Adaptive Dynamic Surface Control Research Articles

Adaptive Dynamic Surface Control for Servo System Driven by Twin Motors With Unknown Actuator Failures

Adaptive backstepping control is an effective method for servo system driven by twin motors with unknown actuator failures, however, problems such as explosion of complexity make it can not be used in the actual control systems. In this paper, an algorithm of adaptive dynamic surface control is proposed for servo system driven by twin motors with unknown actuator failures, which can eliminate the expansion of the differential term and simplify calculations in the controller design. In addition, all parameters and disturbances in the system model are unknown, which require to be identified online. The simulation results also provided in order to show the effectiveness of the algorithm.

FSE-RBFNNs-Based Robust Adaptive DSC Design for a Larger Class of Nonlinear Strict-Feedback Systems

A novel set-invariance adaptive neural dynamic surface (DSC) control scheme is presented for an extended class of the periodically disturbed nonlinear MIMO strict-feedback systems whose control gain functions are possibly unbounded. The most advanced is that the restrictive bounds assumption is removed after introducing appropriate compact sets, which are constructed in such a way that all the closed-loop trajectories stay in those sets all the time. To tackle the tracking control problem in the presence of more general periodic disturbances, a novel function approximator is well constructed by combining the radial basis function neural networks (RBFNNs) with the Fourier series expansion (FSE). In addition, the DSC technique is employed to overcome the problem of “explosion of complexity”. Furthermore, the Lyapunov theory and invariant set theorem are utilized to prove the closed-loop systems semi-globally uniformly ultimately bounded (SGUUB) stability, and the tracking errors can converge to an arbitrarily small residual set after appropriately choosing design parameters. Finally, the simulation results verify the effectiveness of the proposed method.

A novel control for MDF continuous hot-pressing accurate tracking: adaptive fuzzy approach

In order to guarantee slab thickness precision of Medium Density Fibreboard (MDF), an adaptive fuzzy Dynamic Surface Control (DSC) was investigated. Firstly, a mathematical model for hydraulic servo system of the continuous hot-pressing machine with input saturation was proposed, and the auxiliary system was introduced to compensate the effect of input saturation. Due to the uncertain function in the model, a fuzzy logic system was introduced to approximate it. Subsequently, a slab thickness tracking controller with less complexity was developed with the help of the DSC approach. Simultaneously, an on-line adaptive law was applied to estimate the adjustable parameter vector in the fuzzy system. Finally, experimental results validated that the proposed method can ensure the MDF slab thickness precision in the hot-pressing process.

A novel control for MDF continuous hot-pressing accurate tracking: adaptive fuzzy approach

In order to guarantee slab thickness precision of Medium Density Fibreboard (MDF), an adaptive fuzzy Dynamic Surface Control (DSC) was investigated. Firstly, a mathematical model for hydraulic servo system of the continuous hot-pressing machine with input saturation was proposed, and the auxiliary system was introduced to compensate the effect of input saturation. Due to the uncertain function in the model, a fuzzy logic system was introduced to approximate it. Subsequently, a slab thickness tracking controller with less complexity was developed with the help of the DSC approach. Simultaneously, an on-line adaptive law was applied to estimate the adjustable parameter vector in the fuzzy system. Finally, experimental results validated that the proposed method can ensure the MDF slab thickness precision in the hot-pressing process.

Adaptive Neural Funnel Control for Nonlinear Two-Inertia Servo Mechanisms With Backlash

This paper proposes a modified adaptive neural control algorithm with a funnel function for nonlinear two-inertia servomechanisms with backlash and external disturbance. A continuous tracking differentiator is employed to replace the first-order filter in the traditional dynamic surface control design. A novel funnel function is employed and used in control design to improve the control performance. Then, an adaptive neural dynamic surface controller based on funnel control is presented to guarantee the tracking performance of two-inertia servomechanisms. In addition, the unknown dynamics, including backlash, nonlinear friction, and external disturbance, are approximated by using the echo state neural network and compensated online. The comparative experimental results validate the effectiveness of the developed control algorithm based on a two-inertia servo mechanism.

High-Gain Observer-Based Line-of-Sight Guidance for Adaptive Neural Path Following Control of Underactuated Marine Surface Vessels

This paper presents an adaptive neural network dynamic surface control law with time-varying sideslip compensation, which includes a geometric kinematic control design and a dynamic control design, for path following of underactuated marine surface vessels (MSVs) in the presence of unmeasured states, model uncertainties, unknown ocean environmental disturbances, and input saturation. First, a high gain observer based line-of-sight guidance law, capable of estimating and compensating the time-varying sideslip angle induced by the unknown time-varying ocean disturbances, is proposed to generate the desired heading for the dynamic control system of the MSV. Next, considering the input saturation, adaptive neural network dynamic surface velocity control laws, in which the model uncertainties and unknown ocean environmental disturbances of the system are compensated by adaptive radial basis function neural network and the problem of input saturation is solved by introducing auxiliary dynamic systems, are designed to realize the dynamic control target of the path following. The proposed control strategy of path following compensates the ocean environmental disturbances from the perspective of both the geometric kinematics and the dynamics, and all error signals of a closed-loop control system are proven to be uniformly ultimately bounded. The simulations validate the effectiveness of the proposed control strategy for path following of MSVs.

Adaptive RCMAC Neural Network Dynamic Surface Control for Permanent-Magnet Synchronous Motors Driven Two-Axis X-Y Table

In this paper, a robust adaptive dynamic surface control (RADSC) is proposed with a recurrent cerebellar model articulation controller (RCMAC) based on the function link neural network (FLNN) for two-axis X-Y table derived by two permanent-magnet synchronous motors (PMSMs) servo drives. Initially, an optimal computed torque controller (OCTC) is used to ensure the stability of the two-axis X-Y table system. But there may be destroyed in the control performance due to the presence of parameter uncertainties. This might be due to the fact that linear optimal control possesses inherent robustness within a specified spectrum of model uncertainties. Due to the above reasons, the RADSC is developed according to the system requirements in order to increase the robustness of the control system. In the proposed control scheme, a dynamic surface controller (DSC) and an RCMAC uncertainty observer with a robust controller are combined to constitute the RADSC scheme. In the proposed RADSC, the DSC is utilized to get rid of the complexity of explosion present in the backstepping design. Furthermore, the RCMAC uncertainty observer is developed such that it can approximate the nonlinear parameter uncertainty terms online, whereas the robust controller is designed to recover the residual of the approximation error of the RCMAC. Based on the Lyapunov stability analysis, the online adaptive control laws are derived. The experimental results confirm that the x-axis and y-axis motions are controlled separately, whereas it can be concluded that the proposed RADSC's dynamic behaviors accomplish robust tracking performance though there were parameter uncertainties.

Fuzzy State Observer‐Based Adaptive Dynamic Surface Control of Nonlinear Systems with Time‐Varying Output Constraints

In this paper, a new fuzzy dynamic surface control approach based on a state observer is proposed for uncertain nonlinear systems with time‐varying output constraints and external disturbances. An adaptive fuzzy state observer is used to estimate the states that cannot be measured in the systems. In our method, a time‐varying Barrier Lyapunov Function (BLF) is used to ensure that the output does not violate time‐varying constraints. In addition, dynamic surface control (DSC) technology is applied to overcome the problem of “explosion of complexity” in a backstepping control. Finally, the stability and signal boundedness of the system are confirmed by the Lyapunov method. The simulation results show the effectiveness and correctness of the proposed method.

Automatic Leader-Follower Persistent Formation Control for Autonomous Surface Vehicles

This paper presents a novel leader-follower formation control for autonomous surface vehicles (ASVs). The dynamic model of ASV and the traditional methods of trajectory tracking are analyzed. Previous research about ASVs' formation focuses on the way of realizing trajectory tracking under conditions, such as time-delays, finite-time, and non-holonomic system. However, principles of constructing a suitable ASVs formation are often neglected. We present a novel leader-follower relation-invariable persistent formation (RIPF) control for ASVs, from which a persistent formation can be generated in any position. Obtained by using RIPF control potential function, the outputs of RIPF control are data points, which should be smoothened using broad learning system (BLS). The global leader navigates the mission trajectory, and each follower follows the RIPF trajectory to satisfy the RIPF potential function. The neural network-based adaptive dynamic surface control is introduced to solve the mission trajectory tracking problems. Environmental disturbances exist in ASV model, and BLS also can be used to approximate the disturbances. The simulation results show that the proposed generative method and control law are effective to realize the desired formation.

Neural network based adaptive dynamic surface control of nonaffine nonlinear systems with time delay and input hysteresis nonlinearities

In this paper, neural networks (NNs)-based adaptive dynamic surface control is investigated for a class of pure-feedback nonlinear systems with unknown time delay and input hysteresis nonlinearities. The design difficulty appeared in this paper due to time delay is handled by combining neural networks with finite covering lemma instead of using Krasovskii functionals, and the assumptions on the time-delay functions are removed based the finite covering lemma and NNs. Meanwhile, completely unknown backlash-like hysteresis control input that frequently exists in practice is also considered. By combining the adaptive backstepping recursive design technique with the universal approximation ability of neural networks, an adaptive neural control algorithm is systemically designed for the system under consideration, and the explosion of complexity exists in traditional backstepping design is avoided by using dynamic surface technique. It is shown that the designed adaptive controller can guarantee that all the signals are ultimately uniformly bounded and the desired signal can be tracked with a small domain of the origin. A numerical example and an example of a real plant for adjustable metal cutting system are provided to show the feasibility of the proposed method.

Adaptive neural dynamic surface control of MIMO pure-feedback nonlinear systems with output constraints

In this work, the problem of adaptive neural dynamic surface control (DSC) with the minimum adjustable parameters is discussed for a class of multi-input multi-output (MIMO) pure-feedback nonlinear systems with unmodeled dynamics and output constraints. An auxiliary signal designed by the characteristics of unmodeled dynamics is used to handle the dynamical uncertainties. The unknown continuous black-box functions produced in the controller design process are approximated by using radial basis function neural networks (RBFNNs). Based on an one-to-one nonlinear mapping(NM), the MIMO nonaffine nonlinear system with output constraints is transformed into a novel block-structure MIMO nonaffine nonlinear system without output constraints. Based on the transformed system and modified DSC, robust adaptive neural tracking control scheme is developed. Through theoretical analysis, all the signals in the closed-loop system are shown to be semi-globally uniformly ultimately bounded (SGUUB). A numerical example is provided to demonstrate the effectiveness of the proposed design strategy.

Design of an adaptive neuro-observer-based feedback controller for high-speed trains with parametric uncertainties

An adaptive observer-based controller design for the nonlinear model of a high-speed train is demonstrated in this paper. A high-speed train belongs to the class of multivariable and coupled dynamic system with a higher degree of nonlinearities and uncertainties. The radial basis function neural network is used for the approximation of these nonlinearities and uncertainties. Using this approximation, an adaptive neuro-observer is designed for the estimation of the unavailable states of the high-speed train for measurement. Using one-to-one nonlinear mapping, the high-speed train plant and the adaptive neuro-observer are remodelled in a pure feedback form without any constraints on states. On the basis of the adaptive neuro-observer, an adaptive dynamic surface controller is designed for the high-speed train system. The upper bounds of the actual controller gains of the high-speed train need not be known whereas the lower and upper bounds of the virtual controller gain require prior knowledge. The tuning of the design parameters has been done online in the proposed observer/controller. The closed-loop stability and convergence have been analysed through a formal proof based on the Lyapunov approach. The enhanced performance of the high-speed train with the proposed controller is compared with the backstepping control approach and demonstrated using simulation studies.

Decentralized robust adaptive neural dynamic surface control for multi‐machine excitation systems with static var compensator

SummaryFocusing on solving the control problem of the multimachine excitation systems with static var compensator (SVC), this paper proposes a decentralized neural adaptive dynamic surface control (DNADSC) scheme, where the radial basis function neural networks are used to approximate the unknown nonlinear dynamics of the subsystems and compensate the unknown nonlinear interactions. The main advantages of the proposed DNADSC scheme are summarized as follows: (1) the strong nonlinearities and complexities are mitigated when the SVC equipment are introduced to the multimachine excitation systems and the explosion of complexity problem of the backstepping method is overcome by combining the dynamic surface control method with neural networks (NNs) approximators; 2) the tracking error of the power angle can be kept in the prespecified performance curve by introducing the error transformed function; (3) instead of estimating the weighted vector itself, the norm of the weighted vector of the NNs are estimated, leading to the reduction of the computational burden. It is proved that all the signals in the multimachine excitation system with SVC are semiglobally uniformly ultimately bounded.

Distributed Adaptive Dynamic Surface Control for Synchronization of Uncertain Nonlinear Multi-agent Systems

In this paper a distributed adaptive dynamic surface controller is proposed for multi-agent systems under fixed directed graph topologies. The agents have uncertain nonlinear dynamics and are influenced by bounded unknown disturbances. The controller should synchronize the states of all agents with the corresponding states of the nonautonomous leader. It is proved that, with the proposed controller, the synchronization error remains bounded; and the bounds can be arbitrarily decreased by increasing the controller gains. The control rules are designed such that each agent only requires the state information of its neighbors, rendering a distributed control. The effectiveness of the proposed method is demonstrated through two simulation examples.

Design of adaptive backstepping dynamic surface control method with RBF neural network for uncertain nonlinear system

This paper develops an adaptive backstepping dynamic surface control method with RBF Neural Network for a class of nonlinear system under extra disturbances. The considered RBF Neural Network based on adaptive control is applied to approximate the unknown smooth function arbitrarily. The “explosion of the complexity” is eliminated by utilizing the dynamic surface control technique. The Lyapunov function is employed to verify the globally asymptotically stability of the control nonlinear system. Four examples were given to show that the novel control method can not only tracking the expected trajectory very well but also has a better approximation capability for various complex unknown smooth function under disturbances.

Adaptive fuzzy dynamic surface control for uncertain nonlinear systems in pure-feedback form with input and state constraints using noisy measurements

ABSTRACTThis paper is concerned with the design of an adaptive fuzzy dynamic surface control for uncertain nonlinear pure-feedback systems with input and state constraints using a set of noisy measurements. The design approach is described as follows. The nonlinear uncertainties are approximated by using the fuzzy logic systems at the first stage, secondly the adaptive fuzzy dynamic surface control is introduced to remove the problem of the explosion of complexity for the derivation of the adaptive fuzzy backstepping control, thirdly a new saturation function for state constraints is proposed to design the controllers based on the Lyapunov function, fourthly the number of the adjustable parameters is reduced by using the simplified extended single input rule modules, and finally the weighted least squares estimator to take the estimates for the un-measurable states and the adjustable parameters is in a simplified structure designed. The proposed approach provides effective system performance in the simulation experiment.

Adaptive neural dynamic surface control for full state constrained stochastic nonlinear systems with unmodeled dynamics

This paper solves the problem of adaptive neural dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled dynamics. The concept of the state constraints in probability is first proposed and applied to the stability analysis of the system. The full state constrained stochastic nonlinear system is transformed to the system without state constraints through a nonlinear mapping. The unmodeled dynamics is dealt with by introducing a dynamic signal and the adaptive neural dynamic surface control method is explored for the transformed system. It is proved that all signals of the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded(SGUUB) in mean square or the sense of four-moment. At the same time, the full state constraints are not violated in probability. The validity of the proposed control scheme is demonstrated through the simulation examples.

Distributed adaptive dynamic surface formation control for uncertain multiple quadrotor systems with interval type-2 fuzzy neural networks

This paper presents a distributed adaptive formation control method for uncertain multiple quadrotor systems under a directed graph that characterizes the interaction among the leader and followers. The proposed approach is based on an adaptive dynamic surface control, consensus algorithm and graph theory, where the system uncertainties are approximately modelled by interval type-2 fuzzy neural networks. The adaptive laws of interval type-2 fuzzy neural network parameters are derived from the stability analysis. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and the leader-follower formation goal can be asymptotically achieved. The developed control scheme is applied to the followers of quadrotor systems for performance evaluations. Simulation results are also provided to compare with the existing methods and reveal the superiority of the proposed adaptive formation controller.

Adaptive fuzzy dynamic surface control for a class of stochastic MIMO discrete-time nonlinear pure-feedback systems with full state constraints

ABSTRACTThis paper presents the design of an adaptive fuzzy dynamic surface control for a class of stochastic MIMO discrete-time nonlinear pure-feedback systems with full state constraints using a set of noisy measurements. The design approach is described as follows. The nonlinear uncertainty is approximated by using the fuzzy logic system at the first stage, secondly the proposed adaptive fuzzy dynamic surface control is designed based on a new saturation function for full state constraints, thirdly the number of the adjustable parameters is reduced by using the simplified extended single input rule modules, and finally the simplified weighted least squares estimator is in a simplified structure designed to take the estimates for the un-measurable states and the adjustable parameters. The simulation provides that the proposed approach is effective for the improvement of the system performance.

Adaptive NN Dynamic Surface Controller Design for Nonlinear Pure-Feedback Switched Systems With Time-Delays and Quantized Input

This paper deals with the problem of robust stabilization for a class of uncertain nonlinear pure-feedback switched time-delay systems with quantized input signal. In order to overcome the design difficulty caused by the quantization and unpredictable switchings, a nonlinear decomposition strategy for quantizer is employed in advance. In the controller design procedure, the unknown time-delay terms are compensated by utilizing Lyapunov–Krasovskii functionals, the desired virtual stabilizing functions and desired actual control input are approximated by applying radial basis function neural networks, and dynamic surface control technology are used to handle the issue of “explosion of complexity” in the traditional backstepping procedure. Furthermore, it is shown that the resulting closed-loop system are stable in the sense of semi-global uniformly ultimately bounded. Finally, to verify the effectiveness and applicability of the presented control scheme, an example is given to construct an adaptive neural controller for an electromechanical system.