Showing papers in "Asian Journal of Control in 2021"
148 citations
67 citations
TL;DR: An object perception and path planning algorithm which can detect hollow objects and calculate a traversing path in real‐time and is designed to enhance the safety and stability during traversing is proposed.
60 citations
TL;DR: Simulation results prove that the derived TP model approximately mimics the behavior of the nonlinear model; both system responses and numerical approximation errors are illustrated.
Abstract: This paper presents the application of the tensor product (TP)‐based model transformation approach to produce Tower CRrane (TCR) systems models. The modeling approach starts with a nonline...
50 citations
47 citations
TL;DR: In this article, trajectory tracking of robotic manipulators under varying loads with uncertainties and external disturbances is obtained by proposing model-independent adaptive fractional h(h) h(H) method.
Abstract: In this study, trajectory tracking of robotic manipulators under varying loads with uncertainties and external disturbances is obtained by proposing model‐independent adaptive fractional h
44 citations
TL;DR: Two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC).
Abstract: In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD ...
40 citations
37 citations
36 citations
TL;DR: In this paper, the authors address the trajectory tracking control problem for an underactuated surface vessel (USV) with full state constraints and solve the inconvenience caused by the USV unbalancing.
Abstract: This paper addresses the trajectory tracking control problem for an underactuated surface vessel (USV) with full state constraints. In order to solve the inconvenience caused by the USV un...
25 citations
TL;DR: An adaptive path following control method based on least squares support vector machines (LS-SVM) to deal with parameter changes of the motion model and results show that the proposed method can handle the above situations effectively.
Abstract: Since vessel dynamics could vary during maneuvering because of load changes, speed changing, environmental disturbances, aging of mechanism, etc., the performance of model-based path following control may be degraded if the controller uses the same motion model all the time. This article proposes an adaptive path following control method based on least squares support vector machines (LS-SVM) to deal with parameter changes of the motion model. The path following controller consists of two components: the online identification of varying parameters and model predictive control (MPC) using the adaptively identified models. For the online parameter identification, an improved online LS-SVM identification method is proposed based on weighted LS-SVM. Specifically, the objective function of LS-SVM is modified to decrease the errors of parameter estimation, an index is proposed to detect the possible model changes, which speeds up the rate of parameter convergence, and the sliding data window strategy is used to realize the online identification. MPC is combined with the line-of-sight guidance to track straight line reference paths. Finally, case studies are conducted to verify the effectiveness of the proposed path following adaptive controller. Typical parameter varying scenarios, such as rudder aging, current variations and changes of the maneuverability are considered. Simulation results show that the proposed method can handle the above situations effectively.
TL;DR: In this article, a second-order non-singular fast terminal sliding mode controller is proposed for robotic manipulators in the presence of uncertainties and disturbances, and adaptive control is applied.
Abstract: In this paper, a second‐order non‐singular fast terminal sliding mode controller is proposed for robotic manipulators in the presence of uncertainties and disturbances. Adaptive control is...
TL;DR: The extended Kalman filter (EKF) is a widely used method in navigation applications as mentioned in this paper, but the EKF suffers from noise covariance uncertainty, potentially causing it to perform poorly in practice.
Abstract: The extended Kalman filter (EKF) is a widely used method in navigation applications. The EKF suffers from noise covariance uncertainty, potentially causing it to perform poorly in practice...
TL;DR: This paper studies the super-twisting algorithm (STA) for adaptive sliding mode design and tunes the two gains of STA on line simultaneously such that a second order sliding mode can take place with small rectifying gains.
Abstract: This paper studies the super-twisting algorithm (STA) for adaptive sliding mode design. The proposed method tunes the two gains of STA on line simultaneously such that a second order sliding mode can take place with small rectifying gains. The perturbation magnitude is obtained exactly by employing a third-order sliding mode observer in opposition to the conventional approximations by using a first order low pass filter. While driving the sliding variable to the sliding mode surface, one gain of the STA automatically converges to an adjacent area of the perturbation magnitude in finite time. The other gain is adjusted by the above gain to guarantee the robustness of the STA. This method requires only one parameter to be adjusted. The adjustment is straightforward because it just keeps increasing until it fulfills the convergence constraints. For large values of the parameter, chattering in the update law of the two gains is avoided by employing a geometry based backward Euler integration method. The usefulness is illustrated by an example of designing an equivalent control based sliding mode control (ECBC-SMC) with the proposed adaptive STA for a perturbed LTI system.
TL;DR: An adaptive control scheme based on a radial basis function neural network (RBFNN) is proposed for the kinematic model of a car with n trailers, using Lyapunov stability theory and the adaptive law of network weights to ensure the stability of the error system.
TL;DR: A novel algorithm is proposed, such that, by considering just one channel of communication, a dynamic observer synchronises with a chaotic system in predefined‐time, based on the equivalent control concept of sliding mode theory.
TL;DR: A path‐planning algorithm drives the robotic manipulator towards the hand of the human and permits to adapt the pose of the tool center point of the robot to the poses of the handof the human worker.
TL;DR: A new intelligent control scheme that uses the Fuzzy Super Twisting Sliding Mode Concept and PID controller tuned with the Artificial Bee Colony (ABC) algorithm to control a full vehicle active suspension system with new convergence proof is proposed.
Abstract: This article proposes a new intelligent control scheme that uses the Fuzzy Super Twisting Sliding Mode Concept (FSTSMC) and PID controller tuned with the Artificial Bee Colony (ABC) algori...
TL;DR: In this article, the authors focus on the numerical approximation of nabla fractional order systems with the conditions of nonzero initial instant and non zero initial state, and propose an original algorithm to estimate the parameters of the approximate model with the help of vector fitting method.
Abstract: The paper focuses on the numerical approximation of nabla fractional order systems with the conditions of nonzero initial instant and nonzero initial state. First, the inverse nabla Laplace transform is developed and the equivalent infinite dimensional frequency distributed models of discrete fractional order system are introduced. Then, resorting the nabla Laplace transform, the rationality of the finite dimensional frequency distributed model approaching the infinite one is illuminated. Based on this, an original algorithm to estimate the parameters of the approximate model is proposed with the help of vector fitting method. Additionally, the applicable object is extended from a sum operator to a general system. Three numerical examples are performed to illustrate the applicability and flexibility of the introduced methodology.
TL;DR: In this article, the authors present new stabilization conditions for discrete-time linear parameter-varying systems in the form of linear matrix inequalities, using Lyapunov functions with dependence.
Abstract: This paper presents new stabilization conditions for discrete‐time linear parameter‐varying systems in the form of linear matrix inequalities. The use of Lyapunov functions with dependence...
TL;DR: The proposed neural network controller has excellent performance of trolley position tracking and payload anti‐sway controlling and the robustness of the proposed controller is proved by the Lyapunov stability theory.
Abstract: Asian J Control. 2019;1–13. Abstract Crane systems have been widely applied in logistics due to their efficiency of transportation. The parameters of a crane system may vary from each transport, therefore the anti‐sway controller should be designed to be insensitive to the variation of system parameters. In this paper, we focus on pure neural network adaptive tracking controller design issue that does not require the parameters of crane systems, i.e. the trolley mass, the payload mass, the cable lengths, and etc. The proposed neural network controller only requires the output feedback signals of the trolley, i.e. the position and the velocity, which means no sway measuring equipment is needed. The Lyapunov method is utilized to design the weights update law of neural network, and the robustness of the proposed controller is proved by the Lyapunov stability theory. The results of numerical simulations show that the proposed neural network controller has excellent performance of trolley position tracking and payload anti‐sway controlling.
TL;DR: In order to increase the accuracy of vehicle detection and tracking, a new clustering algorithm is proposed to obtain vehicle candidates from preprocessed point cloud data collected by the LiDAR.