Dynamic Surface Control Technique Research Articles

Adaptive fault-tolerant control for switched nonlinear systems based on command filter technique

This article put forward an adaptive neural fault-tolerant control strategy for a class of switched nonlinear systems subject to actuator fault by means of the command filter approach. By using neural networks, the unknown nonlinear functions of the system under consideration are approximated, while its unmeasurable states are estimated by establishing a switched observer. Furthermore, the “explosion of complexity” issue, which arises from the derivatives of virtual controllers, is addressed with the command filter method. In order to reduce filter errors and overcome the drawbacks of the most traditional approaches, such as the ones based on dynamic surface control techniques, an error compensation mechanism was developed. In summary, by taking advantage of the command filter approach, backstepping algorithm, and average dwell time method, an adaptive NNs fault-tolerant controller is established for the system under consideration. Finally, the designed controller has the ability to make the reference signal which can be tracked by the output of the system as close as possible and the boundness of all signals within the closed-loop system can be guaranteed. The usefulness of the designed controller is illustrated by the simulation example.

Adaptive composite neural network disturbance observer-based dynamic surface control for electrically driven robotic manipulators

This paper presents an adaptive backstepping control scheme for electrically driven robotic manipulator (EDRM) system with uncertainties and external disturbances by using neural network disturbance observer (NNDO) and dynamic surface control (DSC) design technique. NNDO is employed to estimate the uncertainties and external disturbances such that the priori information of the unknown dynamics will not be needed. To overcome the problem of “explosion of complexity” inherent in the backstepping design method, the DSC technique is integrated into the adaptive backstepping control design framework, where the NNDOs with adaptive composite law are utilized to compensate the uncertainties and external disturbances of EDRM. Based on the Lyapunov stability theory, it can be proven that the closed-loop system is stable in the sense that all the variables are guaranteed to be uniformly ultimately bounded. The results of simulation and experimental tests demonstrate the approximation capability of NNDO and the effectiveness of the proposed adaptive DSC scheme.

Event-triggered adaptive fixed-time NN control for constrained nonstrict-feedback nonlinear systems with prescribed performance

In this paper, an observer-based adaptive fixed-time control strategy is put forward for uncertain nonstrict-feedback nonlinear systems subject to prescribed performance, full-state constraints and event-triggered mechanism. The difficulty of control design is to use the state observer to estimate unmeasurable states for nonstrict-feedback form in the fixed-time convergence setting. Neural networks are implemented to model the unknown nonlinearities of system. Via introducing fixed-time theory and asymmetric barrier Lyapunov function (ABLF), the fixed-time control problem of the full-state contrained nonlinear system with prescribed performance is solved. Meanwhile, the problem of “explosion of complexity” caused by backstepping technique is averted by utilizing the dynamic surface control technique. Furthermore, an event-triggered controller is devised, which can significantly save communication resources. Moreover, it is concluded that all signals involved are bounded, full-state constraints are not transgressed, tracking error remains within a prescribed domain and Zeno phenomenon is completely circumvented. Finally, the effectiveness of the proposed algorithm is verified by some simulation results.

Robust adaptive fuzzy control for non-strict feedback switched nonlinear systems with unmodeled dynamics

In this paper, a novel fuzzy adaptive output-feedback control design scheme is concerned for a class of non-strict feedback switched nonlinear systems. The considered nonlinear system contains arbitrary switching, unmodeled dynamics, unmeasured states, and unknown nonlinearities. In the design process, fuzzy logic systems (FLSs) are utilised to identify the unknown nonlinear functions. A state observer without depending on the switching signal is constructed to deal with the unmeasured states. By combining adaptive backstepping with dynamic surface control (DSC) technique, a fuzzy adaptive control scheme is presented to overcomes the ‘explosion of complexity’ problem. It is proved that all the signals of the closed-loop system can be bounded by the common Lyapunov function theory. Finally, a simulation example is provided to elaborate the feasibility and effectiveness of the proposed control method.

Performance-Guaranteed Fault-Tolerant Control for Uncertain Nonlinear Systems via Learning-Based Switching Scheme.

This article is concerned with the challenge of guaranteeing output constraints for fault-tolerant control (FTC) of a class of unknown multi-input single-output (MISO) nonlinear systems in the presence of actuator faults. Most industrial systems are equipped with redundant actuators and a fault detection-isolation mechanism for accommodating unexpected actuator faults. To simplify the system design and reduce the risk of false alarm or missed detection brought by the detection unit, a learning-based switching function scheme is proposed to automatically activate different sets of actuators in a rotational manner without human intervention. By this means, no explicit fault detection mechanism is needed. An additional step has been made to guarantee that the system output remains in user-defined time-varying asymmetric output constraints all the time during the occurrence of failures by utilizing error transformation techniques. The stability of the transformed system can equivalently deliver the result that the original system output stays in the required bounds. Hence, system crash or further catastrophic outcomes can be avoided. A neural network is integrated to embody the adaptive FTC design for dealing with unknown system dynamics. The dynamic surface control (DSC) technique is also invoked to decrease complexity. Furthermore, the stability analysis is carried out by the standard Lyapunov approach to guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the simulation results are provided to verify the effectiveness of the proposed scheme.

Adaptive Fuzzy Dynamic Surface Control for Nonlinear Systems with Time-Varying Input Delay and Sampled Data

This paper studies fuzzy adaptive dynamic surface control strategy for nonlinear system with sampled data and time-varying input delay. By utilizing the approximated property of fuzzy logic systems (FLSs), a fuzzy estimator (FE) model is designed to identify the states of the original system, which is mainly used to provide information of estimation states to replace the sampled data of the nonlinear controlled system. In the proposed strategy, with the help of sampled-data activity, an integral term is designed to compensate the problem of time-varying input delay. Moreover, by invoking the dynamic surface control (DSC) technique, the problem of ‘explosion of complexity’ has been overcame. And the developed control strategy demonstrates that all signals of the controlled system are semi-globally uniformly ultimately bounded (SGUUB). Ultimately, two numerical simulation examples are given to prove the feasibility of the developed control method and theory.

Quantized Adaptive Finite-Time Bipartite NN Tracking Control for Stochastic Multiagent Systems.

This article investigates the quantized adaptive finite-time bipartite tracking control problem for high-order stochastic pure-feedback nonlinear multiagent systems with sensor faults and Prandtl-Ishlinskii (PI) hysteresis. Different from the existing finite-time control results, the nonlinearity of each agent is totally unknown in this article. To overcome the difficulties caused by asymmetric hysteresis quantization and PI hysteresis, a new distributed control method is proposed by adopting the adaptive compensation technique without estimating the lower bounds of parameters. Radial basis function neural networks are employed to estimate unknown nonlinear functions and solve the problem of algebraic loop caused by the pure-feedback nonlinear systems. Then, an adaptive neural-network compensation control approach is proposed to tackle the problem of sensor faults. The problem of the "explosion of complexity" caused by repeated differentiations of the virtual controller is solved by using the dynamic surface control technique. Based on the Lyapunov stability theorem, it is proved that all signals of the closed-loop systems are semiglobal practical finite-time stable in probability, and the bipartite tracking control performance is achieved. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

Decentralized Adaptive Fuzzy Tracking Control for a Class of Nonlinear Uncertain Interconnected Systems With Multiple Faults and Denial-of-Service Attack

This article studies the decentralized adaptive fuzzy tracking control problem for a class of large-scale interconnected uncertain systems while considering faults and denial-of-service attack. It is assumed that nonlinear term, interconnected term, actuator faults coefficients, and sensor faults coefficients are unknown. Thus, fuzzy logic system and adaptive method are utilized to deal with these unknown terms. Backstepping and dynamic surface control technique are used in control design procedure and a new algorithm is proposed to obtain a switching observer gain for tracking problems with faults and cyber-attack. It turns out that the designed controller can compensate the effect of faults and uncertainties, all the signals in close-loop systems are bounded, and the tracking error converges to a small neighborhood of origin. Finally, a simulation example is given to prove the feasibility and effectiveness of the theoretical findings in this article.

Adaptive fuzzy finite‐time fault‐tolerant control of nonlinear systems with state constraints and input quantization

SummaryThis article concentrates on an adaptive finite‐time fault‐tolerant fuzzy tracking control problem for nonstrict feedback nonlinear systems with input quantization and full‐state constraints. By utilizing the fuzzy logic systems and less adjustable parameters method, the unknown nonlinear functions are addressed in each step process. In addition, a dynamic surface control technique combined with fuzzy control is introduced to tackle the variable separation problem. The problem for the effect of quantization and unlimited number of actuator faults is tackled by a damping term with smooth function in the intermediate control law. Finite‐time stability is achieved by combining barrier Lyapunov functions and backstepping method. The finite‐time controller is designed such that all the responses of the systems are semiglobal practical finite‐time stable and ensured to remain in the predefined compact sets while tracking error converges to a small neighborhood of the origin in finite time. Finally, simulation examples are utilized to testify the validity of the investigated strategy.

Hybrid threshold strategy‐based adaptive tracking control for nonlinear stochastic systems with full‐state constraints

SummaryThis article focuses on the adaptive tracking control problem for a class of interconnected nonlinear stochastic systems under full‐state constraints based on the hybrid threshold strategy. Different from the existing works, we propose a novel pre‐constrained tracking control algorithm to deal with the full‐state constraint problem. First, a novel nonlinear transformation function and a new coordinate transformation are developed to constrain state variables, which can directly cope with asymmetric state constraints. Second, the hybrid threshold strategy is constructed to provide a reasonable way in balancing system performance and communication constraints. By the use of dynamic surface control technique and neural network approximate technique, a smooth pre‐constrained tracking controller with adaptive laws is designed for the interconnected nonlinear stochastic systems. Moreover, based on the Lyapunov stability theory, it is proved that all state variables are successfully pre‐constrained within asymmetric boundaries. Finally, a simulation example is presented to verify the effectiveness of proposed control algorithm.

Adaptive Implicit Inverse Control for a Class of Discrete-Time Hysteretic Nonlinear Systems and Its Application

This article proposes an adaptive implicit inverse control scheme for a class of discrete-time hysteretic nonlinear systems. The Prandtl–Ishlinskii model is employed to characterize the hysteresis loop in piezoelectric actuator. The main contributions are as follows: 1) by using the dynamic surface control technique, which introduces the digital first-order low-pass filter, the original control system are not required to be transformed into an unknown special form; 2) the hysteresis implicit inverse compensator is constructed to overcome the hysteresis, which implies that the hysteresis item coupled with control signal is treated as the temporary control signal from which the method of searching the approximately control signal is designed; and 3) by employing the experimental platform of the piezoelectric positioning stage, the experimental verifications of the designed discrete-time adaptive controller are implemented. It is proved that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded and the experimental results show the effectiveness of the proposed adaptive dynamic surface discrete-time motion control (ADSDMC) scheme.

Adaptive output-feedback tracking control for a class of nonlinear systems with input saturation: a multi-dimensional Taylor network-based approach

For the nonlinear systems with input saturation constraint, a novel MTN-based output-feedback control strategy is developed via backstepping in this paper. Firstly, an MTN-based nonlinear state observer is designed to estimate the unmeasured states. Secondly, multi-dimensional Taylor networks (MTNs) are used to deal with the unknown nonlinear functions, based on this, the procedure of the adaptive MTN output-feedback controller design is developed by combining backstepping approach and dynamic surface control technique, and then the stability of the closed-loop system is proved based on the principle of Lyapunov stability theory. Finally, the theoretical results of this paper are demonstrated by two examples.

Finite-Time Adaptive Fuzzy DSC for Uncertain Switched Systems

The finite-time adaptive fuzzy tracking control problem for a class of strict-feedback uncertain switched systems is investigated in this paper. Based on fuzzy approximation and adaptive dynamic surface control (DSC) technique, a finite-time adaptive state feedback fuzzy controller is developed via the common Lyapunov functions. Different from the existing works on uncertain switched systems, the DSC control scheme is developed based on a nonlinear filter to solve the “explosion of complexity” problem, and the structure of the proposed fuzzy controller is simple. Under the designed controller, all the signals of the closed-loop system remain semi-globally bounded, and within a finite-time interval, the system tracking error converges to an arbitrarily small region. That is, the semi-globally practical finite-time stability of the controlled system is guaranteed. To show the availability of the presented control scheme, a simulation example is given in this paper.

Robust adaptive neural trajectory tracking control of surface vessels under input and output constraints

In this paper, a novel robust adaptive control scheme is developed for the trajectory tracking of surface vessels in the presence of dynamic uncertainties, unknown time-varying disturbances and input and output constraints. Firstly, the output constraint problem is transformed into the constraint problem of trajectory tracking error by coordinate transformations. Then, a nonlinear transformation is introduced to transform the tracking error into the transformed variable, with the aid of which the output constraint problem of surface vessel trajectory tracking is transformed into the boundedness problem of transformed variable, such that the various types of output constraint boundaries including constant, time-varying, symmetry and asymmetry ones can be handled in a unified framework. An auxiliary dynamic system (ADS) is applied to handle the input saturation effect. Incorporating the nonlinear transformation, the radial basis function neural networks and the ADS into dynamic surface control technique, a novel robust adaptive neural trajectory tracking control law is designed. It is theoretically proved that all signals in the closed-loop trajectory tracking control system of surface vessels are locally uniformly ultimately bounded and the actual positions and heading of surface vessels are guaranteed to lie in the constrained region. Simulation results on a scale model vessel illustrate the effectiveness of the proposed control scheme.

Integrated guidance and control for missile with narrow field-of-view strapdown seeker

Due to the removal of the mechanically stable platform in the conventional gimbaled seeker, the strapdown seeker’s measurement is coupled with the missile body attitude motion, such that the inertial line-of-sight (LOS) angular rate required to implement traditional guidance laws cannot be measured, and the field-of-view (FOV) limit must be considered when designing guidance and control systems for a strapdown homing missile. To address these practical problems, an integrated guidance and control (IGC) scheme with considering the FOV limit is proposed in this paper. A novel IGC model is first derived based on the body-LOS (BLOS) angle that a strapdown seeker can directly measure, and then an IGC controller is designed using the dynamic surface control technique. A great merit of this design is that the inertial LOS angle and its angular rate are not needed, and thus the filters/estimators required to extract this guidance information in previous studies can be canceled. Next, by using the output to input saturation transformation (OIST) technique, the FOV limit, which is always considered as a state/output constraint, is transformed to a time-varying boundary limitation on the control input, and then is handled simultaneously with the actuator saturation constraint. Finally, extensive numerical simulations against both stationary and moving targets are performed to fully demonstrate the efficiency of the proposed IGC law.

Observer‐based decentralized adaptive neural control for uncertain interconnected systems with input quantization and time‐varying output constraints

SummaryIn this article, observer‐based decentralized adaptive neural control is proposed for uncertain interconnected nonlinear systems with input quantization and time‐varying output restrictions. Decentralized hysteresis quantizer is employed to handle input signal. The unmeasured states are estimated by designing K‐filters. A Lyapunov description is used to dispose of state unmodeled dynamics. The constrained interconnected nonlinear systems are transformed into novel interconnected nonlinear systems without output constraints by constructing invertible nonlinear mappings. Dynamic surface control technique and a hyperbolic tangent function are adopted to design decentralized controller, which has a simpler structure. Stability analysis indicates that all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded and system outputs satisfy the constraints. Two numerical examples are used to verify the effectiveness of the theoretical findings.

An adaptive dynamic surface control of output constrained stochastic nonlinear systems with unknown control directions

SummaryThis article is concerned about an adaptive dynamic surface control (DSC) of output constrained stochastic nonlinear systems with unknown control directions and unmodeled dynamics. Nonlinear mapping‐based backstepping control design is presented for stochastic nonlinear systems with output constraint. The explosion of complexity exists in tradition backstepping method is avoided by using the DSC technique. The radial basis function neural networks are employed to deal with unknown nonlinear functions. Nussbaum gain technique is employed to handle the unknown control directions. And a dynamic signal is employed to dominate the unmodeled dynamics. The adaptive controller is designed can ensure that the tracking error converges on a small region of the origin. And all signals of the closed‐loop systems are semiglobal uniformly ultimately bounded. Finally, the results of the simulation cases are provided to show the effectivity of the designed controller scheme.

Finite-Time Adaptive Output Feedback Control for MIMO Nonlinear Systems With Actuator Faults and Saturations

This article addresses the finite-time tracking control for multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems with actuator faults and saturations. First, a fuzzy state observer is constructed to approximate the unmeasured system states, where the restrictions of the known actuator faults are removed from the observer design. Based on the state observer, a novel adaptive output feedback control is then proposed to achieve favorable tracking performance even if actuator saturations and faults occur. Also, the nonlinear functions in the MIMO nonlinear systems are not required to follow the linearly parameterization or growth conditions making the control design more generally available. Furthermore, the dynamic surface control technique is adopted to avoid tedious analytic computations inherent in the backstepping procedure. It can be proved that the proposed control can not only guarantee the closed-loop system states bounded, but also regulate the tracking errors to a small neighborhood around the equilibrium in finite time despite the existence of the actuator saturations and faults. Finally, comparative simulations are carried out to demonstrate the feasibility and effectiveness of the theoretical results.

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.

Adaptive finite-time output-feedback control design for switched pure-feedback nonlinear systems with average dwell time

In this paper, the issue of adaptive fuzzy tracking control is addressed for a class of completely non-affine uncertain switched pure-feedback nonlinear systems with unmeasurable states. In such a system, the average dwell time switching rule is adopted, and its stability is discussed for the first time in the sense of finite time. A fuzzy logic system-based switched observer is constructed to approximate unmeasurable states, which reduces the conservativeness stemming from utilizing a common observer for all subsystems. In addition, filter signals are applied to avoid algebraic loop problem existing in adoption of the controller. Then, a novel adaptive finite-time control strategy is developed on the basis of multiple Lyapunov functions approach, dynamic surface control and backstepping technique. It is proved that the designed controllers of subsystems can ensure that all the closed-loop signals are bounded under a class of switching signals with average dwell time, while the tracking error can converge to a small area of the origin in a finite time. Finally, two simulation examples are given to demonstrate the validity of the proposed control scheme.