Robust Dynamic Surface Control Research Articles

Robust adaptive asymmetric state constraint control of brush DC motor systems driving a one-link robot manipulator: A feasibility-condition-free method

In this paper, we investigate the tracking control problem for a class of brush direct current (DC) motor systems driving a one-link robot manipulator subject to asymmetric full-state constraints. By constructing a state-dependent nonlinear transformation function (NTF), we present an adaptive robust dynamic surface control (DSC) strategy that can directly address both symmetric and asymmetric state constraints, so that there is no need to convert the problem of state constraint into the constraints on tracking errors as necessitated by the Barrier Lyapunov Function (BLF)-based existing works. Furthermore, by employing the first-order filter and constructing a new coordinate transformation, the demanding feasibility condition imposed on the BLF methods is removed, allowing the designer more freedom to select design parameters. Moreover, it is worth mentioning that under the proposed nonlinear transformation function the extra condition on the constraining function is not required. The effectiveness of the proposed control is verified via the Simulation results.

Robust dynamic surface control of series elastic actuators based on reduced-order extended state observer

The compliance of series elastic actuators (SEAs) is significant for ensuring safe human–machine interaction. However, in practical applications, the SEA is inevitably encountered with unknown disturbances, such as parameter uncertainties, unmodeled dynamics, and environmental interferences. In this paper, a robust dynamic surface control scheme is presented to address the high-precision trajectory tracking problem of the SEA. Firstly, a reduced-order extended state observer (RESO) with only link-side position measurements is designed to estimate the unknown lumped disturbance and unmeasurable system states. Based on the estimated values from the RESO, a robust dynamic surface controller is proposed for the trajectory tracking of the SEA. Additionally, the finite-time filter is introduced to overcome the “explosion of complexity” in the backstepping controller, and the filtering error in the dynamic surface controller is reduced compared with the conventional first- and second-order filters. The stability of the closed-loop system is guaranteed by the Lyapunov theory. Finally, the effectiveness and superiority of the proposed control algorithm are verified through simulations and 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.

Robust Adaptive Neural Control for Wing-Sail-Assisted Vehicle via the Multiport Event-Triggered Approach.

This article presents a robust adaptive neural control algorithm for the wing-sail-assisted vehicle to track the desired waypoint-based route, where the event-triggered mechanism is with the multiport form. The main features of the proposed algorithm are three-fold: 1) the communication burden, in the channel from the sensor to the controller as well as the actuator, has been reduced for the merits of the multiport event-triggered approach. The feedback error signals and the control input will be updated only on the event-triggered time point; 2) for the wing-sail-assisted vehicle, the thrust force is provided by devices with the propeller and the sail. From this consideration, the proper sail force compensation is derived on the basis of information about the current heading angle and the wind direction. The corresponding control law can guarantee the energy-saving for the propeller; and 3) in the algorithm, the system uncertainties are remodeled by the neural-network approximator. Furthermore, by fusion of the robust neural damping and dynamic surface control (DSC) techniques, the corresponding gain-related adaptive law is developed to address constraints of the gain uncertainty and the environmental disturbances. Through the Lyapunov theorem, all signals of the closed-loop control system have been proved to be with the semiglobal uniform ultimate bounded (SGUUB) stability, including the triggered time point and the intermediate triggered interval. Finally, the numerical simulation and the practical experiment are illustrated to verify the effectiveness of the proposed strategy.

Reinforcement learning-based saturated adaptive robust neural-network control of underactuated autonomous underwater vehicles

This paper studies a high-performance intelligent online adaptive robust saturated dynamic surface control framework for underactuated autonomous underwater vehicles by engaging Actor–Critic neural networks in the presence of unmodeled dynamics, uncertainties, ocean disturbances and actuator saturation. The proposed controller is designed based on reinforcement learning method to compensate the effects of unmodeled dynamics and uncertainties more accurately that can lead to a better performance for the controller. The Actor–Critic neural networks are trained real-time by designing online training laws creatively and a new critic function is proposed to supervise the closed-loop performance with the aid of the Critic neural network. The proposed structure for the reinforcement learning method benefits from a model-free algorithm and only relies on the measurable variables of the closed-loop control system. This independency from system dynamics results in considerable low computational burden for the controller and, hence, the proposed control algorithm is efficient computationally. The hyperbolic tangent function is ingeniously used as a saturated stabilizer term to bound the control signals that results in low-amplitude control action and the probability of actuator saturation phenomenon is minimized by learning and compensating the actuator saturation nonlinearity as well. Finally, the stability of the proposed closed-loop system is investigated by the Lyapunov’s direct methodology and simulations along a comparative study with some quantitative assessments certify the contributions of this paper.

Robust saturated dynamic surface controller design for underactuated fast surface vessels including actuator dynamics

In this paper, a saturated trajectory tracking controller is proposed for high-speed surface vessels whose actuator dynamics is not negligible in practice. The hyperbolic tangent function is used as a stabilizer term to design a tracking controller via the dynamic surface control approach to improve the controller performance for all subsystems in the presence of input saturation, unmodeled dynamics, external disturbances and actuator nonlinearity. To this end, adaptive neural networks and adaptive robust controllers are combined to develop a novel saturated dynamic surface controller. Moreover, the proposed controller also compensates the effects of unknown system dynamics, actuator nonlinearity, external kinematic, dynamic and actuator disturbances simultaneously. By using Lyapunov’s stability method, it is shown that all signals of the closed-loop system are uniformly ultimately bounded and simulations are performed to verify the efficacy of the proposed control scheme.

Robust neural event-triggered control for dynamic positioning ships with actuator faults

This paper presents a novel robust neural event-triggered control algorithm to achieve the dynamic positioning operation of marine surface ships in the presence of actuator faults. In the algorithm, the model uncertainty and the “explosion of complexity” are addressed by fusion of the robust neural damping and dynamic surface control techniques. The gain related adaptive law is constructed to compensate the gain uncertainties and the unknown actuator faults, which improved the stability of the ship dynamic loop system. Furthermore, the event-triggered mechanism is introduced to reduce the communication load between the controller and actuator. For merits of the aforementioned design, the proposed algorithm is with the advantages of concise form and easy to be implemented in practical ocean engineering. Based on the Lyapunov theory, rigorous analysis is proved to guarantee the semi-global uniform ultimate boundedness (SGUUB) of the closed-loop system. Finally, numerical simulations are conducted to demonstrate the validity and feasibility of the proposed algorithm.

On performance recovery of robust dynamic surface control

SummaryRobust dynamic surface control (RDSC) is effective in alleviating the implementation difficulty of backstepping‐based multiple‐surface sliding control (MSSC) for a class of strict‐feedback nonlinear systems with mismatched uncertainties. However, the synthesis and analysis of the classical RDSC are conservative, which not only may lead to an impractical control law but also cannot fully reveal the technical nature of RDSC. This article provides a comprehensive study of a preferred RDSC law with an approximation of the signum function based on the singular perturbation theory. By formulating the control problem into a singular perturbation form, it is proven that the preferred RDSC recovers the performance of MSSC as the decrease of a filter parameter. The attractive features of the preferred RDSC revealed during the proposed synthesis and analysis include: (a) the control gain can be significantly reduced resulting in a sharp decrease of control energy and (b) the closed‐loop stability can be guaranteed by only decreasing the filter parameter. Simulation results have been shown to be consistent with the theoretical findings.

Adaptive robust dynamic surface control for uncertain strict‐feedback nonlinear systems using fuzzy logic systems

AbstractIn this paper, an adaptive robust stabilization problem is dealt with for a class of uncertain strict‐feedback nonlinear systems in the presence of unknown structure uncertainties, external disturbances, and unknown time‐varying virtual control coefficients. It is not required to know the upper bounds of external disturbances, as well as the upper and lower bounds of unknown time‐varying virtual control coefficients. The controller is designed by adopting backstepping. In addition, to avoid suffering from the problem of ‘explosion of terms’, a dynamic surface control approach is employed by introducing the first‐order low‐pass filter. Furthermore, at every step of the backstepping design procedure, fuzzy logic systems are used to approximate the unknown structure uncertainties. In particular, the norms of weight matrices and the upper bounds of approximation errors of fuzzy logic systems are supposed to be unknown. It is also shown that the proposed controller can guarantee the uniform boundedness of uncertain strict‐feedback nonlinear systems. Finally, the simulation for a single‐link manipulator actuated by a brush DC motor is carried out to illustrate the validity of the proposed controller.

Dynamic surface based tracking control of uncertain quadrotor unmanned aerial vehicles with multiple state variable constraints

This study considers the tracking control problem of an uncertain quadrotor unmanned aerial vehicle with model uncertainties and wind gust disturbances, and a novel robust dynamic surface control based multiple state variables constrained control scheme is proposed. Under the presented control framework, the overall quadrotor system is decoupled into a translational subsystem and a rotational subsystem. These two subsystems are connected to each other through common attitude extraction algorithms. For the translational subsystem, the novel robust multiple state variables constrained control inputs are designed to ensure the state variables within the prescribed constraints. For the rotational subsystem, the dynamic surface control based tracking controllers are proposed to track the desired attitudes. In the end, the resulting closed-loop system is proved to be stable in the sense of uniform ultimate boundness, and numerical simulations and experiments are conducted to validate the feasibility and effectiveness of the designed control scheme.

Extended state observer-based robust non-linear integral dynamic surface control for triaxial MEMS gyroscope

SUMMARYThis paper reports an extended state observer (ESO)-based robust dynamic surface control (DSC) method for triaxial MEMS gyroscope applications. An ESO with non-linear gain function is designed to estimate both velocity and disturbance vectors of the gyroscope dynamics via measured position signals. Using the sector-bounded property of the non-linear gain function, the design of an $\mathcal{L}_2$-robust ESO is phrased as a convex optimization problem in terms of linear matrix inequalities (LMIs). Next, by using the estimated velocity and disturbance, a certainty equivalence tracking controller is designed based on DSC. To achieve an improved robustness and to remove static steady-state tracking errors, new non-linear integral error surfaces are incorporated into the DSC. Based on the energy-to-peak ($\mathcal{L}_2$-$\mathcal{L}_\infty$) performance criterion, a finite number of LMIs are derived to obtain the DSC gains. In order to prevent amplification of the measurement noise in the DSC error dynamics, a multi-objective convex optimization problem, which guarantees a prescribed $\mathcal{L}_2$-$\mathcal{L}_\infty$ performance bound, is considered. Finally, the efficacy of the proposed control method is illustrated by detailed software simulations.

Robust composite neural dynamic surface control for the path following of unmanned marine surface vessels with unknown disturbances

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

Energy saving control in separate meter in and separate meter out control system

With the demand for energy efficiency in electro-hydraulic servo system (EHSS) increasing, the separate meter in and separate meter out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system was decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Besides, a fast parameter estimation scheme was proposed to adapt to the parameter change for a better estimation performance. Also, a supply pressure controller with a disturbance observer and a supply flow rate controller with a grey model predictor were investigated and employed to save the power consumption. Finally, experimental results showed that the proposed IARDSC could achieve a good trajectory tracking performance with the fast parameter estimation. Meanwhile, the two energy saving techniques were validated.

Indirect adaptive robust dynamic surface control in separate meter-in and separate meter-out control system

With the demand for energy efficiency in electrohydraulic servo systems (EHSS), the separate meter-in and separate meter-out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system is decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Indirect adaptive robust control (IARC) is proposed to address the internal parameter uncertainties and external disturbances. Dynamic surface control (DSC) is utilized in the design procedure of IARC to deal with the inherent ‘explosion of terms’ problem. The proposed IARDSC simplifies the design procedure and decreases the computational cost of the controller. Besides, a faster parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. Finally, experimental results show that the proposed IARDSC can achieve a good parameter estimation and trajectory tracking performance. Meanwhile, two energy saving techniques are discussed.

Robust adaptive DSC of directly input‐coupled nonlinear systems with input unmodeled dynamics and time‐varying output constraints

SummaryIn this paper, an adaptive robust dynamic surface control is proposed for a class of uncertain nonlinear interconnected systems with time‐varying output constraints and dynamic input and output coupling. The directly coupled inputs and control inputs are both of nonlinear input unmodeled dynamics. To counteract the instable impact of the nonlinear input unmodeled dynamics, normalization signals are designed on the basis of the convergence rates of their Lyapunov functions. With new state variables and control variables being defined, the real control inputs are obtained through solving the equations of intermediate control laws. The time‐varying constraints on output signals are implemented by introducing asymmetric barrier Lyapunov functions. In addition, dynamic signals and decentralized K‐filters are used to deal with the state unmodeled dynamics and to estimate the unmeasurable states, respectively. By the theoretical analysis, the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded, and the output constraints are guaranteed simultaneously. A numerical example is provided to show the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.

An Adaptive Dynamic Surface Controller for Ultralow Altitude Airdrop Flight Path Angle with Actuator Input Nonlinearity

In the process of ultralow altitude airdrop, many factors such as actuator input dead-zone, backlash, uncertain external atmospheric disturbance, and model unknown nonlinearity affect the precision of trajectory tracking. In response, a robust adaptive neural network dynamic surface controller is developed. As a result, the aircraft longitudinal dynamics with actuator input nonlinearity is derived; the unknown nonlinear model functions are approximated by means of the RBF neural network. Also, an adaption strategy is used to achieve robustness against model uncertainties. Finally, it has been proved that all the signals in the closed-loop system are bounded and the tracking error converges to a small residual set asymptotically. Simulation results demonstrate the perfect tracking performance and strong robustness of the proposed method, which is not only applicable to the actuator with input dead-zone but also suitable for the backlash nonlinearity. At the same time, it can effectively overcome the effects of dead-zone and the atmospheric disturbance on the system and ensure the fast track of the desired flight path angle instruction, which overthrows the assumption that system functions must be known.

Indirect adaptive robust dynamic surface control of electro-hydraulic fatigue testing system with huge elastic load

In this article, an electro-hydraulic-based fatigue testing system is considered. Electro-hydraulic system suffers from internal parameter uncertainties and external disturbances. Indirect adaptive robust control has been proposed to address these problems; however, since the mathematical model of this electro-hydraulic system has an order of four, the inherent “explosion of terms” problem makes the controller hard to design, and a large computational cost is needed. Thus, dynamic surface control is utilized in the design procedure of indirect adaptive robust control. The proposed indirect adaptive robust dynamic surface controller simplified the design procedure and decreased the computational cost of the controller. Furthermore, due to the fatigue of the elastic load, fast internal parameter change is encountered in this system. A fast parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. For the mechanical system, a change in the hydraulic circuit is made to address the problem of chattering and impact caused by huge elastic load and to simplify the system model at the same time. Simulations and experiments show that the proposed controller achieves a better parameter estimation and trajectory tracking performance.

A novel DVS guidance principle and robust adaptive path-following control for underactuated ships using low frequency gain-learning

Around the waypoint-based path-following control for marine ships, a novel dynamic virtual ship (DVS) guidance principle is developed to implement the assumption “the reference path is generated using a virtual ship”, which is critical for applying the current theoretical studies in practice. Taking two steerable variables as control inputs, the robust adaptive scheme is proposed by virtue of the robust neural damping and dynamic surface control (DSC) techniques. The derived controller is with the advantages of concise structure and being easy-to-implement for its burdensome superiority. Furthermore, the low frequency learning method improves the applicability of the algorithm. Finally, the comparison experiments with the line-of-sight (LOS) based fuzzy scheme are presented to demonstrate the effectiveness of our results.

Research on Aircraft Attack Angle Control Considering Servo-Loop Dynamics

This paper presents a novel robust attack angle control approach, which can effectively suppress the impacts from system uncertainties and servo-loop dynamics. A second-order linear model of electromechanical servo loop is considered in the modeling and design processes. With regard to the block-structure models facing attack angle control, the multiple robust surfaces and dynamic surface control (DSC) approaches are both employed. By means of Lyapunov function method, the stability conditions of attack angle control systems are, respectively, given without/with considering the servo-loop dynamics in design process. Computer simulation results present that, compared with the attack angle control scheme which does not consider the servo-loop dynamics in design process, the proposed scheme can guarantee that the whole attack angle control system possesses the better comprehensive performances. Moreover, it is easy to be realized in engineering application.

Multiple‐model adaptive robust dynamic surface control with estimator resetting

SummaryA multiple‐model adaptive robust dynamic surface control with estimator resetting is investigated for a class of semi‐strict feedback nonlinear systems in this paper. The transient performance is mainly considered. The multiple models are composed of fixed models, one adaptive model, and one identification model that can be obtained when the persistent exciting condition is satisfied. The transient performance of the final tracking system can be improved significantly by designing proper switching mechanism during the parameter tuning procedure. The semi‐globally uniformly ultimately bounded stability of the closed‐loop system can be easily achieved because of the framework of adaptive robust dynamic surface control. Numerical examples are provided to demonstrate the effectiveness of the proposed multiple‐model controller. Copyright © 2014 John Wiley & Sons, Ltd.