Unknown Actuator Nonlinearities Research Articles

Adaptive Distributed Synchronization of Multiple Vehicles With Actuator Nonlinearities: Design and Experimentation

This article aims to investigate a distributed synchronization problem of multiple vehicles with unknown actuator nonlinearities under a directed communication network. In the existing works of multiple agent systems (MAS) that are feasible for modeling the synchronization of multiple vehicles, actuators are usually assumed to perform perfectly in a linear condition, which, however, contradicts practical dynamical systems. To attack such an assumption, we propose an adaptive fully distributed synchronization protocol that is capable of handling unknown actuator nonlinearities in multiple vehicles. Two distributed adaptive laws are designed for each follower vehicle, wherein one adaptive law is for tuning unknown scaling coefficients from actuator nonlinearities and also for unknown graph gains from the directed communication network, and the other adaptive law is for tackling unmodeled and bounded disturbances. It is proved that the synchronization error in the vehicle synchronization is asymptotically stable under the proposed protocol regardless of unknown nonlinearities on actuators. Both simulation and experimental studies are carried out to verify the effectiveness of the proposed control protocol.

Adaptive sliding tracking control for nonlinear uncertain robotic systems with unknown actuator nonlinearities

AbstractThis study is concerned with the tracking control problem for nonlinear uncertain robotic systems in the presence of unknown actuator nonlinearities. A novel adaptive sliding controller is designed based on a robust disturbance observer without any prior knowledge of actuator nonlinearities and system dynamics. The proposed control strategy can guarantee that the tracking error eventually converges to an arbitrarily small neighborhood of zero. Simulation results are included to demonstrate the effectiveness and superiority of the proposed strategy.

Neural adaptive integral sliding mode control for attitude tracking of flexible spacecraft with signal quantisation and actuator nonlinearity

This study investigates the neural adaptive integral sliding mode control for attitude tracking of flexible spacecraft, where unknown actuator nonlinearity, input quantisation and external disturbances are considered simultaneously. In this design, the hysteresis encoder–decoder scheme is employed between the controller and actuator side for signal quantisation. A quantised adaptive integral sliding mode control strategy is developed, where the neural network scheme is applied to approximate the unmeasurable rigid-flexible coupled nonlinear dynamics. The proposed control strategy can compensate for quantisation error, actuator faults, actuator dead-zone as well as external disturbances effectively, and guarantee the trajectory of the attitude tracking error converge to the equilibrium point along the designed sliding surface. Finally, a simulation example is conducted to demonstrate the validness of the developed quantised control strategy for flexible spacecraft attitude tracking problem.

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities

This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on Caputo’s definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computer-simulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller.

Robust adaptive FTC allocation for over‐actuated systems with uncertainties and unknown actuator non‐linearity

In this study, a robust adaptive fault-tolerant control (FTC) allocation method is proposed to address the FTC problem of over-actuated systems in the presence of matched disturbance, unmodelled dynamics and unknown actuator non-linearity simultaneously. The main idea is to design the virtual control law via robust adaptive control and then distribute the virtual control among individual actuators by weighted pseudo-inverse control allocation whether the actuators are faulty or not. The virtual control consists of three terms: a linear term is designed through adaptive control to maintain stability, a non-linear term constructed by radial basis function neural network (RBFNN) is used to approximate the unmodelled dynamics, and a robust term is added to eliminate the approximation error introduced by RBFNN as well as matched disturbance and unknown actuator non-linearity. With the aid of the Lyapunov stability theorem, the convergence of the closed-loop system is proven. The simulation results demonstrate the effectiveness, fault-tolerant capability and robustness of the proposed method.

Chaos Suppression and Practical Stabilization of Uncertain Generalized Duffing-Holmes Control Systems with Unknown Actuator Nonlinearity

Chaos Suppression and Practical Stabilization of Uncertain Generalized Duffing-Holmes Control Systems with Unknown Actuator Nonlinearity

A new online delay estimation-based robust adaptive stabilizer for multi-input neutral systems with unknown actuator nonlinearities

This paper studies the problem of actuator-nonlinearities compensation in the multi-input uncertain neutral systems. The neutral systems with different unknown time-varying delays, unmodeled dynamics, nonlinear perturbations and disturbances are considered. A new methodology based on the online estimation of the delays to compensate for the effects generated by dead-zone and saturation, acting in series on a system's input are presented. The online estimations of the unknown delays are accomplished by adaptive laws that guarantee the exponential stabilities of the estimated delays. In the presence of state time delay, derivative state time delay, unmodeled dynamics, nonlinear perturbations, disturbances and both input dead-zone and saturation, the asymptotic stability of the closed-loop system is ensured and the state response converge to the origin. Finally, the practicability and the efficacy of the proposed approach is demonstrated via a numerical example.

Adaptive tracking control for stochastic mechanical systems with actuator nonlinearities

In this paper, output tracking control problem is investigated for a class of uncertain nonlinear random mechanical systems subjected to unknown actuator nonlinearities. Two common kinds of actuator nonlinearities, namely, Bouc–Wen hysteresis and dead-zone, are considered in a unified framework, such that the designed adaptive control law is robust to the mentioned actuator nonlinearities (hysteresis and dead-zone). Different from some existing controller design approaches for nonlinear systems with actuator nonlinearities, the usual priori knowledge on the known compact set for system uncertain parameters has been eliminated with the proposed control method. The proposed control law can ensure that the system output tracking error eventually converges to an arbitrarily small neighborhood of zero in the sense of mean square by turning controller gains. Simulation studies are performed to demonstrate the effectiveness of the proposed controller design approach.

Neural adaptive quantized output-feedback control-based synchronization of uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities

This research is concerned with the problem of generalized function projective synchronization of nonlinear uncertain time-delay incommensurate fractional-order chaotic systems with input nonlinearities. The considered problem is challenging owing to the presence of unmeasured master-slave system states, external dynamical disturbances, unknown nonlinear system functions, unknown time-varying delays, quantized outputs, unknown control directions unknown actuator nonlinearities (backlash-like hysteresis, dead-zone and asymmetric saturation actuators) and distinct fractional-orders. Under some mild assumptions and using Caputo's definitions for fractional-order integrals and derivatives, the design procedure of the proposed neural adaptive controller consists of a number of steps to solve the generalized function projective synchronization problem. First, smooth functions and the mean value theorem are utilized to overcome the difficulties from actuator nonlinearities and distributed time-varying delays, respectively. Then, a simple linear observer is established to estimate the unknown synchronization error variables. In addition, a Nussbaum function is incorporated to cope with the unknown control direction and a neural network is adopted to tackle the unknown nonlinear functions. The combination of the frequency distributed model, the Razumikhin Lemma, the neural network parameterization, the Lyapunov method and the Barbalat's lemma is employed to perform the stability proof of the closed-loop system and to derive the adaption laws. The major advantages of this research are that: (1) the Strictly Positive Real (SPR) condition on the estimation error dynamics is not required, (2) the considered class of master-slave systems is relatively large, (3) all signals in the resulting closed-loop systems are semi-globally uniformly ultimately bounded and the synchronization errors semi-globally converge to zero. Finally, numerical examples are presented to illustrate the performance of the proposed synchronization scheme.

Adaptive robust image-based visual servoing control of robot with unknown actuator hysteresis

This paper investigates the image-based visual servoing (IBVS) manipulator system with unknown hysteresis nonlinearity by utilizing the uncalibrated eye-to-hand configuration. Compared with the conventional IBVS system, the unknown actuator hysteresis nonlinearity, which widely exists in the real physical mechanical systems and significantly limits the performances of the robotic system, is fully considered. The problem of unknown actuator nonlinearity prevents direct input of existing controllers and will result in some challenging difficulties in control design. In this paper, an adaptive IBVS control scheme, which takes the unknown actuator hysteresis into account, is presented. Without the prior knowledge of both the intervals of hysteresis parameters and the bound of the dynamic disturbance term, a novel adaptive algorithm is developed to estimate the unknown parameters on-line. The image tracking errors are guaranteed to converge to a small neighborhood of the origin. Simulations and comparative experiments are carried out to test the performance of the proposed scheme.

Adaptive control of MIMO mechanical systems with unknown actuator nonlinearities based on the Nussbaum gain approach

This paper investigates MIMO mechanical systems with unknown actuator nonlinearities. A novel Nussbaum analysis tool for MIMO systems is established such that unknown time-varying control coefficients are tackled. In contrast to existing literatures on continuous-time systems, the newly-developed Nussbaum tool focuses on extending the traditional Nussbaum result from one dimensional case to the multiple one. Specifically, not only the multiple unknown input coefficients are extended to the time-varying, but also the limitation of the prior knowledge of coefficients� upper and lower bounds is removed. Furthermore, an adaptive robust controller associated with the proposed tool is presented. The asymptotic tracking of MIMO mechanical systems is guaranteed with the help of the Lyapunov Theorem. Finally, a simulation example is provided to examine the validity of the proposed scheme.

Adaptive control of robotic systems with unknown actuator nonlinearities and control directions

This paper presents an adaptive control approach to bridge the gap between unknown time-varying actuator nonlinearities and identical control directions in the area of robotic systems. Technically, a novel Nussbaum gain and its properties are first proposed to pave the way for the formulation of a more advanced tool. Subsequently, a newly developed theorem is summarized and integrated into the stability analysis such that all control units automatically and simultaneously estimate the unknown actuator nonlinearities and directions. Finally, it is rigorously proved that robotic systems asymptotically converge to the desired trajectories.

Adaptive fuzzy observer-based stabilization of a class of uncertain time-delayed chaotic systems with actuator nonlinearities

An observer-based output feedback adaptive fuzzy controller is proposed to stabilize a class of uncertain chaotic systems with unknown time-varying time delays, unknown actuator nonlinearities and unknown external disturbances. The actuator nonlinearity can be backlash-like hysteresis or dead-zone. Based on universal approximation property of fuzzy systems the unknown nonlinear functions are approximated by fuzzy systems, where the consequent parts of fuzzy rules are tuned with adaptive schemes. The proposed method does not need the availability of the states and an observer based output feedback approach is proposed to estimate the states. To have more robustness and at the same time to alleviate chattering an adaptive discontinuous structure is suggested. Semi-global asymptotic stability of the overall system is ensured by proposing a suitable Lyapunov–Krasovskii functional candidate. The approach is applied to stabilize the time-delayed Lorenz chaotic system with uncertain dynamics amid significant disturbances. Analysis of simulations reveals the effectiveness of the proposed method in terms of coping well with the modeling uncertainties, nonlinearities in actuators, unknown time-varying time-delays and unknown external disturbances while maintaining asymptotic convergence.

Adaptive Sliding-Mode Antisway Control of Uncertain Overhead Cranes With High-Speed Hoisting Motion

This paper proposes an adaptive sliding-mode antisway control law for uncertain overhead cranes with high-speed hoisting motion. Since the sway dynamics are disturbed by the trolley acceleration and hoisting velocity, antisway control law is designed based on the sliding mode control by defining a sliding surface in such a way that the trolley acceleration contributes to the sway dynamics as nonlinear damping in sliding mode. In addition, this nonlinear damping is designed such that it dominates the inherent damping coupled with the hoisting velocity so that the asymptotic stability of the sway dynamics can be achieved. To cope with system uncertainties such as system parameter variations, unknown actuator nonlinearities, unmodeled dynamics, and external disturbances, we design a fuzzy uncertainty observer and incorporate it into the sliding-mode antisway control law. Via the stability analysis and computer simulations, we show that the proposed control law guarantees the robust antisway performance of overhead cranes, regardless of hoisting velocity, even in the presence of system uncertainties.

Asymptotic Tracking Control of Uncertain Nonlinear Systems With Unknown Actuator Nonlinearity

This technical note is concerned with the problem of adaptive tracking control for a class of nonlinear systems with parametric uncertainty, unknown actuator nonlinearity and bounded external disturbance. Two type of actuator nonlinearities, that is, symmetric dead-zone and Bouc-Wen hysteresis, are considered, respectively. First, an adaptive control scheme with positive integrable time-varying function is presented to compensate for the dead-zone nonlinearity. Then, the actuator nonlinearity under consideration is modeled as Bouc-Wen hysteresis, and desired compensation controller is designed based on the backstepping technique and Nussbaum gain approach. In both of the two schemes, the asymptotic tracking is guaranteed with the tracking error converging to zero. Finally, an illustrative example is provided to show the effectiveness of the proposed design methods.

Asymptotic tracking control of uncertain nonlinear systems with unknown actuator nonlinearity and unknown gain signs

Two adaptive control schemes for a class of uncertain nonlinear systems preceded by actuator nonlinearity are presented. We consider two possible input nonlinearities, that is, backlash-like hysteresis and symmetric dead-zone, which can be simultaneously dealt with by using the proposed compensation controllers. The knowledge on the signs of the unknown plant parameters and the parameters in the input nonlinearity models is not required. Only the boundedness of the reference signal is assumed. The Nussbaum gain approach is employed to remove the assumption on the control gain sign. The first scheme is based on the backstepping technique. The final step of the recursive design is crucial for the closed-loop stability. In the second scheme, a direct adaptive control law is designed. The controller has a simple structure and only one adaptation parameter is updated. In both controllers, it is shown that asymptotic tracking is ensured. Two numerical examples are provided to show the effectiveness of the theoretical results.

Zero-error tracking control of uncertain nonlinear systems in the presence of actuator hysteresis

In this paper, the problem of adaptive tracking control is addressed for a class of nonlinear systems with unknown constant parameters and unknown actuator nonlinearity. The actuator nonlinearity is modelled as the backlash-like hysteresis, which is described by a differential model. The prior knowledge on the control gain sign is not required, and only the assumption on the reference signal is made. By combining the adaptive backstepping technique with the Nussbaum gain approach, an adaptive compensation controller design approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking error can converge to zero asymptotically despite the presence of the actuator hysteresis. Two simulation examples are included to illustrate the effectiveness of the proposed approach.

Exponential Tracking Control of Nonlinear Systems with Actuator Nonlinearity

In this paper, the problem of adaptive tracking control is addressed for a class of nonlinear systems with parametric uncertainty, unknown actuator nonlinearity and disturbance. Two type of actuator nonlinearities, that is, backlash-like hysteresis and symmetric dead-zone, are considered simultaneously. Without constructing the inverse function of actuator nonlinearity, unified control framework is established. An adaptive control scheme, capable of guaranteeing the exponential tracking with zero tracking error, is proposed. Two simulation examples are provided to clarify and verify the proposed approach.

Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach

This paper deals with the adaptive sliding-mode control problem for nonlinear active suspension systems via the Takagi-Sugeno (T-S) fuzzy approach. The varying sprung and unsprung masses, the unknown actuator nonlinearity, and the suspension performances are taken into account simultaneously, and the corresponding mathematical model is established. The T-S fuzzy system is used to describe the original nonlinear system for the control-design aim via the sector nonlinearity approach. A sufficient condition is proposed for the asymptotical stability of the designing sliding motion. An adaptive sliding-mode controller is designed to guarantee the reachability of the specified switching surface. The condition can be converted to the convex optimization problems. Simulation results for a half-vehicle active suspension model are provided to demonstrate the effectiveness of the proposed control schemes.

Robust adaptive control for uncertain nonlinear systems with unknown control directions and actuator nonlinearity

A robust adaptive control scheme is proposed for a class of uncertain nonlinear systems in strict feedback form with both unknown control directions and non-symmetric dead-zone nonlinearity based on backstepping design. The conditions that the dead-zone slopes and the boundaries are equal and symmetric are removed by simplifying nonlinear dead-zone input model, the assumption that the priori knowledge of the control directions to be known is eliminated by utilizing Nussbaum-type gain technique and neural networks (NN) approximation capability. The possible controller singularity problem and the effect of dead-zone input nonlinearity are avoided perfectly by combining integral Lyapunov design with sliding mode control strategy. All the signals in the closed-loop system are guaranteed to be semi-globally uniformly ultimately bounded and the tracking error of the system is proven to be converged to a small neighborhood of the origin. Simulation results demonstrate the effectiveness of the proposed control scheme.