancel out high-frequency harmonics caused by pulse width modulation of voltage source inverters. However, the LC and LCL filters have shown better harmonic attenuation than the conventional L filter. Nevertheless, the control process of LC and LCL filters is more complicated due to their higher-order dynamics. The problem gets more challenging in the presence of uncertainties such as load and grid impedance variations. To overcome these challenges, two novel adaptive neural dynamic surface controllers are proposed for LC and LCL filters in the load and grid-connected modes, respectively. Meanwhile, the issue of computational complexity inherent in the conventional backstepping method is avoided here by utilizing the dynamic surface control technique. Furthermore, the matched and unmatched uncertainties of LC/LCL filters are approximated via multi-input multi-output radial basis function neural networks. Stability of the closed-loop systems is guaranteed by converging the tracking errors to a small neighborhood of the origin. Simulations are given to illustrate the effectiveness and potential of the proposed adaptive neural dynamic surface control methods under the load and grid impedance changes.

Adaptive control for a class of switched nonlinear systems with output constraints and quantized input signal

The adaptive tracking problem for a class of strict-feedback switched nonlinear systems with output constraints, quantization input, distributed time-varying delays and external disturbances is studied in this paper. Firstly, we introduce the hyperbolic tangent function to handle the quantized signal in the design of the controller. For the nonlinear term and distributed time-varying delay of the system, the integral term is introduced into the candidate functional to compensate it. Secondly, based on Dynamic Surface Control (DSC) technique and Barrier Lyapunov function (BLF) method, an adaptive controller is designed to guarantee the stability of the controlled closed-loop system, and the system tracking error can converge to the origin without violating the constraints. Finally, simulation results verify the effectiveness of the proposed control strategy.

Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as network resource constraints and discontinuities of course and yaw caused by data transmission, are taken into account. To tackle the issue of network resource constraints, an event-sampled scheme is developed to obtain the course data, and a novel event-sampled adaptive neural-network-based state observer (NN–SO) is developed to achieve the state reconstruction of discontinuous yaw. Using a backstepping design method, an event-sampled mechanism, and an adaptive NN–SO, an adaptive neural output feedback (ANOF) control law is designed, where the dynamic surface control technique is introduced to solve the design issue caused by the intermission course data. Moreover, an event-triggered mechanism (ETM) is established in a controller–actuator (C–A) channel and a dual-channel event-triggered adaptive neural output feedback control (ETANOFC) solution is proposed. The theoretical results show that all signals in the closed-loop control system (CLCS) are bounded. The effectiveness is verified through numerical simulations.

Finite-Time Adaptive Dynamic Surface Asymptotic Tracking Control of Uncertain Multi-Agent Systems with Unknown Control Gains

In this work, the finite-time asymptotic tracking control problem of uncertain multi-agent systems with unknown control gains is studied. For the unknown control gain of each subsystem in multi-agent systems, we consider using the Nussbaum gain function techniques to handle them. To deal with the unknown uncertain nonlinear dynamics, the radial basis function neural network is introduced in each step of the dynamic surface control design. In addition, a nonlinear compensating term with the estimation of an unknown bounded parameter is designed to avoid repeated differentiation of each virtual control law. Then, based on the neural network control method, dynamic surface control technique, and finite-time control theory, an adaptive neural network finite-time dynamic surface control law is finally designed. Using stability analysis, it is proven that the presented adaptive control law can guarantee all signals of the closed-loop system semi-global practical finite-time stable, and the tracking error of each follower agent can converge to a small neighborhood of zero in finite time. Finally, a class of single-link robot systems is provided to illustrate the effectiveness of the designed control law.

Design of a Novel NNs Learning Tracking Controller for Robotic Manipulator with Joints Flexibility

The precise tracking control problem for the robotic manipulator with flexible joints, subjected to system uncertainties and external disturbances, is addressed. A novel control scheme is presented that does not use link velocity measurements and high-order derivatives of the link states. The control scheme employs neural networks-based observers to estimate both motor velocity and link velocity. By using the virtually applied torque, the link controller is designed based on rigid link dynamics, and the motor controller is designed using the dynamic surface control technique. The proposed control scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The simulation results confirm our theoretical analysis, and a comparison study demonstrates the advantages of the proposed control scheme compared to the standard DSC method.

Fuzzy adaptive tracking control of fractional-order multi-agent systems with partial state constraints and input saturation via event-triggered strategy

This paper addresses the problem of adaptive tracking control for fractional-order multi-agent systems subject to input saturation and partial state constraints, utilizing an event-triggered strategy with output feedback. The interconnected graph of multiple agents is assumed to be directed and contain a spanning tree. Since only the system output is available, a fractional-order state observer is designed to obtain the unmeasurable states. The nonlinear dynamics of the system are assumed to be unknown and are approximated by a fuzzy logic system. By utilizing the barrier Lyapunov function, backstepping procedure and dynamic surface control technique, an event-triggered adaptive control scheme is proposed to ensure that the constrained states remain within their bounds, all system signals are bounded, tracking error converges to a bounded set containing the origin, and no Zeno behavior occurs. Finally, the validity of the presented method is verified by simulation.

Prescribed performance LOS guidance-based dynamic surface path following control of unmanned sailboats

The unmanned sailboat path following control problem with model uncertainties and unknown external disturbances is addressed. Firstly, different sailing strategies are set for the sailboat under three different wind fields in nature for its vulnerability to the true wind field so that the sailboat can complete the path-following task under different wind directions. Secondly, a barrier Lyapunov function with prescribed performance is used to limit position errors and heading errors of the unmanned sailboat within prescribed constraints. A radial-based function neural network technique is then introduced to approximate model uncertainties. Then an adaptive law is introduced to estimate an upper bound on the sum of the external environmental disturbances and neural network approximation errors. A path-following control scheme for the unmanned sailboat is proposed by the backstepping method and combined with the dynamic surface control technique. Finally, stability analysis is performed by Liapunov functions, demonstrating that the proposed control method can guarantee that all error signals in the closed-loop system are semi-global uniform ultimate bounded and that the prescribed performance constraints are not violated. Finally, the effectiveness of the scheme is demonstrated by simulation.

Event‐triggered fuzzy adaptive control of incommensurate fractional order nonlinear systems

AbstractThis paper focuses on the event‐triggered control of nonstrict‐feedback incommensurate fractional order systems with external disturbances. To release more communication resources and reduce the triggered time instant, we adopt the relative‐threshold‐based event‐triggered strategy and prove that the Zeno behavior does not exist. We utilize the fuzzy systems to approximate the nonlinear systems and design an adaptive update law to estimate the approximation errors and external disturbances. To conquer the dimension explosion phenomenon in the backstepping process, we design a fractional order filter and apply the dynamic surface control technique. The closed‐loop system is semiglobally stable and the tracking error converges to a small neighborhood of equilibrium point under the proposed control approach. Finally, the validity of the developed controller is verified by the simulation examples.

Neuroadaptive Dynamic Surface Asymptotic Tracking Control of a VTOL Aircraft with Unknown Dynamics and External Disturbances

This work studies the asymptotic tracking control problem of a vertical take-off and landing (VTOL) aircraft with unknown dynamics and external disturbances. The unknown nonlinear dynamics of the VTOL aircraft are approximated via the introduction of radial basis function neural networks. Then, the weight update laws are designed. Furthermore, the parameter update control laws are presented to deal with the errors generated during the approximation process and the external disturbances of the aircraft system. Moreover, first-order filters are introduced to avoid repeated differentiation of the designed virtual control laws, thereby effectively eliminating the “complexity explosion” problem caused by traditional backstepping control. Based on the application of the neural network control method, dynamic surface control technique, weight update laws and parameter update control laws, neuroadaptive dynamic surface control laws for the aircraft system are finally proposed. Theoretical analysis shows that the proposed control law can ensure that the aircraft system asymptotically tracks the reference trajectories and the tracking errors can converge to a small neighborhood of zero by choosing the appropriate designed parameters. Finally, simulation examples are provided to verify the effectiveness of proposed control laws.

Robust adaptive control for dynamic positioning vehicles in presence of adjustable threshold rule and input constraints

In this work, we present a new class of event-triggered mechanism for dynamic positioning vehicles under input saturation and system uncertainties. The class is characterized by the introduction of an internal dynamic variable, which motivated the proposed name of the dynamic event-triggered mechanism. Different from the traditional event-triggered mechanism with invariable thresholds, the proposed mechanism with the adjustable threshold parameters could reduce communication burden. The internal adjustable threshold parameters are updated online in an adaptive manner. Furthermore, an auxiliary system is employed to resolve the input saturation of actuators. System uncertainty and explosion of complexity are resolved by combining dynamic surface control technique and robust neural damping technique. Due to the virtues of the above design, the proposed algorithm is concise in form and easy to be implemented in the marine practice. Based on the Lyapunov theory, it is proved that the closed-loop system is semi-globally uniform ultimate bounded (SGUUB). Finally, the effectiveness and feasibility of the proposed algorithm are verified by simulation experiments.

Robust adaptive synchronized event-triggered formation control of USVs with the fault amendment strategy

In this paper, the formation control of the underactuated surface vehicles (USVs) is studied in the presence of the condition of actuator fault and limited communication resources. Considering the performance characteristics of the underactuated surface vehicle (USV), a robust adaptive synchronized event-triggered formation control method is proposed under the usage of the event-triggered control (ETC) and robust neural damping technique. In consideration of the constant energy expenditure of the law of adaptation, the formulated control method is set in the mechanism of triggering by both the controller and the gain related adaptive law, which could cause the communication to reduce more much power consumption. The robust adaptive rule is fault compensated in the way of combining robust neural damping and dynamic surface control (DSC) techniques. Since the fault amendment strategy compresses inconclusive bias fault parameters and model uncertainty parameters, system stability can be enhanced under the premise of reducing the influence of adaptive parameters. A huge volume of work has taken place to ensure semi-global uniform ultimate bounded (SGUUB) stability. To conclude, the effectiveness of the algorithm is verified by two arithmetic examples.

Adaptive active fault-tolerant dynamic surface load following controller for a modular high-temperature gas-cooled reactor

The control of nuclear reactors is one of the most challenging requirements in performing the load following operation mode; however, most of the previous studies either do not consider the adverse effects of the lumped uncertainty or rely on the assumption of possessing healthy actuators without any faults. Besides, the recently published backstepping load following controllers may result in the problem of “explosion of complexity” due to repeatedly differentiating the virtual control laws. To this end, this paper provides an adaptive active fault-tolerant dynamic surface control strategy for the load following problem of a modular high-temperature gas-cooled reactor suffering from lumped uncertainty and actuator faults. The main contributions of this paper are threefold. (i) a radial basis function neural network is introduced to real-timely learn the lumped uncertainty that contains model uncertainties, parameter variations, external disturbances, and unmeasured states; (ii) a dynamic surface control technique is employed to cope with the problem of “explosion of complexity” induced by traditional backstepping approach; and (iii) an active fault-tolerant controller, incorporated with a fault detection observer and a fault estimator, is developed such that the reliability and safety of the modular high-temperature gas-cooled reactor system could be enhanced. With Lyapunov stability theory, both the stability of the closed-loop system and the error convergence are proved. At the end, comparative simulation studies and analysis using quantitative performance indices indicate the proposed control strategy’s superiority in dealing with lumped uncertainty and actuator faults.

Adaptive tracking control of a new nonlinear energy harvester system based on the cantilever beam structure

In this paper, an energy harvester consisting of a cantilever beam, magnets, and magnetic springs embedded on its structure is proposed to harvest electricity energy. In order to extract energy from the system permanently, an adaptive controller is proposed. With the help of the proposed controller, it is prevented from damping the oscillations of the beam or excessive increasing in the amplitude of these oscillations. Also, the use of the proposed controller provides the possibility that despite the presence of unknown external disturbances such as wind waves and sudden changes of sea waves, as well as the uncertainty of the parameters of the cantilever beam, a permanent energy is harvested from the proposed mechanism. The equations describing the electromechanical behavior of the proposed mechanism are derived from Lagrange equations. On the other hand, dynamic surface control technique is employed to design the adaptive and control laws. The stability of the closed-loop system is shown using Lyapunov stability theory, and the tracking error converges to a small neighborhood around the origin. Numerical simulation for cantilever beam is investigated to clarify the effectiveness of the proposed mechanism and controller.

Harmonic and synchronous compound control of a dual-valve controlled single-rod electro-hydraulic actuator

This article presents a novel harmonic and synchronous compound control scheme based on the dual-valve parallel structure for controlling a single-rod electro-hydraulic actuator with high-precision control and large-flow requirements. Harmonic and synchronous compound control consists of an adaptive robust control layer and a control law allocation layer. In adaptive robust control layer, an adaptive robust control algorithm is designed to deal with the system nonlinearities and uncertainties via backstepping method. Meanwhile, dynamic surface control technique and “lumped flow” are introduced to solve the differential explosion problem and the multiple input-single output control problem. In control law allocation layer, the harmonic and synchronous compound strategy allocates the control law to two valves reasonably based on system flow and valve frequency characteristics. Furthermore, since the effect of the proportional valve dead-zone can be compensated effectively by combining the estimation of the dead-zone flow by adaptive term, the estimation error compensation by robust term, and the allocation method of control law allocation layer, improved tracking performance can be achieved. Finally, both simulations and experiments are demonstrated to illustrate the advantages of the proposed control scheme.