In this paper, we propose a new asymptotically stabilizing control law for a four-wheeled vehicle with a steering limitation. We adopt a locally semiconcave control Lyapunov function (LS-CLF) for the system. To overcome the nonconvexity of the input-constraint set, we utilize a saturation function and a signum function in the control law. The signum function makes the vehicle velocity nonzero except at the origin so that the angular velocity can be manipulated within the input constraint. However, the signum function may cause a chattering phenomenon at certain points of the state far from the origin. Thus, we integrate a lazy-switching mechanism for the vehicle velocity into the control law. The mechanism makes a sign of the vehicle velocity maintain, and the new control input also decreases the value of the LS-CLF. We confirm the effectiveness of our method by a computer simulation and experiments.

This paper aims to propose a Fallback Control System isolated from cyber-attacks against networked control systems. The fallback control system implements maintaining functionality after an incident. Since cyber-attacks tamper with the communication contents of the networked control systems, the fallback control system is installed in a control target side. The fallback control system detects the incident without the communication contents on field network. This system detects an incident based on a bilinear observer and a switched Lyapunov function. When an incident is detected, normal operation is switched to fallback operation automatically. In this paper, a practical experiment with Ball-Sorter simulating a simple defective discriminator as a part of Factory Automation systems is shown. Assumed cyber-attacks against Ball-Sorter are Man In The Middle attack and Denial of Service attack.

In this paper, a stochastic asymptotic stabilization method is proposed for deterministic input-affine control systems, which are randomized by including Gaussian white noises in control inputs. The sufficient condition is derived for the diffusion coefficients so that there exist stochastic control Lyapunov functions for the systems. To illustrate the usefulness of the sufficient condition, the authors propose the stochastic continuous feedback law, which makes the origin of the Brockett integrator become globally asymptotically stable in probability.

This paper presents the stability analysis for continuous-time Takagi-Sugeno fuzzy systems using a fuzzy Lyapunov function. The proposed fuzzy Lyapunov function involves the time derivatives of states to include new free matrices in the LMI stability conditions. These free matrices extend the solution space for Linear Matrix Inequalities (LMIs) problems. Numerical examples illustrate the effectiveness of the proposed methods.

The process of designing analogue circuits is formulated as a controlled dynamic system. For analysis of such system's properties it is suggested to use the concept of Lyapunov's function for a dynamic system. Various forms of Lyapunov's function are suggested. Analyzing the behavior of Lyapunov's function and its first derivative allowed us to determine significant correlation between this function's properties and processor time used to design the circuit. Numerical results prove the possibility of forecasting the behavior of various designing strategies and processor time based on the properties of Lyapunov's function for the process of designing the circuit.

A Lyapunov-based recurrent neural networks unified power flow controller (UPFC) is developed for improving transient stability of power systems. First, a simple UPFC dynamical model, composed of a controllable shunt susceptance on the shunt side and an ideal complex transformer on the series side, is utilized to analyze UPFC dynamical characteristics. Secondly, we study the control configuration of the UPFC with two major blocks: the primary control, and the supplementary control. The primary control is implemented by standard PI techniques when the power system is operated in a normal condition. The supplementary control will be effective only when the power system is subjected by large disturbances. We propose a new Lyapunov-based UPFC controller of the classical single-machine-infinite-bus system for damping enhancement. In order to consider more complicated detailed generator models, we also propose a Lyapunov-based adaptive recurrent neural network controller to deal with such model uncertainties. This controller can be treated as neural network approximations of Lyapunov control actions. In addition, this controller also provides online learning ability to adjust the corresponding weights with the back propagation algorithm built in the hidden layer. The proposed control scheme has been tested on two simple power systems. Simulation results demonstrate that the proposed control strategy is very effective for suppressing power swing even under severe system conditions.

This paper deals with stability analysis of hybrid systems. Such systems are characterized by a combination of continuous dynamics and logic based switching between discrete modes. Lyapunov theory is a well known methodology for the stability analysis of linear and nonlinear systems in control system literature. Construction of Lyapunov functions for hybrid systems is generally a difficult task, but once these functions are defined, stabilization of the system is straight-forward. The sum of squares (SOS) decomposition and semidefinite programming has also provided an efficient methodology for analysis of nonlinear systems. The computational method used in this paper relies on the SOS decomposition of multivariate polynomials. By using SOS, we construct a (some) Lyapunov function(s) for the hybrid system. The reduction techniques provide numerical solution of large-scale instances; otherwise they will be practically unsolvable. The introduced method can be used for hybrid systems with linear or nonlinear vector fields. Some examples are given to demonstrate the capabilities of the proposed approach.

This study introduces the fuzzy Lyapunov function to the fuzzy PID control systems, modified fuzzy systems, with an optimized robust tracking performance. We propose a compound search strategy called conditional linear matrix inequality (CLMI) approach which was composed of the proposed improved random optimal algorithm (IROA) concatenated with the simplex method to solve the linear matrix inequality (LMI) problem. If solutions of a specific system exist, the scheme finds more than one solutions at a time, and these fixed potential solutions and variable PID gains are ready for tracking performance optimization. The effectiveness of the proposed control scheme is demonstrated by the numerical example of a cart-pole system.

This paper studies the problem of the relations between existence conditions of common quadratic and those of common infinity-norm Lyapunov functions for sets of discrete-time linear time-invariant (LTI) systems. Based on the equivalence between the robust stability of a class of time-varying systems and the existence of a common infinity-norm Lyapunov function for the corresponding set of LTI systems, the relations are determined. It turns out that although the relation is an equivalent one for single stable systems, the existence condition of common infinity-norm type is strictly implied by that of common quadratic type for the set of systems. Several existence conditions of a common infinity-norm Lyapunov functions are also presented for the purpose of easy checking.

In this paper, we present new stability conditions for a class of large-scale hybrid dynamical systems composed of a number of interconnected hybrid subsystems. The stability conditions are given in terms of discontinuous Lyapunov functions of the stable hybrid subsystems. Furthermore, the stability conditions are represented by LMIs (Linear Matrix Inequalities) which are computationally tractable.

An extension is made for a set of systems that have a quadratic Lyapunov function in common for the purpose of analysis and design. The nominal set of system matrices comprises stable symmetric matricies, which admit a hyperspherical Lyapunov function. Based on stability robustness results, sets of matrices are constructed so that they share the same Lyapunov function with the nominal ones.