Haoran WangAndrew J. KurdilaAndrea L'AfflittoDerek Oesterheld...
1128-1148页
查看更多>>摘要：ABSTRACT This article introduces native space embedding for robust adaptive control of ordinary differential equations that contain vector‐valued functional uncertainties in a reproducing kernel Hilbert space (RKHS). The proposed approach is based on a two‐phase method for analyzing and designing adaptive controllers. In the first phase, a limiting distributed parameter system (DPS), which describes the ideal closed‐loop system's performance, is introduced. The limiting DPS is not realizable in practice since it evolves in a generally infinite‐dimensional space. In the second phase, consistent finite‐dimensional approximations of the DPS are introduced to determine realizable controllers. Uniform ultimate bounds on the trajectory tracking error dynamics are derived for the functional uncertainty classes contained in the native space. These bounds are derived in terms of the power function of the RKHS or in terms of the fill distance of centers that define the scattered basis for approximations. Two numerical examples demonstrate the applicability of the proposed results.
Haoran WangAndrew J. KurdilaAndrea L'AfflittoDerek Oesterheld...
1128-1148页
查看更多>>摘要：ABSTRACT This article introduces native space embedding for robust adaptive control of ordinary differential equations that contain vector‐valued functional uncertainties in a reproducing kernel Hilbert space (RKHS). The proposed approach is based on a two‐phase method for analyzing and designing adaptive controllers. In the first phase, a limiting distributed parameter system (DPS), which describes the ideal closed‐loop system's performance, is introduced. The limiting DPS is not realizable in practice since it evolves in a generally infinite‐dimensional space. In the second phase, consistent finite‐dimensional approximations of the DPS are introduced to determine realizable controllers. Uniform ultimate bounds on the trajectory tracking error dynamics are derived for the functional uncertainty classes contained in the native space. These bounds are derived in terms of the power function of the RKHS or in terms of the fill distance of centers that define the scattered basis for approximations. Two numerical examples demonstrate the applicability of the proposed results.
查看更多>>摘要：ABSTRACT This article proposes an event‐triggered command filtering adaptive fuzzy tracking control strategy for nonlinear systems with multiple unknown high‐frequency gains and actuator constraints. During the design phase, a fuzzy logic system is used to approximate unknown nonlinear functions, whereas a novel equivalent transformation technique is introduced to simplify the design complexity of multiple input constraints by converting the input dead zones and saturation nonlinearities into a unified functional form. Subsequently, command filtering technology is used to address the issue of “complexity explosion” in control systems, and two additional adaptive laws are developed to assist in designing the compensation mechanism, which can both handle multiple unknown high‐frequency gains and eliminate the impact of filtering errors on the control performance. Furthermore, a relative threshold event‐triggered controller is developed to decrease data redundancy, and the viability of its triggering mechanism is demonstrated by excluding the Zeno phenomenon. The designed controller can ensure that the tracking error converges to a small vicinity near the origin, while all signals within the closed‐loop system remain bounded. Finally, the effectiveness of the proposed solution is validated through simulation results.
查看更多>>摘要：ABSTRACT This article proposes an event‐triggered command filtering adaptive fuzzy tracking control strategy for nonlinear systems with multiple unknown high‐frequency gains and actuator constraints. During the design phase, a fuzzy logic system is used to approximate unknown nonlinear functions, whereas a novel equivalent transformation technique is introduced to simplify the design complexity of multiple input constraints by converting the input dead zones and saturation nonlinearities into a unified functional form. Subsequently, command filtering technology is used to address the issue of “complexity explosion” in control systems, and two additional adaptive laws are developed to assist in designing the compensation mechanism, which can both handle multiple unknown high‐frequency gains and eliminate the impact of filtering errors on the control performance. Furthermore, a relative threshold event‐triggered controller is developed to decrease data redundancy, and the viability of its triggering mechanism is demonstrated by excluding the Zeno phenomenon. The designed controller can ensure that the tracking error converges to a small vicinity near the origin, while all signals within the closed‐loop system remain bounded. Finally, the effectiveness of the proposed solution is validated through simulation results.
查看更多>>摘要：ABSTRACT Within the framework of privacy protection, this paper explores the issue of finite‐time dynamic event‐triggered fuzzy control for vehicle suspension systems (VSSs) with imperfect communication channels. Firstly, a privacy protection mechanism is introduced into the VSSs to protect the privacy of data, which avoids data being accessed or misused without authorization, thereby enhancing the safety of the driving experience. Secondly, under the privacy protection mechanism, a novel dynamic event‐triggered mechanism (DETM) is proposed for VSSs. Compared to existing DETMs, the designed DETM not only adjusts the threshold based on real‐time environmental changes and achieves better system performance while reducing data transmissions but also reduces the risk of data leakage during transmission. Then, a finite‐time state encrypted fuzzy controller subject to imperfect communication channels is designed, which ensures that the closed‐loop system is finite‐time bounded. Simultaneously, the requirements for the VSSs are also met. Finally, a simulation example is provided to illustrate the effectiveness of the designed control approach.
查看更多>>摘要：ABSTRACT Within the framework of privacy protection, this paper explores the issue of finite‐time dynamic event‐triggered fuzzy control for vehicle suspension systems (VSSs) with imperfect communication channels. Firstly, a privacy protection mechanism is introduced into the VSSs to protect the privacy of data, which avoids data being accessed or misused without authorization, thereby enhancing the safety of the driving experience. Secondly, under the privacy protection mechanism, a novel dynamic event‐triggered mechanism (DETM) is proposed for VSSs. Compared to existing DETMs, the designed DETM not only adjusts the threshold based on real‐time environmental changes and achieves better system performance while reducing data transmissions but also reduces the risk of data leakage during transmission. Then, a finite‐time state encrypted fuzzy controller subject to imperfect communication channels is designed, which ensures that the closed‐loop system is finite‐time bounded. Simultaneously, the requirements for the VSSs are also met. Finally, a simulation example is provided to illustrate the effectiveness of the designed control approach.
查看更多>>摘要：ABSTRACT This article presents a decomposition‐based least squares estimation algorithm for the multivariate input nonlinear system. By using the hierarchical identification principle, the algorithm breaks down a nonlinear system into two subsystems, one containing the parameters of the linear dynamic block and the other containing the parameters of the nonlinear static block. Treating the unknown variables contained in the information vector of the model is to replace them with the outputs of an auxiliary model. The comparative results between the hierarchical recursive algorithm developed in this article and the recursive least squares algorithm are provided to test the proposed algorithms have lower computational cost and the higher estimation accuracy. Furthermore, the convergence of the hierarchical recursive algorithm is analyzed, which can guarantee the stability of the algorithm. The simulation results confirm the efficacy of the derived algorithm in effectively estimating the parameters of the nonlinear systems.
查看更多>>摘要：ABSTRACT This article presents a decomposition‐based least squares estimation algorithm for the multivariate input nonlinear system. By using the hierarchical identification principle, the algorithm breaks down a nonlinear system into two subsystems, one containing the parameters of the linear dynamic block and the other containing the parameters of the nonlinear static block. Treating the unknown variables contained in the information vector of the model is to replace them with the outputs of an auxiliary model. The comparative results between the hierarchical recursive algorithm developed in this article and the recursive least squares algorithm are provided to test the proposed algorithms have lower computational cost and the higher estimation accuracy. Furthermore, the convergence of the hierarchical recursive algorithm is analyzed, which can guarantee the stability of the algorithm. The simulation results confirm the efficacy of the derived algorithm in effectively estimating the parameters of the nonlinear systems.
查看更多>>摘要：ABSTRACT Herein, a new barrier function (BF)‐based adaptive integral terminal sliding mode control (ABF‐ITSMC) with output constraints methodology is proposed for high‐order nonlinear systems (HONSs) exposed to unknown lumped disturbance. The proposed algorithms utilize the backstepping control (BSC) technique for management of high‐order mismatched uncertainties by incorporating the dynamic surface control (DSC) via first order low pass filter (FOLPF) to eliminate “complexity explosion.” To deal with the output constraints requirement, a barrier Lyapunov function is introduced for the design of the virtual control law. Besides, an integrate sliding manifold surface is designed to obtain the finite time convergent and singularity free features to enhance the robustness. Additionally, an adaption control gain law is devised by BF to precisely estimate the real‐time lumped disturbance without the requirement of any boundary information. Lyapunov stability theory is employed for proving that the uniformly and eventually bounded tracking deviation of the overall system. Lastly, simulations with two cases are conducted to validate the devised method with respect to the strength and effectiveness by comparing the derived outcomes of the method with that of the existing method.
查看更多>>摘要：ABSTRACT Herein, a new barrier function (BF)‐based adaptive integral terminal sliding mode control (ABF‐ITSMC) with output constraints methodology is proposed for high‐order nonlinear systems (HONSs) exposed to unknown lumped disturbance. The proposed algorithms utilize the backstepping control (BSC) technique for management of high‐order mismatched uncertainties by incorporating the dynamic surface control (DSC) via first order low pass filter (FOLPF) to eliminate “complexity explosion.” To deal with the output constraints requirement, a barrier Lyapunov function is introduced for the design of the virtual control law. Besides, an integrate sliding manifold surface is designed to obtain the finite time convergent and singularity free features to enhance the robustness. Additionally, an adaption control gain law is devised by BF to precisely estimate the real‐time lumped disturbance without the requirement of any boundary information. Lyapunov stability theory is employed for proving that the uniformly and eventually bounded tracking deviation of the overall system. Lastly, simulations with two cases are conducted to validate the devised method with respect to the strength and effectiveness by comparing the derived outcomes of the method with that of the existing method.
NETL
NSTL
Wiley