Active Model-Based Hysteresis Compensation and Tracking Control of Pneumatic Artificial Muscle

The hysteretic nonlinearity of pneumatic artificial muscle (PAM) is the main factor that degrades its tracking accuracy. This paper proposes an efficient hysteresis compensation method based on the active modeling control (AMC). Firstly, the Bouc–Wen model is adopted as the reference model to describe the hysteresis of the PAM. Secondly, the modeling errors are introduced into the reference model, and the unscented Kalman filter is used to estimate the state of the system and the modeling errors. Finally, a hysteresis compensation strategy is designed based on AMC. The compensation performances of the nominal controller with without AMC were experimentally tested on a PAM. The experimental results show that the proposed controller is more robust when tracking different types of trajectories. In the transient, both the overshoot and oscillation can be successfully attenuated, and fast convergence is achieved. In the steady-state, the proposed controller is more robust against external disturbances and measurement noise. The proposed controller is effective and robust in hysteresis compensation, thus improving the tracking performance of the PAM.

Model-based Damage Detection through Physics Guided Learning

Data-driven learning approaches have gained a lot of interest in evaluating and validating complex dynamic systems. One of the main challenges for developing a reliable learning model is the lack of training data covering a large range of various operational conditions. Extensive training data can be generated using a physics-based simulation model. On the other hand, the learning algorithm should be still tested with experimental data obtained from the actual system. Modeling errors may lead to a statistical divergence between the simulation training data and the experimental testing data, causing poor performance, especially for domain-agnostic black-box learning methods. To close the gap between the simulation and experimental domains, this paper proposes a physics-guided learning approach that combines the power of the neural network with domain-specific physics knowledge. Specifically, the proposed architecture integrates physical parameters extracted from the physics-based simulation model into the intermediate layers of the neural network to constrain the learning process. To demonstrate the effectiveness of the proposed approach, the architecture is adopted to a damage classification problem for a three-story structure. Our results show that the accuracy for localizing the damage correctly based on experimental data improves significantly over black-box models, especially under large modeling errors. In this paper, we also use the physics-based intermediate layers to analyze the interpretability of the classification results.

An Improved Smooth Variable Structure Filter for Robust Target Tracking

As a new-style filter, the smooth variable structure filter (SVSF) has attracted significant interest. Based on the predictor-corrector method and sliding mode concept, the SVSF is more robust in the face of modeling errors and uncertainties compared to the Kalman filter. Since the estimation performance is usually insufficient in real cases where the measurement vector is of fewer dimensions than the state vector, an improved SVSF (ISVSF) is proposed by combining the existing SVSF with Bayesian theory. The ISVSF contains two steps: firstly, a preliminary estimation is performed by SVSF. Secondly, Bayesian formulas are adopted to improve the estimation for higher accuracy. The ISVSF shows high robustness in dealing with modeling uncertainties and noise. It is noticeable that ISVSF could deliver satisfying performance even if the state of the system is undergoing a sudden change. According to the simulation results of target tracking, the proposed ISVSF performance can be better than that obtained with existing filters.

Novel Hybrid Interval Type-2 Fuzzy Adaptive Backstepping Control for a Class of Uncertain Discrete-Time Nonlinear Systems

A Novel hybrid backstepping interval type-2fuzzy adaptive control (HBT2AC) for uncertain discrete-time nonlinear systems is presented in this paper. The systems are assumed to be defined with the aid of discrete equations with nonlinear uncertainties which are considered as modeling errors and external unknown disturbances, and that the observed states are considered disturbed. The adaptive fuzzy type-2 controller is designed, where the fuzzy inference approach based on extended single-input rule modules (SIRMs) approximate the modeling errors, non-measurable states and adjustable parameters are estimated using derived weighted simplified least squares estimators (WSLS). We can prove that the states are bounded and the estimation errors stand in the neighborhood of zero. The efficiency of the approach is proved by simulation for which the root mean squares criteria are used which improves control performance.

Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245m0.0224m0.0039rad]T and the average localization errors using the conventional EKF are [1.0405m2.2700m0.1782rad]T, [0.4963m0.3482m0.0254rad]T and [0.2774m0.3897m0.0353rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

A Nonlinear Model Predictive Control Design for Autonomous Multivehicle Merging into Platoons

Integrated control for automated vehicles in platoons with nonlinear coupled dynamics is developed in this article. A nonlinear MPC approach is used to address the multi-input multi-output (MIMO) nature of the problem, the nonlinear vehicle dynamics, and the platoon constraints. The control actions are determined by using model-based prediction in conjunction with constrained optimization. Two distinct scenarios are then simulated. The first scenario consists of the multivehicle merging into an existing platoon in a controlled environment in the absence of noise, whereas the effects of external disturbances, modeling errors, and measurement noise are simulated in the second scenario. An extended Kalman filter (EKF) is utilized to estimate the system states under the sensor and process noise effectively. The simulation results show that the proposed approach is a suitable tool to handle the nonlinearities in the vehicle dynamics, the complication of the multivehicle merging scenario, and the presence of modeling uncertainties and measurement noise.

Full-State-Constrained Adaptive Control for a Class of UAVs Suffering from Coupled Uncertainties Using the HOBLF

Nowadays, the practical tasks of UAVs are becoming more and more complicated and diversified. In the practical flight process, the large-scale changes of the flight environment, the modeling errors, and the external disturbances may induce the instability of the UAV flight system. Meanwhile, the constraints of the UAV attitudes also have to be guaranteed during the flight process. However, most existing control methods still have limitations in handling the constraints and the multisource disturbances simultaneously. To address this problem, in this paper, we focus on the actual output tracking control for the UAV systems with full-state constraints and multisource disturbances. Firstly, a high-order tan-type barrier Lyapunov function (HOBLF) has been constructed for the UAV to maintain the full-state constraints. Secondly, by combining the adaptive backstepping technique and the fuzzy logic systems, the modeling errors and the unknown nonlinearities of the UAV attitude control system can be handled. Moreover, by properly constructing several adaptive laws, the time-varying disturbances existing in the UAV attitude control system can be suppressed. Finally, the full-state-constrained antidisturbance controller is formed, ensuring that the tracking error approaches arbitrarily to small neighborhood and does not violate the given constraints. The simulation results illustrate the feasibility and the advantages of the proposed method.

Analytical, Experimental, and Numerical Investigation of Energy in Hydraulic Cylinder Dynamics of Agriculture Scale Excavators

This paper discusses energy behaviors in hydraulic cylinder dynamics, which are important for model-based control of agriculture scale excavators. First, we review hydraulic cylinder dynamics and update our physical parameter identification method to agriculture scale experimental excavators in order to construct a nominal numerical simulator. Second, we analyze the energy behaviors from the port-Hamiltonian point of view which provides many links to model-based control at laboratory scale at least. At agriculture scale, even though the nominal numerical simulator is much simpler than an experimental excavator, the analytical, experimental, and numerical energy behaviors are very close to each other. This implies that the port-Hamiltonian point of view will be applicable in agriculture scale against modeling errors.