Liyang Wang

Energy-Efficient SVM Learning Control System for Biped Walking Robots

An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.

Energy-Efficiency-Based Gait Control System Architecture and Algorithm for Biped Robots

A novel systematic architecture and algorithm of gait control based on energy-efficiency optimization is represented, aiming at the fatal problem of high energy consumption for biped robots walking in unstructured environments. By designing an optimal controller to minimize the energy criterion, the proposed method provides a remarkable descend rate of energy consumption in the trunk-rotation walking mechanism. The proposed algorithm is able to optimize the trunk trajectory by minimizing the energy-related cost function while guaranteeing zero-moment point (ZMP) criterion. Simulations and experimental results show the validity of the method.

A UKF-Based Predictable SVR Learning Controller for Biped Walking

An unscented Kalman filter (UKF)-based predictable support vector regression (SVR) learning controller is proposed to improve the flexibility of biped walking robots. After estimating the biped states of the next moment using a UKF, an SVR learning controller with the predicted biped states is implemented to ensure the zero moment point (ZMP) stability. Using the predicted biped states, the SVR learning controller can predictably adjust the posture of the trunk timely and properly to adapt to the dynamic posture of the whole body. The flexibility of biped robots is enhanced by the proposed method, which is promising for realizing the stable biped walking in unstructured environments. Simulation and experimental results demonstrate the superiority of the proposed methods.

Fuzzy SVM learning control system considering time properties of biped walking samples

To learn biped walking dynamics accurately, and then compensate time-varying external disturbances timely, a time-sequence-based fuzzy SVM (TSF-SVM) learning control system considering time properties of biped walking samples is proposed. For the first time, time-sequence-based triangular and Gaussian fuzzy membership functions have been proposed for the single support phase (SSP) and the double support phase (DSP), respectively, according to time properties of different biped phases, which provides an effective way to formulate time properties of biped walking samples in the context of time-varying external disturbances. In addition, a time-sequence-based moving learning window (TS-MLW) is proposed for online training of the proposed TSF-SVM. The performance of the proposed TSF-SVM is compared with other typical intelligent methods; simulation results demonstrate that the proposed method is more sensitive to occasional external disturbances, which increases the stability margin and prevents the robot from falling down.

Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking

An interval type-2 fuzzy weighted support vector machine (IT2FW-SVM) is proposed to address the problem of high energy consumption for biped walking robots. Different from the traditional machine learning method of ‘copy learning’, the proposed IT2FW-SVM obtains lower energy cost and larger zero moment point (ZMP) stability margin using a novel strategy of ‘selective learning’, which is similar to human selections based on experience. To handle the uncertainty of the experience, the learning weights in the IT2FW-SVM are deduced using an interval type-2 fuzzy logic system (IT2FLS), which is an extension of the previous weighted SVM. Simulation studies show that the existing biped walking which generates the original walking samples is improved remarkably in terms of both energy efficiency and biped dynamic balance using the proposed IT2FW-SVM.

A SVM controller for the stable walking of biped robots based on small sample sizes

A SVM gait controller is proposed for the stable walking of biped robots.The SVM controller is equipped with a mixed kernel function for the gait learning.The SVM controller is trained based on small sample sizes.The proposed method can satisfy the ZMP criterion well. Conventional machine learning methods such as neural network (NN) uses empirical risk minimization (ERM) based on infinite samples, which is disadvantageous to the gait learning control based on small sample sizes for biped robots walking in unstructured, uncertain and dynamic environments. Aiming at the stable walking control problem in the dynamic environments for biped robots, this paper puts forward a method of gait control based on support vector machines (SVM), which provides a solution for the learning control issue based on small sample sizes. The SVM is equipped with a mixed kernel function for the gait learning. Using ankle trajectory and hip trajectory as inputs, and the corresponding trunk trajectory as outputs, the SVM is trained based on small sample sizes to learn the dynamic kinematics relationships between the legs and the trunk of the biped robots. Robustness of the gait control is enhanced, which is propitious to realize the stable biped walking, and the proposed method shows superior performance when compared to SVM with radial basis function (RBF) kernels and polynomial kernels, respectively. Simulation results demonstrate the superiority of the proposed methods.

A time-sequence-based fuzzy support vector machine adaptive filter for tremor cancelling for microsurgery

Hand tremors may cause some blemishes in precision and stability of a minimally invasive surgery (MIS). To track the tremor signals accurately, there are two main problems left to be settled. First, it is not practical to collect the sample data of tremor in large scale in practical applications. To deal with the hand tremors, a learning method based on small samples sizes and high dimensional input space is needed. Second, the hand tremors have time-varying characteristics. This fact is neglected by traditional learning methods, which could lead to imprecision and instability of a MIS. In this work, a time-sequence-based fuzzy support vector machine adaptive filter (TSF-SVMAF) for tremor cancelling is proposed. The proposed method is based on support vector machine and time series. It is suitable for solving the problem that the inputs are time-varying and the samples are small-scale. To cancel the time-varying hand tremors, different learning-weight-functions are designed for tremor signals with different frequencies. From the simulation results, compared with the existing methods such as back propagation (BP), weighted-frequency Fourier combiner (WFLC) and bandlimited multiple Fourier linear combiner (BMFLC), the proposed method has better performance when learning the time-varying hand tremors with small sample sizes.