Volume 12, Number 2, 2015
Special Issue on Advances in Nonlinear Dynamics and Control (pp.117-155)
This paper is dealing with the problem of tracking control for uncertain flexible joint manipulator robots driven by brushless direct current motor (BDCM). Flexibility of joint in the manipulator constitutes one of the most important sources of uncertainties. In order to achieve high performance, all parts of the manipulator including actuator have been modeled. To cancel the tracking error, a hysteresis current controller and speed controllers have been developed. To evaluate the effectiveness of speed controllers, a comparative study between proportional integral (PI) and sliding mode controllers has been performed. Finally, simulation results carried out in the Matlab simulink environment demonstrate the high precision of sliding mode controller compared with PI controller in the presence of uncertainties of joint flexibility.
This paper proposes a new method for control of continuous large-scale systems where the measures and control functions are distributed on calculating members which can be shared with other applications and connected to digital network communications. At first, the nonlinear large-scale system is described by a Takagi-Sugeno (TS) fuzzy model. After that, by using a fuzzy Lyapunov-Krasovskii functional, sufficient conditions of asymptotic stability of the behavior of the decentralized networked control system (DNCS), are developed in terms of linear matrix inequalities (LMIs). Finally, to illustrate the proposed approach, a numerical example and simulation results are presented.
In this paper, first-order and second-order sliding mode controllers for underactuated manipulators are proposed. Sliding mode control (SMC) is considered as an effective tool in different studies for control systems. However, the associated chattering phenomenon degrades the system performance. To overcome this phenomenon and track a desired trajectory, a twisting, a super-twisting and a modified super-twisting algorithms are presented respectively. The stability analysis is performed using a Lyapunov function for the proposed controllers. Further, the four different controllers are compared with each other. As an illustration, an example of an inverted pendulum is considered. Simulation results are given to demonstrate the effectiveness of the proposed approaches.
This paper presents an adaptive terminal sliding mode control (ATSMC) method for automatic train operation. The criterion for the design is keeping high-precision tracking with relatively less adjustment of the control input. The ATSMC structure is designed by considering the nonlinear characteristics of the dynamic model and the parametric uncertainties of the train operation in real time. A nonsingular terminal sliding mode control is employed to make the system quickly reach a stable state within a finite time, which makes the control input less adjust to guarantee the riding comfort. An adaptive mechanism is used to estimate controller parameters to get rid of the need of the prior knowledge about the bounds of system uncertainty. Simulations are presented to demonstrate the effectiveness of the proposed controller, which has robust performance to deal with the external disturbance and system parametric uncertainties. Thereby, the system guarantees the train operation to be accurate and comfortable.
This paper presents a backstepping control method for speed sensorless permanent magnet synchronous motor based on slide model observer. First, a comprehensive dynamical model of the permanent magnet synchronous motor (PMSM) in d-q frame and its space-state equation are established. The slide model control method is used to estimate the electromotive force of PMSM under static frame, while the position of rotor and its actual speed are estimated by using phase loop lock (PLL) method. Next, using Lyapunov stability theorem, the asymptotical stability condition of the slide model observer is presented. Furthermore, based on the backstepping control theory, the PMSM rotor speed and current tracking backstepping controllers are designed, because such controllers display excellent speed tracking and anti-disturbance performance. Finally, Matlab simulation results show that the slide model observer can not only estimate the rotor position and speed of the PMSM accurately, but also ensure the asymptotical stability of the system and effective adjustment of rotor speed and current.
In this paper, a novel real time non-linear model predictive controller (NMPC) for a multi-variable coupled tank system (CTS) is designed. CTSs are highly non-linear and can be found in many industrial process applications. The involvement of multi-input multi-output (MIMO) system makes the design of an effective controller a challenging task. MIMO systems have inherent couplings, interactions in-between the process input-output variables and generally have an complex internal structure. The aim of this paper is to design, simulate, and implement a novel real time constrained NMPC for a multi-variable CTS with the aid of intelligent system techniques. There are two major formidable challenges hindering the success of the implementation of a NMPC strategy in the MIMO case. The first is the difficulty of obtaining a good non-linear model by training a non-convex complex network to avoid being trapped in a local minimum solution. The second is the online real time optimisation (RTO) of the manipulated variable at every sampling time. A novel wavelet neural network (WNN) with high predicting precision and time-frequency localisation characteristic was selected for an MIMO model and a fast stochastic wavelet gradient algorithm was used for initial training of the network. Furthermore, a genetic algorithm was used to obtain the optimised parameters of the WNN as well as the RTO during the NMPC strategy. The proposed strategy performed well in both simulation and real time on an MIMO CTS. The results indicated that WNN provided better trajectory regulation with less mean-squared-error and average control energy compared to an artificial neural network. It is also shown that the WNN is more robust during abnormal operating conditions.
Constrained reentry trajectory optimization for hypersonic vehicles is a challenging job. In particular, this problem becomes more difficult when several objectives with preemptive priorities are expected for different purposes. In this paper, a fuzzy satisfactory goal programming method is proposed to solve the multi-objective reentry trajectory optimization problem. Firstly, direct collocation approach is used to discretize the reentry trajectory optimal-control problem with nonlinear constraints into nonlinear multi-objective programming problem with preemptive priorities, where attack angles and bank angles at nodes and collocation nodes are selected as control variables. Secondly, the preemptive priorities are transformed into the relaxed order of satisfactory degrees according to the principle that the objective with higher priority has higher satisfactory degree. Then the fuzzy satisfactory goal programming model is proposed. The balance between optimization and priorities is realized by regulating parameter λ, such that the satisfactory reentry trajectory can be acquired. The simulation demonstrates that the proposed method is effective for the multi-objective reentry trajectory optimization of hypersonic vehicles.
In this paper, the problem of controlling chaos in a Sprott E system with distributed delay feedback is considered. By analyzing the associated characteristic transcendental equation, we focus on the local stability and Hopf bifurcation nature of the Sprott E system with distributed delay feedback. Some explicit formulae for determining the stability and the direction of the Hopf bifurcation periodic solutions are derived by using the normal form theory and center manifold theory. Numerical simulations for justifying the theoretical analysis are provided.
This paper derives the bounded real lemmas corresponding to L∞ norm and H∞ norm (L-BR and H-BR) of fractional order systems. The lemmas reduce the original computations of norms into linear matrix inequality (LMI) problems, which can be performed in a computationally efficient fashion. This convex relaxation is enlightened from the generalized Kalman-Yakubovich-Popov (KYP) lemma and brings no conservatism to the L-BR. Meanwhile, an H-BR is developed similarly but with some conservatism. However, it can test the system stability automatically in addition to the norm computation, which is of fundamental importance for system analysis. From this advantage, we further address the synthesis problem of H∞ control for fractional order systems in the form of LMI. Three illustrative examples are given to show the effectiveness of our methods.
Life science research aims to continuously improve the quality and standard of human life. One of the major challenges in this area is to maintain food safety and security. A number of image processing techniques have been used to investigate the quality of food products. In this paper, we propose a new algorithm to effectively segment connected grains so that each of them can be inspected in a later processing stage. One family of the existing segmentation methods is based on the idea of watersheding, and it has shown promising results in practice. However, due to the over-segmentation issue, this technique has experienced poor performance in various applications, such as inhomogeneous background and connected targets. To solve this problem, we present a combination of two classical techniques to handle this issue. In the first step, a mean shift filter is used to eliminate the inhomogeneous background, where entropy is used to be a converging criterion. Secondly, a color gradient algorithm is used in order to detect the most significant edges, and a marked watershed transform is applied to segment cluttered objects out of the previous processing stages. The proposed framework is capable of compromising among execution time, usability, efficiency and segmentation outcome in analyzing ring die pellets. The experimental results demonstrate that the proposed approach is effectiveness and robust.
In the last few years, cloud computing as a new computing paradigm has gone through significant development, but it is also facing many problems. One of them is the cloud service selection problem. As increasingly boosting cloud services are offered through the internet and some of them may be not reliable or even malicious, how to select trustworthy cloud services for cloud users is a big challenge. In this paper, we propose a multi-dimensional trust-aware cloud service selection mechanism based on evidential reasoning (ER) approach that integrates both perception-based trust value and reputation based trust value, which are derived from direct and indirect trust evidence respectively, to identify trustworthy services. Here, multi-dimensional trust evidence, which reflects the trustworthiness of cloud services from different aspects, is elicited in the form of historical users' feedback ratings. Then, the ER approach is applied to aggregate the multi-dimensional trust ratings to obtain the real-time trust value and select the most trustworthy cloud service of certain type for the active users. Finally, the fresh feedback from the active users will update the trust evidence for other service users in the future.
Recently in the area of biological manufacturing and rapid prototyping manufacturing, the bone scaffolds based on the additive manufacturing in repairing bone defects have been paid more and more attention. In the process of preparation, path planning directly affects the structure, performance as well as the final bone cell culture conditions. Due to the special natural bone scaffold structural characteristic, the traditional rapid prototyping (RP) path planning is not fully suitable for the preparation of bone scaffolds. In this paper, based on the 3D printing extrusion forming technology, a method of path planning for osteochondral integrated scaffolds with gradient structure is put forward, which provides a theoretical basis for bone-scaffold modeling and practical preparation. The implementation of the path planning processing system makes it possible to process data automatically from the initial stereo lithography (STL) model of the actual bone defect part by computer X-ray tomography technique (CT) scan or modeling, to generate the path code and to generate the final machining information after post-processing. This work provides some guidelines for independent research and development of automation equipment for biological manufacturing preparation and software technology. The experiment and test results have verified the validity of the path planning method and the good properties of the bone scaffolds with gradient structures.