Adaptive Tracking and Obstacle Avoidance Control for Mobile Robots with Unknown Sliding

An adaptive control approach is proposed for trajectory tracking and obstacle avoidance for mobile robots with consideration given to unknown sliding. A kinematic model of mobile robots is established in this paper, in which both longitudinal and lateral sliding are considered and processed as three time-varying parameters. A sliding model observer is introduced to estimate the sliding parameters online. A stable tracking control law for this nonholonomic system is proposed to compensate the unknown sliding effect. From Lyapunov-stability analysis, it is proved, regardless of unknown sliding, that tracking errors of the controlled closed-loop system are asymptotically stable, the tracking errors converge to zero outside the obstacle detection region and obstacle avoidance is guaranteed inside the obstacle detection region. The efficiency and robustness of the proposed control system are verified by simulation results.

An adaptive unscented Kalman filter-based adaptive tracking control for wheeled mobile robots with control constrains in the presence of wheel slipping

A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.

Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control

This paper presents an iterative learning control (ILC) strategy to resolve the trajectory tracking problem of wheeled mobile robots (WMRs) based on dynamic model. In the previous study of WMRs’ trajectory tracking, ILC was usually applied to the kinematical model of WMRs with the assumption that desired velocity can be tracked immediately. However, this assumption cannot be realized in the real world at all. The kinematic and dynamic models of WMRs are deduced in this chapter, and a novel combination of D-type ILC algorithm and dynamic model of WMR with random bounded disturbances are presented. To analyze the convergence of the algorithm, the method of contracting mapping, which shows that the designed controller can make the velocity tracking errors converge to zero completely when the iteration times tend to infinite, is adopted. Simulation results show the effectiveness of D-type ILC in the trajectory tracking problem of WMRs, demonstrating the effectiveness and robustness of the algorithm in the condition of random bounded disturbance. A comparative study conducted between D-type ILC and compound cosine function neural network (NN) controller also demonstrates the effectiveness of the ILC strategy.

Generalized Extended State Observer Approach to Robust Tracking Control for Wheeled Mobile Robot with Skidding and Slipping

This paper proposes a robust tracking controller based on a Generalized Extended State Observer (GESO) method for a wheeled mobile robot (WMR) with unknown skidding and slipping. Skidding and slipping of a WMR are inevitable in practice. We regard skidding and slipping as disturbances and modify the dynamics model to consider them simply. Then, we adopt the GESO to design a robust tracking controller at kinematic and dynamic level. Using Lyapunov theory, we derive the control law and guarantee the stability of the control system. The proposed control achieves attenuation of the disturbance and convergence of the tracking errors. The performance of the proposed method is verified by some simulation results.

FAST LEARNING ALGORITHM OF WAVELET NETWORK BASED ON FAST WAVELET TRANSFORM

In this paper, a novel learning algorithm of wavelet networks based on the Fast Wavelet Transform (FWT) is proposed. It has many advantages compared to other algorithms, in which we solve the problem in previous works, when the weights of the hidden layer to the output layer are determined by applying the back propagation algorithm or by direct solution which requires to compute the matrix inversion, this may cause intensive computation when the learning data is too large. However, the new algorithm is realized by iterative application of FWT to compute the connection weights. Furthermore, we have extended the novel learning algorithm by using Levenberg–Marquardt method to optimize the learning functions. The experimental results have demonstrated that our model is remarkably more refreshing than some of the previously established models in terms of both speed and efficiency.

ADAPTIVE CONTROL FOR MIMO UNCERTAIN NONLINEAR SYSTEMS USING RECURRENT WAVELET NEURAL NETWORK

Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort.

Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems

This paper proposes a wavelet adaptive backstepping control (WABC) system for a class of second-order nonlinear systems. The WABC comprises a neural backstepping controller and a robust controller. The neural backstepping controller containing a wavelet neural network (WNN) identifier is the principal controller, and the robust controller is designed to achieve L2 tracking performance with desired attenuation level. Since the WNN uses wavelet functions, its learning capability is superior to the conventional neural network for system identification. Moreover, the adaptation laws of the control system are derived in the sense of Lyapunov function and Barbalat's lemma, thus the system can be guaranteed to be asymptotically stable. The proposed WABC is applied to two nonlinear systems, a chaotic system and a wing-rock motion system to illustrate its effectiveness. Simulation results verify that the proposed WABC can achieve favorable tracking performance by incorporating of WNN identification, adaptive backstepping control, and L2 robust control techniques.

A Competitive Wavelet Network for Signal Clustering

This correspondence proposes a novel signal clustering method based on the unsupervised training of a wavelet network. The synaptic weights are parameterized by wavelet basis functions, which are adjusted by a competitive algorithm that makes use of the neighborhood concept proposed by Kohonen. The robustness of the wavelet network with respect to noise is illustrated in a simulated problem, in which dynamic systems are grouped on the basis of their step responses. An example involving clustering of electrocardiographic signals taken from the MIT-BIH database is also presented. In this case, the ability of the proposed network to perform clustering at successive resolution levels is illustrated. The possibility of interpreting the information encoded in the network at the end of training is also discussed.