Robust Adaptive Output Feedback Control Research Articles

Dual cerebella model neural networks based robust adaptive output feedback control for electromechanical actuator with anti‐parameter perturbation and anti‐disturbance

SummaryTo realize a high‐accuracy tracking control of an electromechanical actuator in which only position signal is available, a new robust adaptive output feedback control strategy based on dual CMAC neural networks is proposed in this article. A high‐gain observer and a neural network are combined to estimate the unmeasured system states, in which a neural network is designed to estimate and compensate the system parameter estimation error and other uncertain nonlinearity to improve the performance of the traditional observer. A robust adaptive controller and another neural network are combined to decrease the impact of parameter perturbation and external disturbance and strengthen the system robustness. Lyapunov theorem is used to derive an on‐line weight adaption law for the two neural networks and an on‐line parameter adaptation law for the uncertain parameters to stabilize the system. Thus the parameter uncertainty and disturbance can be compensated with feedforward compensation technique. A nonlinear robust feedback term is designed to suppress the model compensation deviation. Considering the stronger approximation ability of CMAC basis function against RBF basis function, an improved CMAC neural network is introduced in the proposed controller. A large number of simulations and experiments show that the proposed controller has better tracking performance than many main‐stream output feedback controllers in present.

Decentralized robust adaptive backstepping control for a class of non-minimum phase nonlinear interconnected systems

In this paper, a class of interconnected systems is considered, where the nominal isolated subsystems are fully nonlinear and non-minimum phase. A decentralized Extended Kalman Filter-Extended High Gain Observer (EKF-EHGO) is designed to observe the system states. Then, a systematic backstepping design procedure is employed to develop a novel decentralized robust adaptive output feedback control, in which the adaptive law is designed to counter the effects of the interconnections and uncertainties. The proposed decentralized dynamic output feedback control scheme can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Both interconnections and uncertainties are allowed to be unmatched and bounded by an unknown high-order polynomial, which is a more general form when compared with existing work. Two MATLAB simulation examples are used to demonstrate the effectiveness of the proposed method including a system comprising translational oscillator with rotating actuator (TORA) sub-systems.

Reinforcement learning-based saturated adaptive robust output-feedback funnel control of surface vessels in different weather conditions

This article tackles the problem of saturated adaptive robust actor–critic learning PID tracking control design guaranteeing a prescribed funnel performance for ships with multiple dead bands in which the ship’s velocities/accelerations and dynamic parameters are unknown and time-variant subject to the availability of external disturbances. The controller is proposed by integrating an observer, adaptive actor–critic multi-layer neural networks, generalized saturation functions, the prescribed performance funnel control, an adaptive robust controller and multiple dead bands. The observer calculates the estimation of ship’s velocities/accelerations, in which the peaking phenomenon is prevented by limiting the observer signals. Within the structure of adaptive actor–critic design, adaptive multi-layer neural networks are exerted, which counteracts against ship’s time-variant uncertainties and the actuator saturation nonlinearity. In order to reduce the likelihood of actuator saturation well, generalized saturation functions are taken into account. To realize prespecified transient and steady-state performance of the tracking errors, the prescribed performance control is applied. With the purpose of avoiding the corner cutting to increase the trajectory tracking accuracy, curvature of the circular trajectories is calculated and the angle of arc is compensated. An adaptive robust controller is utilized to resist against external disturbances, and a scheme of multiple dead bands is devised in this paper to protect the actuator system in heavy seas.

Modeling and Adaptive Boundary Robust Control of Active Heave Compensation Systems

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, where hydraulic heave compensators implement actuators with input nonlinearity, model coupling, and unknown nonlinear disturbances. A robust adaptive output feedback control scheme based on the backstepping control method is designed to eliminate deep-ocean robot vibration, where the adaptive law handles the system parameter uncertainty. Meanwhile, a nonlinear disturbance observer (NDO) is introduced to overcome the effects of random disturbances and model coupling. In addition, the stability of the whole system is proved according to Lyapunov’s theory, and the scheme is shown to be feasible by theoretical analysis. Finally, a comparative simulation study was conducted to validate the effectiveness of the proposed controller.

Reduced-Order Filters-Based Adaptive Backstepping Control for Perturbed Nonlinear Systems.

In this article, a robust adaptive output-feedback control approach is presented for a class of nonlinear output-feedback systems with parameter uncertainties and time-varying bounded disturbances. A reduced-order filter driven by control input is proposed to reconstruct unmeasured states. The state estimation error is shown to be bounded by dynamic signals driven by system output. The bound estimation technique is employed to estimate the unknown disturbance bound. Based on the backstepping design with three sets of tuning functions, an adaptive output-feedback control scheme with the flat-zone modification is proposed. It is shown that all the signals in the resulting closed-loop adaptive control systems are bounded, and the output tracking error converges to a prespecified small neighborhood of the origin. Two simulation examples are provided to illustrate the effectiveness and validity of the proposed approach.

Neural network state observer-based robust adaptive iterative learning output feedback control for the rigid-flexible coupled robotic systems with unknown delays and backlash-like hysteresis

Neural network state observer-based robust adaptive iterative learning output feedback control for the rigid-flexible coupled robotic systems with unknown delays and backlash-like hysteresis

Adaptive robust output feedback control for attitude tracking of quadrotor unmanned aerial vehicles

SummaryThis work proposes a new adaptive robust output feedback control method for attitude reference tracking of a quadrotor unmanned aerial vehicle without using the angular velocity measurements. By using the K‐filters well known in the adaptive control community, the necessity of velocity measurements or estimating is avoided. The attitude system model is transformed into a second‐order model where the angular velocity measurements are not involved. However, the model includes mismatched uncertainties which should be estimated and compensated by the disturbance observers (DOBs). The controller is designed in a backstepping manner, and the dynamic surface technique is adopted to avoid the explosion of the controller complexity. For each Euler angle axis, the prescribed performance control technique is adopted to ensure a prescribed performance, the lumped disturbance is compensated by a DOB, and furthermore an adaptive law is introduced to adaptively update the corresponding uncertain inertia parameter which affects the control performance significantly. The control performance of the overall control system is analyzed rigorously from the viewpoint of input‐to‐state practical stability. In addition, it is shown how the adaptive laws contribute to improving the control performance. And simulation examples are provided to demonstrate the performance of the proposed method.

Adaptive robust output-feedback boundary control of an unstable parabolic PDE subjected to unknown input disturbance

In this paper, an adaptive robust boundary controller is designed for an unstable heat equation with external disturbance flowing into the control end. The disturbance and uncertainty effect is cancelled out in the closed-loop system by using on-line approximation of disturbance upper-bound provided as an output of adaptive robust update law. The asymptotic stability of the controlled system in the presence of unknown disturbance and/or parametric uncertainties is proved utilising the Lyapunov theorem. Unlike previous researches, the control implementation does not require prior knowledge of unknown disturbance. Moreover, the controller does not require measuring the system states or estimating the system states by an observer. The adaptive robust controller can be regarded as a combination of the best qualities of the adaptive controller and the robust controller with no prior knowledge of system uncertainty, and asymptotic tracking error performance. Numerical simulations and comparisons are provided to illustrate the effectiveness of the proposed method.

Fuzzy adaptive output feedback control for uncertain nonlinear systems with unknown control gain functions and unmodeled dynamics

In this work, the fuzzy adaptive robust output feedback control issues are investigated for single-input single-output (SISO) strict-feedback nonlinear systems. The controlled systems under consideration of this paper contain the unknown control gain functions, unmodeled dynamics and immeasurable states. The unknown nonlinear functions are be approximated by utilizing the fuzzy logic systems, and the immeasurable states are estimated by constructing a new fuzzy state observer. In order to handle the unknown control gain functions and the unmodeled dynamics problems, the Logarithm Lyapunov functions are constructed, under which a new fuzzy adaptive robust output feedback control scheme is proposed by using the adaptive backstepping control design technique and a dynamical signal function. The designed fuzzy adaptive control algorithm can ensure that the closed-loop system is semi-globally uniformly ultimately boundedness (SGUUB) and has the robustness to the unmodeled dynamics. Finally, a simulation example is considered to illustrate the availability of the designed controller.

Uniform robust exact differentiator-based output feedback adaptive robust control for DC motor drive systems

This paper studies a high-accuracy motion control method named output feedback adaptive robust control for a dc motor with uncertain nonlinearities and parametric uncertainties, which always exist in physical servo systems and deteriorate their tracking performance. As only position signal is measurable, a uniform robust exact differentiator (URED) for the unmeasurable states and adaptive control for the parametric uncertainties are integrated in the model compensation term; and the robust control is applied to handle uncertain nonlinearities and stabilize the system. Then, the stability of the closed-loop system is proved theoretically. Finally, simulation and experimental results are studied for a dc motor system to prove the control performance of the proposed control method.

Adaptive Super-Twisting Sliding Mode Control for Mobile Robots Based on High-Gain Observers

In this paper, a robust adaptive output feedback control strategy based on a sliding mode super-twisting algorithm is designed for the trajectory tracking control of a wheeled mobile robot. First, a robust adaptive law is designed to eliminate the influence of parameter uncertainties. Second, a double-power sliding mode surface is designed to improve the response speed of the robot system. Finally, a high-gain observer is employed to estimate the speed information. The stability of the closed-loop system is proved using the Lyapunov stability theorem. The effectiveness of the proposed control strategy is verified by simulation.

Robust Adaptive Contact Force Control of Pantograph–Catenary System: An Accelerated Output Feedback Approach

The pantograph-catenary system plays the crucial role in high-speed electric trains for electrical power collection, where continuity and smoothness of the contact between the pantograph and the catenary is one of the key factors ensuring high-quality power collection essential for safe and reliable train operation. In this article, a nonlinear and highly coupled model for the pantograph-catenary system is established, and an accelerated robust adaptive output feedback control method based on a high-gain observer is proposed to solve the contact force tracking control problem of the pantograph-catenary system using output information only. It is shown that, with the proposed method, the tracking error of the contact force is ensured to be ultimately uniformly bounded with an accelerated preassignable decay rate, resulting in smooth contact force and good-quality current collection. Both theoretical analysis and numerical simulation confirm the effectiveness and benefits of the method.

Robust Adaptive Output Feedback Control for a Class of Underactuated Aerial Vehicles with Input and Output Constraints

This paper addresses the problem of nonlinear robust adaptive output feedback controller for a class of underactuated aerial vehicles with input and output constraints. To solve the problem, the modular design strategy is proposed for the control design. By using the neural networks (NNs) to approximate system uncertainties and observers to reconstruct system states, robust adaptive output feedback controllers are developed. By using a combination of saturation functions and barrier functions, input and output constraints are simultaneously dealt with. The design methodology shows that a cascaded system of an input-to-state stable (ISS) subsystem driven by an ultimate bounded (UB) subsystem enjoys ultimate boundedness property. In addition, the tracking error converges to adjustable neighbourhoods of the origin.

Adaptive Robust Simultaneous Stabilization of Two Dynamic Positioning Vessels Based on a Port-Controlled Hamiltonian (PCH) Model

In this paper, the adaptive robust simultaneous stabilization problem of two ships is studied. Firstly, the water surface three-degree-of-freedom ship models are transformed into port-controlled Hamiltonian (PCH) models. Using a single output feedback controller, the two PCH systems are combined to generate an enhanced PCH system based on Hamiltonian structural attributes. Then, considering the situation with both external interference and structural parameter perturbation in the systems, an adaptive robust output feedback controller is designed to stabilize the two systems simultaneously. Finally, the effectiveness of the controller proposed in this paper is illustrated by a simulation example.

Fuzzy Adaptive Robust Control for Stochastic Switched Nonlinear Systems With Full-State-Dependent Nonlinearities

This paper solves the problem of fuzzy adaptive robust output feedback control using average dwell-time switching for a class of stochastic switched nonstrict feedback nonlinear systems with more general uncertainties, including unmodeled dynamics, unknown control coefficients, unknown drift/diffusion terms, and unknown output functions. The unmodeled dynamics and drift/diffusion terms are allowed to depend on the whole unmeasurable states, whose effect is difficult to counteract. By virtue of the fuzzy approximation capability and variable partition technique, an adaptive backstepping procedure is proposed to compensate these uncertainties. Based on the constructed switched observer, which shows its strong robustness to the unknown control coefficients and output functions, an output feedback controller is designed to render all the signals of the closed-loop system bounded in probability under an appropriately chosen average dwell time.

Hybrid Scheduling and Quantized Output Feedback Control for Networked Control Systems

A novel co-design scheme of hybrid scheduling strategy, adaptive logarithmic quantizer and dynamic robust H-infinity output feedback controller for a class of networked control system (NCS)with communication constraints and time delay is proposed. The hybrid scheduling scheme integrates dead zone scheduling and Try Once Discard (TOD) scheduling so as to get the stronger adaptability and flexibility than the single scheduling. In this scheme, dead zone scheduling which updates the threshold according to mode-dependent control strategy is used for single node of NCS to reduce the network bandwidth utilization while TOD scheduling is used for the whole node of NCS in order to meet the requirements of communication constraints and guarantee the overall system performance.We develop the integrated design for the hybrid scheduling strategy, adaptive quantizer and dynamic robust output feedback controller to maintain asymptotic stability of the closed-loop NCS by using the multiple-Lyapunov function and switched system theory. The proposed method can improve the the quality of service (QoS) meanwhile ensure the quality of control (QoC) of overall systems, which make a better trade-off between network utilization and control performance. An simulation example demonstrates the efficiency of the proposed method.

Robust adaptive output feedback control to a class of non-triangular stochastic nonlinear systems

In this paper, the robust adaptive control design problem is studied for a class of non-triangular nonlinear systems with unmodeled dynamics and stochastic disturbances. It is assumed that the states of the systems to be controlled are unmeasurable, and thus an adaptive state observer is first developed. By utilizing the stochastic small-gain theorem and the backstepping recursive design procedure, a robust adaptive output feedback control scheme is then proposed. It is shown that all the signals in the resulting closed-loop system are bounded in probability, and the system output converges to a small residual set of the equilibrium in probability.

Output feedback adaptive robust control of hydraulic actuator with friction and model uncertainty compensation

This paper studies output feedback control of hydraulic actuators with modified continuous LuGre model based friction compensation and model uncertainty compensation. An output feedback adaptive robust controller is constructed which combines the adaptive control part and the robust control part seamlessly. The adaptive part is constructed to handle the parametric uncertainties existed in the model. The residuals coming from parameter adaption and the unmodeled dynamics are taken into consideration by the robust part. Since only the position signal is available, the velocity, pressure, and internal friction states are obtained by observation or estimation. The errors coming from observation and estimation are also dealt with the robust part. Furthermore, the convergence of the closed-loop controller–observer scheme is achieved by the Lyapunov method in the presence of parametric uncertainties only. Extensive comparative experiments performed on a hydraulic actuator demonstrate the effectiveness of the proposed controller–observer scheme.

Robust adaptive output feedback control for uncertain nonlinear systems with quantized input

SummaryIn this paper, we study the input quantization problem for a class of uncertain nonlinear systems. The quantizer adopted belongs to a class of sector‐bounded quantizers, which basically include all the currently available static quantizers. Different from the existing results, the quantized input signal, rather than the input signal itself, is used to design the state observers, which guarantees that the state estimation errors will eventually converge to zero. Because the resulting system may be discontinuous and non‐smooth, the existence of the solution in the classical sense is not guaranteed. To cope with this problem, we utilize the non‐smooth analysis techniques and consider the Filippov solutions. A robust way based on the sector bound property of the quantizers is used to handle the quantization errors such that certain restrictive conditions in the existing results are removed and the problem of output feedback control with input signal quantized by logarithmic (or hysteresis) quantizers is solved for the first time. The designed controller guarantees that all the closed‐loop signals are globally bounded and the tracking error exponentially converges towards a small region around zero, which is adjustable. Copyright © 2016 John Wiley & Sons, Ltd.

Implementable Adaptive Inverse Control of Hysteretic Systems via Output Feedback With Application to Piezoelectric Positioning Stages

This paper aims to develop a neural-network-based robust adaptive output-feedback motion controller for a class of hysteretic nonlinear systems, where the inverse hysteresis approach is adopted. The developed scheme is applied to the piezoelectric positioning stage, showing the advantages of the proposed approach by the experimental results. The main contributions are: 1) by using the neural networks as an approximator, the nonlinear function in the systems can be completely unknown; 2) by designing a high-gain observer to estimate the states of the system and cope with the uncertainties of the system, only the output of the control system is required measurable; 3) experiments on the piezoelectric positioning stage were conducted where the piezoelectric positioning stage is considered as a third-order system under the condition only the output of the system is available; 4) by adjusting the initial conditions of the states observer and adaptive laws of unknown parameters, the arbitrarily small ${L} _{\infty }$ norm of the tracking error is deviated. It is proved that all the signals in the closed-loop systems are semiglobally ultimately uniformly bounded.