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Critical Infrastructures such as transportation or energy systems are central to the existence of modern societies. Due to the expected level of performance and developments like the shift towards renewable, fluctuating energy generation in Smart Grids, advanced monitoring and control systems are required for stable operation. This in turn increases the dependence on robust communication technology...
In this paper, we address the finite-time tracking consensus control problem for nonlinear multi-agent systems under no-cycle communication graph. Unlike most existing works of finite-time consensus, we focus on nonlinear multi-agent systems with lower triangular subsystems. Based on the local cooperative information among neighboring agents, we propose a tracking consensus protocol ensuring that...
In this paper, to increase the accuracy and the rate of convergence of the algorithm, by employing orthogonal experiment and mutation operation to the traditional Quantum-behaved Particle Swarm Optimization (QPSO), an improved QPSO (IQPSO) has been presented. The optimization criterion, the ITAE (Integral of Time and Absolute Error) tested that the efficiency of IQPSO is superior to the traditional...
Average-consensus filter problem is investigated for a mixed-order multi-agent system, which consists of first-order and second-order agents, and the proportional-integral consensus filter algorithms are proposed for the agents with different constant inputs. Based on generalized Nyquist stability criterion, sufficient convergence conditions are obtained for the multiagent system under a fixed, symmetric...
The principle of the repetitive control circuit is that the control deviation from the previous interval is added to the control signal of the current time interval. This process is carried out continuously and in this way an asymptotic equality of the output signal time course with the control signal can be achieved. But this process is not always successful. Ensuring convergence of such a process...
This paper tackles an aggregate state control problem of multi-agent systems with white noise, where the control objective is that the aggregate state of all agents exponentially converges to zero. For this purpose, networked PI-consensus controllers are employed to facilitate the controller design. An explicit condition of the controller parameters for the aggregate state convergence is derived and...
In this paper, iterative learning control (ILC) based on two-dimensional (2D) system theory for a linear singular discrete-time system is considered. By applying the singular 2D linear Roesser's discrete model to describe the ILC process of the system, necessary and sufficient conditions for impulse-free convergence of the proposed ILC rules are given. Ultimately, two numerical examples are given...
The paper proposes iterative data-driven generalized minimum variance (GMV) regulatory control via L2 — regularization. The proposed approach reformulates the derivation of the GMV regulatory control as a L2-regularized optimization problem, and employs an iterative design approach that repeats the same routine as the one-shot GMV regulatory control via L2-regularization. The L2-regularization assures...
The purpose of the paper is to propose a new control scheme in active front steering (AFS) for vehicle stability using disturbance observer method. In the paper, the ideal yaw rate can be calculated on the linear two-degrees-of-freedom vehicle model. In addition, the actual sideslip angle can also be estimated by using a state observer. Based on this, two kinds of baseline AFS controllers are proposed...
Aiming to increase the convergence speed of perturbation based extremum seeking control, this paper proposed a novel gradient-based perturbation extremum seeking control (GPESC) scheme by combining sine perturbation signal and input-output gradient. In this scheme, the demodulated output signal in classic extremum seeking control (CESC) is replaced with filtered input-output gradient signal to decrease...
The generalization ability of learning algorithms is the focus of machine learning research, where the empirical risk minimization (ERM) plays an important role when the population distribution of observations is unknown. Most of the previous results are mainly based on computational learning theory, which is interested in how many samples are needed to make sure the estimated expected risk satisfies...
In this paper, we will investigate the problem of continuous finite-time disturbance observer (CFTDO) design and its application in the finite-time control (FTC) for systems subject to non-vanishing disturbances. First of all, based on the homogeneous domination approach and state saturation technique, a CFTDO design approach is proposed such that the disturbance can be precisely estimated in finite...
Conventional routing protocols such as RIP, OSPF, EIGRP and BGP have a very rigid and intricate system thus narrowing the adaptability of networks to the ever changing Internet. The emergence of Software Defined Networking (SDN) provides a solution for this problem. Due to the handiness of a centralized controller, SDN has provided an effective method in terms of routing computation and fine control...
In this work an estimation based iterative learning control (ILC) approach for a vehicle suspension rig is presented. Slow convergence rates and non-monotonic learning transients of the typically cross-coupled multi-axial system require a high number of system measurements. This significantly damages the specimen before the actual endurance test. In combination with inverse model ILC the presented...
The paper considers control problems of a group of frontsteering wheels kinematic nonholonomic vehicles with three wheels. The problems of driving the vehicles towards desired rigid and stringlike formations are considered. Special attention is given to the string stability problem and a control algorithm has been established for regulating propagation of disturbances through the vehicle string. Finally,...
This paper is devoted to frequency estimation of a pure sinusoidal signal. The new parameterization approach based on applying delay operators to a measurable signal is proposed. The result is the first order linear regression model with one parameter, which depends on the signal frequency. The estimation algorithm is basing on standard gradient approach. It is shown that the frequency estimation...
Controlling the motion of a group of unmanned flight vehicles (FVs) in a perturbed environment is considered on the example of two similar problems: tracking of the dynamic target and moving along the given path. The problem of the target tracking implies that a randomly-arranged FV group approaches close to the target and flies near it during a specified time period. The low-velocity target seeks...
A problem of closed-loop identification of a linear scalar plant with a bounded disturbance is considered. Tunings of the stabilizing controller are fixed, disturbance independent test signal of a bounded average power additively randomizes the control. For the plant identification, a stochastic version of the randomized Motskin procedure is used to solve systems of linear inequalities. A randomization...
The attitude of the spacecraft is its angular orientation in space. Attitude control design of a satellite system is an important area of space technology. An efficient controller is required to take the satellite system to its target orientation. In some cases, the maneuver need to be completed within a finite time. This paper introduces a novel control law which achieves finite time convergence...
The purpose of this work is to improve the tracking performance of the iterative learning control (ILC) by designing a new learning law that has the ability to update the input along both the time and iterative axes. First, the reference is generated by a high-order internal model (HOIM) along the iterative axis and can be approximated by an HOIM along the time axis. Then, the HOIM-based repetitive...
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