Projection-type Adaptation Laws Research Articles

Adaptive Tracking and Stabilization of Nonholonomic Mobile Robots With Input Saturation

This article investigates the control problems of tracking and stabilization simultaneously for nonholonomic mobile robots subjected to input constraints and parameter uncertainties. A saturated time-varying controller is developed by applying a novel error state modification with bounded auxiliary variables. To improve the estimation performance of unknown kinematic and dynamic parameters, two projection-type adaptation laws are presented, while radial basis function approximations are used to estimate uncertain dynamics. Simulations are conducted to verify the effectiveness of the proposed controller.

Adaptive sliding mode control for tunnel boring machine cutterhead telescopic system with uncertainties

PurposeThis paper aims to present an adaptive sliding mode control (ASMC) for tunnel boring machine cutterhead telescopic system with uncertainties to achieve a high-precision trajectory in complex strata. This method could be applied to solve the problems caused by linear and nonlinear model uncertainties.Design/methodology/approachFirst, an integral-type sliding surface is defined to reduce the static tracking error. Second, a projection type adaptation law is designed to approximate the linear and nonlinear redefined parameters of the electrohydraulic system. Third, a nonlinear robust term with a continuous approximation function is presented for handling load force uncertainty and reducing sliding mode chattering. Moreover, Lyapunov theory is applied to guarantee the stability of the closed-loop system. Finally, the effectiveness of the proposed controller is proved by comparative experiments on a scaled test rig.FindingsThe linear and nonlinear model uncertainties lead to large variations in the dynamics of the mechanism and the tracking error. To achieve precise position tracking, an adaptation law was integrated into the sliding mode control which compensated for model uncertainties. Besides, the inherent sliding mode chattering was reduced by a continuous approximation function, while load force uncertainty was solved by a nonlinear robust feedback. Therefore, a novel ASMC for tunnel boring machine cutterhead telescopic system with uncertainties can improve its tracking precision and reduce the sliding mode chattering.Originality/valueTo the best of the authors’ knowledge, the ASMC is proposed for the first time to control the tunnel boring machine cutterhead telescopic system with uncertainties. The presented control is effective not only in control accuracy but also in parameter uncertainty.

Adaptive robust integral sliding mode pitch angle control of an electro-hydraulic servo pitch system for wind turbine

Abstract A new electro-hydraulic servo pitch system is proposed to enhance pitch angle control performances and hence to smooth generator power fluctuations for wind turbines. The pitch system mainly consists of a variable-speed hydraulic pump, a hydraulic motor and a pitch gear set. The hydraulic pump is employed to directly regulate the direction and rotational speed of the hydraulic motor that achieves the final pitch angle control through the gear set. An adaptive robust integral sliding mode pitch angle controller and a projection type adaptation law are synthesized to accurately track the desired pitch angle trajectory and compensate for model uncertainties and uncertain disturbances. The proposed controller and the adaptation law have their achievable performances in practice. Comparative results verify that the proposed pitch angle controller, together with the adaptation scheme, gives improved performances for the accurate pitch angle tracking and generator power control regardless of various uncertainties and disturbances.

Delay-Tolerant Adaptive Robust Tracking Control of Uncertain Time-Delay Systems

The simultaneous presence of parameter variations, time-varying disturbances, and delay in the control loop of an $n{\text{th}}$ -order (possibly unstable) dynamical system makes formal tracking control design difficult. Existing model-based control methodologies can handle only a subset of the above-mentioned complexities, and hence, become insufficient to accurately track the desired trajectory. In this paper, a prediction-based adaptive robust control framework is proposed for high performance control of dynamical systems subject to the above-mentioned complexities, which involves the following. First, prediction-based projection-type adaptation laws with model compensation to reduce the effect of parameter uncertainties under delay and time-varying disturbances. Second, a robust prediction scheme that factors in both unknown parameters and disturbance uncertainties under delay to handle the resulting unmatched disturbances. Third, prediction-based continuous robust feedback to attenuate the cumulative effect of disturbance terms due to uncertain prediction. The controller guarantees semiglobal, exponential convergence of the tracking error with an ultimate error bound proportional to delay, disturbance, and controller gain. The controller effectiveness is demonstrated with an illustrative flight control example and compared with the baseline adaptive robust controller.

Prediction‐based Adaptive Robust Tracking Control of an Uncertain First‐Order Time‐Delay System

AbstractThis paper considers the tracking problem of a delayed uncertain first‐order system which is simultaneously subject to (possibly large) known input delay, unknown but bounded time‐varying disturbance, and unknown plant parameter. The proposed predictor adaptive robust controller (PARC) involves prediction‐based projection type adaptation laws with model compensation and prediction‐based continuous robust feedback such that the closed loop system has global exponential convergence with an ultimate bound proportional to delay, disturbance bound, and switching gain. Further, if there are only delay and parameter uncertainties after some finite time, then semi‐global asymptotic tracking is guaranteed. The proposed design is shown to have significant closed loop performance improvement over the baseline controller.

Improved Transparency for Haptic Systems with Complex Environments * *This work was supported by NSERC and the Government of Nova Scotia, Canada

Abstract Haptic systems in surgical training applications require highly accurate force feedback. However, high-resolution models of the virtual environment (VE) can be very computationally intensive, lowering the force feedback update rate. The objective of this work is to improve transparency by developing a predictor that approximates the complex nonlinear VE as a linear VE with a much higher update rate. By using feedback from the more accurate but slower VE, the predictor can provide increased transparency to the operator. The full control design of the predictor and haptic controller is considered for a nonlinear haptic device. The predictor is designed using Lyapunov-based methods, by numerical solution of a linear matrix inequality. The predictor uses a projection-type adaptation law to estimate the unknown VE parameters. Simulation results are shown to demonstrate the effectiveness of the method assuming unknown and time-varying VE parameters.

A novel meta-cognitive fuzzy-neural model with backstepping strategy for adaptive control of uncertain nonlinear systems

A significant increase of system complexity and state changes requires an effective data-driven system identification and machine learning algorithm to deal with the control of nonlinear systems. Using streams of data collected from the system, the data-driven controller aims to stabilize the unknown nonlinear systems with modeling uncertainties and external disturbances. The paper proposes a novel data-driven adaptive control approach with the backstepping strategy for online control of unknown nonlinear systems with no human intervention. A new meta-cognitive fuzzy-neural model is first introduced to construct the unknown system dynamics and utilize the self-adaptive tracking error as the learning parameters to determine the deletion of the state data, adapt the structure and parameters of the controller using the information extracted from nonstationary data streams. Subsequently, the control law is constructed based on the meta-cognitive fuzzy-neural model rather than the actual systems and the backstepping control strategy. Then, the stability analysis of the closed-loop system is presented from the Lyapunov function and shows that the tracking errors converge to zero. In the proposed control scheme, the bound of the control input is considered and ensured via the stable projection-type adaptation laws of the parameters. Moreover, in order to further save online computation time, only the parameters of the rule nearest to the current state are updated while those of other rules maintain unchanged. This is different from the existing studies where the parameters of all rules are updated. Finally, various simulation results from an inverted pendulum system and a thrust active magnetic bearing system demonstrate the superior performance of the proposed meta-cognitive fuzzy-neural control approach.

Adaptive robust attitude control and active vibration suppression of flexible spacecraft

This paper presents the large angle attitude manoeuvre control design of a single-axis flexible spacecraft system that consists of a central rigid body and a cantilever beam with bonded piezoelectric sensor/actuator pairs as a flexible appendage. The proposed control strategy combines the attitude controller designed by the adaptive robust control technique with the active vibration controller designed by the positive position feedback control method. The desired angular position of the spacecraft is planned and an adaptive robust attitude control approach based on a projection type adaptation law is proposed to track the planned path and to achieve precise attitude manoeuvre control. Meanwhile, the positive position feedback control method is applied to actively increase the damping of the flexible appendage and to suppress the residual vibration induced by manoeuvre. Improved transient and steady state performance during and after large angle attitude manoeuvre can be both achieved by integration of the technical merits of all these control methods. Analytical and numerical results illustrate the effectiveness of this approach.

Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems

This paper proposes a robust self-organizing neural-fuzzy-control (RSONFC) scheme for a class of uncertain nonlinear multiple-input-multiple-output (MIMO) systems. We first develop a self-organizing neural-fuzzy network (SONFN) with concurrent structure and parameter learning. The fuzzy rules of SONFN are generated or pruned systematically. The proposed RSONFC scheme comprises an SONFN identifier, an uncertainty observer, and a supervisory controller. The SONFN identifier functions as the principal controller, and the uncertainty observer is designed to oversee uncertainties within the compound system. The supervisory controller combines sliding-mode control (SMC) and an adaptive bound-estimation scheme with various weights to achieve H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> tracking performance with a desired level of attenuation. Projection-type adaptation laws of network parameters developed using the Lyapunov's synthesis approach guarantee the stability of the overall control system. Simulation studies on a single-link flexible-joint manipulator and a two-link robot demonstrate the effectiveness of the proposed control scheme.

Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems

This paper proposes a novel dynamic structure neural fuzzy network (DSNFN) to address the adaptive tracking problems of multiple-input–multiple-output (MIMO) uncertain nonlinear systems. The proposed control scheme uses a four-layer neural fuzzy network (NFN) to estimate system uncertainties online. The main feature of this DSNFN is that it can either increase or decrease the number of fuzzy rules over time based on tracking errors. Projection-type adaptation laws for the network parameters are derived from the Lyapunov synthesis approach to ensure network convergence and stable control. A hybrid control scheme that combines the sliding-mode control and the adaptive bound estimation control with different weights improves system performance by suppressing the influence of external disturbances and approximation errors. As the employment of the DSNFN, high-quality tracking performance could be achieved in the system. Furthermore, the trained network avoids the problems of overfitting and underfitting. Simulations performed on a two-link robot manipulator demonstrate the effectiveness of the proposed control scheme.

Iterative learning of model reference adaptive controller for uncertain nonlinear systems with only output measurement

In this paper, a model reference adaptive control strategy is used to design an iterative learning controller for a class of repeatable nonlinear systems with uncertain parameters, high relative degree, initial output resetting error, input disturbance and output noise. The class of nonlinear systems should satisfy some differential geometric conditions such that the plant can be transformed via a state transformation into an output feedback canonical form. A suitable error model is derived based on signals filtered from plant input and output. The learning controller compensates for the unknown parameters, uncertainties and nonlinearity via projection type adaptation laws which update control parameters along the iteration domain. It is shown that the internal signals remain bounded for all iterations. The output tracking error will converge to a profile which can be tuned by design parameters and the learning speed is improved if the learning gain is large.