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Iterative learning control (ILC) is an important method for networked control systems and it uses the control information several times before to form the current control input signal [1]. Nowadays, ILC has developed maturely, but its output signal may cannot track the desired trajectory accurately because of the channel noise of wireless transmission. In this paper, we introduce the Adaptive Fourier...
Based on the concept of persistent excitation (PE), a deterministic learning algorithm is proposed for neural network (NN)-based identification of nonlinear systems recently. This paper investigates the quantitative relationship between the PE levels (including the level of excitation), the architectures of NNs and the convergence properties of deterministic learning, which is motivated by a practical...
This paper studies a load transportation problem using two and three quadcopters for outdoor applications. We construct a transportation controller, that consists of a formation controller to track a desired trajectory and a load sharing controller to share the load effects, in a distributed way. Specifically, based on the displacement-based formation and consensus approaches, the formation and load...
Stability analysis of nonlinear systems based on Lyapunov densities has been receiving a considerable amount of attention after the original work of Rantzer for almost attraction of nonlinear time-invariant systems. Though a stability criterion has been proposed also for nonlinear time-varying systems, the assumption of local stability of the equilibrium is needed in the previous result. The purpose...
This work present a collaborative indoor localization system which provides a lightweight location estimation solution for resource constrained IoT devices. The proposed EcoLoc, an encounterbased collaborative indoor localization system, uses the chance of encounter to enable the sharing and composition of multiple trajectories which are generated by Pedestrian Dead Reckoning. A collaborative version...
This paper discusses the learning of robot point-to-point motions via non-linear dynamical systems and Gaussian Mixture Regression (GMR). The novelty of the proposed approach consists in guaranteeing the stability of a learned dynamical system via Contraction theory. A contraction analysis is performed to derive sufficient conditions for the global stability of a dynamical system represented by GMR...
In the case of particle swarm optimization, this paper mainly analyzes and discusses the mathematical model and the analysis on searching for the global optimum region. Firstly, the global optimum region Θ is defined and calculated in the convergence step and the divergence step. Furthermore, the rate μ of locating into the global optimum region is mathematically related to the number of particles,...
We propose a real-time near-optimal motion and trajectory planner using an interior-point method (IPM) based simultaneous dynamic optimization approach applicable to automatic parallel parking. Partitioning an automatic parking zone into two areas with and without inequality constraints drastically improves the convergence efficiency of the IPM-based simultaneous dynamic optimization of nonlinear...
Heel-toe walking can increase the stride length within the same joint limit of the robot. This paper proposes a method to compute the foot angle for heel-toe walking. In heel-toe walking, the value of the foot angle influences the movement of the legs, so it is proposed to select the foot angle to minimize the movement of the legs. The changes in stride and movement of leg with or without the heel-toe...
In this paper, we present a new adaptive iterative learning controller for a class of discrete-time nonlinear systems with iteration-varying uncertainties including initial tracking error, system parameters and external disturbance. The learning objective is to control the nonlinear system to track an iteration-varying desired trajectory after suitable numbers of learning iterations. The main challenge...
Iterative learning control demands the same initial state in each iteration, which is equal to the desired state. But this condition is unattainable in practice. This paper addresses the problem of some fixed initial state in iterative learning control for high-order nonlinear system. It presents a new control algorithm. In the process of tracking, this algorithm can rectify the initial errors through...
In this paper, sampled-data repetitive learning control of electro-hydraulic actuator used in lower limb exoskeleton for rehabilitation is addressed. The controller is proposed entirely in the discrete-time domain. First, the discrete-time model of the electro-hydraulic actuator is derived by direct discretization using Euler approximation, where the equivalent mass and force acted on the electro-hydraulic...
The paper exploits the convergence characteristics of the first- and second-order PI-type iterative learning control (ILC) schemes for linear time-invariant (LTI) systems with direct-through terms. The aim is to investigate the effects of the integration embedments into the conventional P-type ILC rule. In the exploitation, the tracking errors are assessed in the form of the Lebesgue-p norm and the...
This paper investigates a high-order PDα — type iterative learning control strategy for a class of fractional-order linear time-invariant systems with Caputo derivative 0<α<1. On the basis of fractional integration by parts and generalized Young inequality, sufficient convergence condition of the learning control law is established in the sense of Lebesgue-p norm. It is shown that the convergence...
In this paper, we present an iterative learning control (ILC) frameworks for robust path-tracking problem of nonholonomic mobile robots in the presence of initial shifts. The major difficulties are owning to the simultaneous existence of state disturbances and biased initial state in the mobile robot kinematic system. To design ILC strategy for such problem, a new ILC scheme is proposed as a combination...
In this paper, the problem of iterative learning control is considered for a class of switched singular systems. And the considered switched singular systems with arbitrary switching rules are operated in a fixed time interval repetitively. Based on the singular value decomposition method, the switched singular systems are transformed into the switched differential-algebraic systems. Then an iterative...
In many practical applications, the states, inputs and outputs of the systems show 2-dimensional (2-D) property and operate in a repetitive mode. A PID-type iterative learning control (ILC) is designed in this paper for 2-D system which can be described as a Roesser model and operates in a repetitive mode. The convergence conditions of the control algorithm are derived. In order to demonstrate the...
Recent studies pay attention to the similarity between black-box optimization problem and reinforcement learning (RL) problem. They expect that black-box optimization algorithm can solve RL problems. Among the black-box optimization algorithms, extremum seeking (ES) is a notable one. Two past studies implicitly solved RL problem using ES, but their problem settings were limited to linear deterministic...
This paper considers a nonsingular terminal sliding mode control problem for the attitude tracking of a quadrotor. To solve the chattering problem of sliding mode control, a novel terminal sliding mode controller based on a second-order sliding mode algorithm known as super-twisting algorithm is proposed. With the help of the proposed terminal sliding mode controller, the pitch, roll and yaw angles...
A problem of terminal control with linear dynamics on a finite time interval is considered. The right-hand end of trajectory is defined implicitly as a solution of boundary-value problem. The problem is reduced to finding a saddle point of the Lagrange function, and linear dynamics is regarded as an equality-type constraint. Dual extraproximal iterative method for solving the problem was proposed,...
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