Continuous‐ and discrete‐time fixed‐gain controller designs for the control of vehicle lateral dynamics

In this paper, fixed‐gain feedback linearization controls are presented to stabilize the vehicle lateral dynamics at bifurcation points for both continuous‐time and discrete‐time cases. Based on the assumption of constant driving speed, a second‐order nonlinear lateral dynamics model is adopted for controller design. Via the feedback linearization scheme and the first‐order Taylor series expansion, a time‐invariant feedback linearization control is proposed as a fixed‐gain linear version of the previously proposed nonlinear one. Furthermore, the conventional linear quadratic regulator (LQR) design is applied to facilitate the choice of the fixed‐gain matrix. Refined controls to compensate the model uncertainty and their local stability analysis are provided. Extension of the continuous‐time design results to discrete‐time cases is also addressed. Numerical simulations for an example model demonstrate the effectiveness of the proposed continuous‐time and discrete‐time design results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Target tracking of autonomous robotic vehicles using range‐only measurements: a switched logic‐based control strategy

This paper considers the pursuing or target tracking problem where an autonomous robotic vehicle is required to move towards a maneuvering target using range‐only measurements. We propose a switched logic‐based control strategy to solve the pursuing problem that can be described as comprising a continuous cycle of two distinct phases: (1) the determination of the bearing, and (2) the steering control of the vehicle to follow the direction computed in the previous step while the range is decreasing. We provide guaranteed conditions under which the switched closed‐loop system achieves convergence of the relative distance error to a small neighborhood around zero. Simulation results are presented and discussed.Copyright © 2011 John Wiley & Sons, Ltd.     

Multi‐targets localization and elliptical circumnavigation by multi‐agents using bearing‐only measurements in two‐dimensional space

In order to enable the multi‐agents to elliptically circumnavigate the multi‐targets in more complex environment, we propose a geometric center estimator and an elliptical circumnavigation controller in two‐dimensional space by only employing bearing measurements without knowing the target's position and velocity. The stability of the algorithm is proved for both stationary targets and dynamic targets. Finally, a series of numerical simulations is presented to verify the correctness of the algorithm both in ideal networks and in networks with communication delays.     

Neural Network Based Feedback Linearization Control of an Unmanned Aerial Vehicle

This paper presents a flight control design for an unmanned aerial vehicle (UAV) using a nonlinear autoregressive moving average (NARMA-L2) neural network based feedback linearization and output redefinition technique.The UAV investigated is non- minimum phase.The output redefinition technique is used in such a way that the resulting system to be inverted is a minimum phase system.The NARMA-L2 neural network is trained off-line for forward dynamics of the UAV model with redefined output and is then inverted to force the real output to approximately track a command input.Simulation results show that the proposed approaches have good performance.     

Finite‐time posture stabilization of the unicycle mobile robot using only position information: A discrete‐time sliding mode approach

The posture stabilization of a unicycle mobile robot is useful in executing parking and docking maneuvers. It becomes more advantageous to guarantee the posture stabilization in finite time for a battery operated robot, especially in an application involving multiple robots. This paper addresses the posture stabilization of the unicycle mobile robot in finite time, especially when only the position information is available. We adopt the reaching law approach and design a discrete‐time sliding mode (DSM) controller by finitely discretizing the chained form of the unicycle, using the notion of multirate input sampling. Finite‐time stabilization of the equilibria is achieved by using multirate piecewise continuous inputs. Furthermore, the control inputs are realized using multirate output‐feedback (MROF) technique, wherein the states are estimated using the knowledge of the past fast output samples and immediate past control inputs. The proposed MROF‐DSM strategy avoids undesirable chatter in the system response owing to the nonswitching‐type controller and guarantees the stabilization in finite time. Simulations demonstrate the efficacy of the proposed method.     

Position‐dependent disturbance rejection using spatial‐based adaptive feedback linearization repetitive control

In this paper, we propose a new design of spatial‐based repetitive control for a class of rotary motion systems operating at variable speeds. The open‐loop system in spatial domain is obtained by reformulating a nonlinear time‐invariant system with respect to angular displacement. A two‐degree‐of‐freedom control structure (comprising two control modules) is then proposed to robustly stabilize the open‐loop system and improve the tracking performance. The first control module applies adaptive feedback linearization with projected parametric update and concentrates on robust stabilization of the closed‐loop system. The second control module introduces a spatial‐based repetitive controller cascaded with a loop‐shaping filter, which not only further reduces the tracking error, but also improves parametric adaptation. The overall control system is robust to model uncertainties of the system and capable of rejecting position‐dependent disturbances under varying process speeds. Stability proof for the overall system is given. A design example with simulation is provided to demonstrate the applicability of the proposed design. Copyright © 2008 John Wiley & Sons, Ltd.     

Additive‐state‐decomposition‐based tracking control framework for a class of nonminimum phase systems with measurable nonlinearities and unknown disturbances

This paper aims to propose an additive‐state‐decomposition‐based tracking control framework, based on which the output feedback tracking problem is solved for a class of nonminimum phase systems with measurable nonlinearities and unknown disturbances. This framework is to ‘additively’ decompose the output feedback tracking problem into two more tractable problems, namely an output feedback tracking problem for a linear time invariant system and a state feedback stabilization problem for a nonlinear system. Then, one can design a controller for each problem respectively using existing methods, and these two designed controllers are combined together to achieve the original control goal. The main contribution of the paper lies on the introduction of an additive state decomposition scheme and its implementation to mitigate the design difficulty of the output feedback tracking control problem for nonminimum phase nonlinear systems. To demonstrate the effectiveness, an illustrative example is given. Copyright © 2013 John Wiley & Sons, Ltd.     

Target point‐based path‐following controller for a car‐type vehicle using bounded controls

In this paper, we have studied the control problem of target point‐based path following for car‐type vehicles. This special path‐following task arises from the needs of vision‐based guidance systems, where a given target point located ahead of the vehicle, in the visual range of the camera, must follow a specified path. A solution to this problem is developed through a nonlinear transformation of the path‐following problem into a reference trajectory tracking problem, by modeling the target point as a virtual vehicle. The use of target point complicates the control problem, as the development produces a first‐order nonlinear nonglobally Lipschitz differential equation with finite escape time. This problem is solved by using small control signals. Bounded feedback laws are designed to control the real vehicle's angular acceleration and the virtual vehicle's velocity, to achieve stability. The resulting controller is globally asymptotically stable with respect to the origin, the proof of which is derived from Lyapunov‐based arguments and a bootstrap argument. It is also shown that the use of exponentially convergent observers/differentiators does not affect the stability of the closed‐loop system. The effectiveness of this controller has been illustrated through simulations. Copyright © 2014 John Wiley & Sons, Ltd.     

Using LMIs to optimize robustness of observer‐based state‐feedback for a synchrotron

In this article, a synthesis method for linear systems subject to uncertain time‐varying parameters is proposed. Starting with the given values for the nominal parameters, the first objective is to find a constant state feedback such that the controlled system is stabilized and given dynamic specifications, such as a minimal decay rate, are met. In a second step, the constant feedback is then locally optimized such that the uncertain system parameters are allowed to vary around their nominal point as much as possible, whereas stabilization and dynamic specifications still hold. In addition, the procedure is extended toward observer‐based state feedback. Finally, this approach is applied to a synchrotron example, where the particle beam in longitudinal direction is to be stabilized and the coherent synchrotron frequency as well as the damping rate are uncertain. Copyright © 2013 John Wiley & Sons, Ltd.     

Stabilization of stochastic coupled systems with Markovian switching via feedback control based on discrete‐time state observations

This study investigates the stabilization issue of stochastic coupled systems with Markovian switching via feedback control. A state feedback controller based on the discrete‐time observations is applied for the stabilization purpose. By making use of the graph theory and the Lyapunov method, we establish both Lyapunov‐ and coefficient‐type sufficient criteria to guarantee the stabilization in the sense of stability, and then, we further develop the mean‐square asymptotical stability. In particular, the upper bound of the duration between 2 consecutive state observations is well formulated. Applications to a concrete stabilization problem of stochastic coupled oscillators with Markovian switching and some numerical analyses are presented to illustrate and to demonstrate the easy verifiability, effectivity, and efficiency of our theoretical findings.     

Cloud data analysis using a genetic algorithm‐based job scheduling process

Data analysis plays a major role in different research applications that require a large volume of data. Cloud computing can provide computer processing resources and device‐to‐device data sharing based on user requirements. The main goal of cloud computing is to allow users and enterprise of varying capabilities to store and process data in an efficient way and to access and distribute resources. However, a crucial problem in cloud computing is job scheduling for numerous users. Prior to the implementation of job scheduling, jobs must be categorized according to degree of criticalness, privacy and time required. Based on the experimental results, the combination of tasks was successfully determined by the processor. In heterogeneous multiprocessor systems, customized job scheduling is highly critical for obtaining optimal job performance. In this paper, an evolutionary genetic algorithm was used for obtaining better results in job scheduling, thereby improving performance in the cloud system in this regard. The genetic algorithm‐based job scheduling process introduced minimizes the investment in time through effective allocation of user requests in order to enhance the overall efficiency of the system.