Cross-coupling integral adaptive robust posture control of a pneumatic parallel platform

An integrated direct/indirect adaptive robust controller (DIARC) is proposed to further improve the achievable posture trajectory tracking control performance of a parallel manipulator driven by pneumatic muscles. Due to the model errors of the static forces and friction forces of pneumatic muscles, the simplified average flow rate characteristics of valves, and the unknown disturbances of entire system, there exist large extent of parametric uncertainties and rather severe uncertain nonlinearities in the modeling of the parallel manipulator. To address these problems, in this paper, an indirect type parameter estimation is used to obtain reliable estimates of effective model parameters for reducing the parametric uncertainties while an integrated direct/indirect ARC with dynamic compensation type fast adaptation is utilized to further attenuate the influences of uncertain nonlinearities for better tracking performance. Considering that the conventional parameter estimation algorithm based on single error minimizing criterion normally fails to provide reliable parameter estimation for the parallel manipulator with symmetric structure due to the difficulty in satisfying the persistent exciting conditions all the time-the theoretical requirement for the convergence of online parameter estimation, additional practical constraints are imposed to further condition the parameter estimation process and a new parameter estimation algorithm based on composite error minimizing criterion in task-space is developed. Experimental results demonstrate that the parallel manipulator under the control of the proposed integrated DIARC has strong self-adaptability and robustness with the steady-state posture tracking error being less than 0.01deg, average tracking error less than 0.1deg, and maximum tracking error less than 0.3deg, which are significantly better than those of the direct ARC.

This paper focuses on an adaptive robust dynamic surface control (ARDSC) with composite adaptation laws (CAL) for a class of uncertain nonlinear systems in semi‐strict feedback form. A simple and effective controller has been obtained by introducing dynamic surface control (DSC) technique and designing novel adaptation laws. First, the ‘explosion of terms’ problem caused by backstepping method in the traditional adaptive robust control (ARC) is avoided. Meanwhile, through a new proof philosophy the asymptotical output tracking that the ARC possesses is theoretically preserved. Second, when persistent excitation (PE) condition satisfies, true parameter estimates could be acquired via designing CALs which integrate the information of estimation errors. Finally, simulation results are presented to illustrate the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.

A torque controller based on adaptive robust control is proposed for trajectory tracking of nonholonomic wheeled mobile robot with kinematic and dynamic uncertainties. In this controller, the parametric uncertainties are estimated by the adaptive control, and the non-parametric uncertainties are compensated by the adaptive robust control. The operation efficiency of the system is improved by distinguishing the differences between parametric and non-parametric uncertainties. Furthermore, the transient performance of adaptive control is optimized by the adaptive robust control and the measurements of uncertainties bounds are avoided, this strengthened the applicability of the controller. The uniformly ultimately bounded of closed-loop control system is proved by the Lyapunov theory. Simulations show that the adversely impacts of complex system uncertainties can be eliminated effectively and the high-precision trajectory tracking is guaranteed.

In the paper, based on a combination of the robust control approach and an adaptive control technique, a design procedure of a new robust adaptive controller is proposed for power system load-frequency control with system parametric uncertainties. The system parametric uncertainties are obtained by changing parameters by 30% to 50% simultaneously from their typical values. In the design of the proposed controller, only the bounds of system parameters are required. The motivation of combining the robust control with an adaptive control is to use the robust control approach to deal with the small parametric uncertainties and to use the adaptive control for large parametric uncertainties so that stability of the overall system and good performance can both be achieved for all admissible uncertainties. The simulation results demonstrate that for the example system the proposed load-frequency controller can achieve good performance even in the presence of the generation rate constraint (GRC).

Uncertainties are prevalent in real-world applications of bipedal walking robots, which may deteriorate the robot’s locomotion performance and even cause instability. However, designing controllers to address uncertainties for bipedal robotic walking is challenging mainly due to the high complexity of the hybrid walking dynamics under uncertainties. In this paper, an adaptive robust control strategy is proposed by combining control Lyapunov functions and the construction of multiple Lyapunov functions for provably guaranteeing the stability and trajectory tracking performance of fully actuated bipedal robots in the presence of uncertainties such as modeling errors and unmodeled disturbances. Simulation results on a three-dimensional bipedal robot with nine revolute joints were conducted to illustrate that the proposed adaptive robust control law can ensure stability and satisfactory tracking performance in the presence of parametric modeling uncertainties and unmodeled nonlinearities.

In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L 2 tracking performance to stabilise the closed-loop system. This L 2 tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.

This paper addresses the trajectory tracking problem for an redundantly-actuated omnidirectional mobile manipulator system with uncertainties and disturbances. The proposed algorithm is robust adaptive control strategy and the parameter estimates are tuned online. First, for designing controller, the conservative upper-bounded function of dynamic model of omnidirectional mobile manipulator system is derived based on the dynamic structure properties. Then, a robust adaptive control scheme is presented to ensure trajectory tracking effect of this closed-loop system. The asymptotical stability is verified a Lyapunov method. Finally, simulation examples are given to demonstrate the proposed approach can guarantee the whole system converge to the desired manifold with prescribed performance.

The traditional robust adaptive control broadens the application of the routine adaptive control because of considering the uncertainty of the practice plant. However, traditional robust adaptive control solves the problem that the plant would be unstable under some uncertainties and it often ignores the dynamic property and steady-state property, which makes the traditional robust adaptive control unfit for practice plant, whose working condition changes randomly. To solve these problems, this paper designs multiple models robust adaptive controller for the plant which can be describeled by Autoregressive Moving Average (ARMA) model and with unmodeled dynamics. First, the normalization factor is used to convert the system unmodeled dynamic to bounded disturbance, and then, many fixed controller and two robust adaptive controllers are designed based on system reduced model according to the changing range of the working condition and the best controller computed by the performance index is chosen as the working controller. Simulation illustrates that the proposed method is preferable when there are unmodeled dynamics and changing working condition.

In a general Adaptive Robust Control (ARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties. However, when secondary purposes such as system health monitoring and prognosis are of equal importance, intelligent integration of output tracking performance oriented direct adaptive robust control (DARC) and the recently proposed accurate parameter estimation-based indirect adaptive robust control (IARC) is required. In this paper, we will consider such a seamless integration for a hydraulic robotic arm. The newly developed IARC design is first applied to the trajectory tracking for the robotic arm but with an improved estimation model, in which accurate parameter estimates are obtained through a parameter estimation algorithm that is based on physical dynamics rather than the tracking error dynamics. An integrated direct/indirect adaptive robust controller (DIARC) is then presented that preserves the excellent transient tracking performance of the direct ARC designs as well as the better parameter estimation process of the IARC design. The proposed Integrated Direct/Indirect Adaptive Robust Controller (DIARC) achieves the controller-identifier separation, thus enabling certain modularity in the controller design.

In this paper, a quadrotor with two manipulators constrained on a straight path is modeled and a robust adaptive controller is proposed for it. Adding two manipulators to quadrotor increases its capabilities and applications in industry. Here, these two manipulators are used to place the robot on a constraint path so that the quadrotor can perform monitoring operations more accurately, because the under-actuated quadrotor becomes over-actuated by these constrained manipulators and one can use this feature to accurately control the position of the robot. Reduced form of motion equations are derived for the constrained quadrotor and based on this a robust adaptive controller is proposed. The nonlinear terms in the dynamic model are approximated by basic functions with constant weights and adaptive laws are designed by projection operator. Stability analysis is performed based on the Lyapunov theory. Evaluation of the presented controller is done by some numerical simulations. The simulation results showed that the robot tracks the reference path with bounded error in spite of dynamic uncertainties and wind force and satisfies the considered constraints.

The design of a new hybrid controller for fractional-order uncertain chaotic systems with unknown time-varying delays

For floating production storage and offloading (FPSO) vessels, a dynamic positioning controller is necessary because using only a mooring system is not possible to keep the ship within a predefined region. Position control of the FPSO vessel is extremely challenging due to model uncertainties and unknown control coefficients. This paper develops a new robust adaptive positioning controller consisting of several components: adaptive law, dynamic surface control (DSC) technology, sigmoid tracking differentiator (STD), Nussbaum gain function, and structural reliability index. Model uncertainties can be estimated by the adaptive law derived from the Lyapunov theory. The DSC technology is used to eliminate repeated differentiation by introducing first-order filtering of the virtual control. The chattering-free STD with the characteristics of global fast convergence can estimate the derivatives of model uncertainties that are difficult to calculate directly. Therefore, the DSC and STD techniques make the proposed controller simpler to compute and easier to implement in engineering practice. Most of the traditional controllers require the information about the control coefficients to guarantee the stability of the closed-loop system while the Nussbaum gain function can remove the requirement for a priori knowledge of the sign of control coefficients. The capacity of the mooring system can be fully utilized to position the FPSO vessel by adjusting the structural reliability index on the premise of ensuring the safety of mooring lines, and hence less control effort is needed for the positioning controller. Simulations using two sets of system parameters demonstrate the proposed controller’s effectiveness. In addition, a qualitative comparison with the adaptive backstepping controller shows that our proposed controller is computationally more efficient and does not require a priori knowledge of the sign of control coefficients. A quantitative comparison with robust adaptive controller without the structural reliability shows that less control effort is needed using our proposed controller.

Liquid sloshing in moving or stationary containers and flexible uncertainty caused by the slosh are considered to be the most probable causing unexpected coupling effects on the dynamics of many systems such as aerospace, ground vehicles, and high speed industries arms. The coupling of dynamic liquid slosh in a container system with the uncertainty caused by the sensors or dampers is rare documented and this coupling can be considered as a highly nonlinear system. In this paper, an investigation is presented to demonstrate a new approach for enabling the reduction of the liquid slosh and uncertainty by implementing adaptive robust wavelet control technique. Starting by creating the mathematical dynamic model for the nonlinear slosh coupled by uncertainty, adaptive robust control based wavelet transform is applied for calculating optimal motion that minimize residual slosh and uncertainty. Subsequently the adaptive robust control based wavelet network approximation and the appropriate parameter algorithms for the container system with slosh and uncertainty are derived to achieve the feedback linearization, adaptive control, and H∞ tracking performance. The simulation results show that the effects of slosh errors and external uncertainty can be successfully attenuated within a desired attenuation level.

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.

This paper investigates the fault-tolerant synchronization control (FTSC) problem for a dual redundant hydraulic actuation system (DRHAS), which works on active/active (A/A) mode and suffers from a kind of common-mode fault (CMF), i.e., internal leakage faults occurring in both hydraulic actuator (HA) channels simultaneously due to a common cause. Firstly, in order to follow the position command and synchronize the force outputs of the two channels, a desired trajectory generator derived from the dynamics of the control surface is employed. Then, considering model uncertainties and nonlinear dynamics of the plant, an FTSC controller is designed based on adaptive robust control (ARC) theory and backstepping technology. The controller parameters, closely related to the fault parameters, are updated online to make the controller adapt to the fault condition only when the system performance degradation exceeds a prescribed tolerable level. It has been verified that the proposed FTSC scheme can guarantee the bounded stability of output tracking error system under common-mode fault. Finally, simulation results under two scenarios demonstrate the effectiveness of the proposed FTSC scheme.