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In this paper, we introduce a new adaptive iterative learning control (AILC) scheme based on a time scaling factor, which enables learning from control tasks with different magnitude and time scales. The proposed AILC scheme overcomes the limitation of traditional ILC that the target trajectory must be identical in all iterations. In addition, the requirement on classic ILC that every trial must repeat...
In this paper, a novel work is presented, where iterative learning control (ILC) approach is applied to a precise speed control approach for a two-link robotic fish. First, by virtue of the Lagrangian mechanics method, we establish a mathematical model for the two-link carangiform robotic fish, which is highly nonlinear and non-affine in control input. According to the structure of the constructed...
This paper provides a novel method towards imparting agility to the anguilliform robotic fish. The anguilliform robotic fishes are idealized to the real fishes using a series of N connected links to be closely approximated to their biological counterparts. To generate robot motion, torque are applied to the N-1 joints connecting any two consecutive links. There is high amount of interest among researchers...
This work addresses the multi-agent consensus tracking problem by iterative learning control (ILC) with input sharing. In many ILC works for multi-agent coordination problem, each agent maintains its own input learning, and the input signal is corrected by local measurements over iteration domain. If the agents are allowed to share their learned inputs among them, the strategy can improve the learning...
This work addresses a leader-follower tracking problem by an iterative learning control approach. In the multi-agent setup, a dynamic leader is connected to a few of followers, and the communication among agents is described by a directed graph. In contrast to many existing literature in which the perfect initial condition is imposed, we assume that the initial state is reset to a fixed position that...
This paper addresses the problem of autonomous tracking a set of ground static targets using minimal fixed wing unmanned aerial vehicles(UAVs). Each target is tracked loosely and has its desired revisit interval. Due to the UAV's dynamic constrains of fixed wing, it is modeled as a Dubins car. The motion planing of UAVs is a NP hard problem to find the shortest path, i.e. Dubins path. Because of the...
In this paper, under repeatable operation environment, an iterative learning control (ILC) scheme is applied for multi-agent systems (MAS) to perform consensus tracking, where the underline communication graph is assumed to be fixed and directed. Different from many existing consensus schemes for linear agent dynamics, we consider time-varying nonlinear agent models with non-parametric uncertainties...
This paper addresses an adaptive iterative learning control (AILC) based scheme for multi-agent systems (MAS) consensus tracking under repeatable control environment. The agent dynamics are assumed to be inherently nonlinear with unknown time-varying parameters. The underline communication among followers is fixed and undirected. The leader's trajectory is dynamically changing, and only available...
This paper 1, presents a novel approach for realizing Carangiform fish swimming patterns by a robotic fish. A video recording system is first set up to capture real fish behaviors. From robotic perspective, three basic Carangiform fish swimming patterns, “cruise”, “cruise in turning”, and “C sharp turning”, are extracted. Base on observations, the mapping between the fish action parts (angular displacements)...
In this paper 1, a motion library and associated control strategies for collision-free motion planning are presented for a biomimetic robotic fish. The Anguilliform robotic fish consists of N links and N − 1 joints. Three major gaits of Anguilliform fish—forward moving, backward moving and turning—are investigated. By giving different reference to joint angles, such as adding reversed phase difference,...
In this paper, a high-order internal model (HOIM) based iterative learning control (ILC) scheme for multi-agent system (MAS) formation is studied. The HOIM-based ILC, which is an effective approach to deal with iteratively varying reference trajectories, provides a suitable framework for derivations and analysis of MAS control in general, and formation control in particular. In this work, the connections...
In this paper, the modeling and control of a biomimetic robotic fish is presented. The Anguilliform robotic fish consists of N links and N−1 joints, and the driving forces are the torques applied to the joints. Considering kinematic constraints, Lagrangian formulation is used to obtain the dynamics of the fish model. The computed torque method is applied, which can provide satisfactory tracking responses...
In this paper, we propose a new iterative learning control (ILC) scheme, which is devoted to dealing with unknown parameters that are both time varying and iteration varying. In particular, we consider iteration-varying parameters that are generated by a second-order internal model. By incorporating the internal model into the parametric learning law, the ILC scheme can handle more generic nonlinear...
In this work we focus on iterative learning control (ILC) design for tracking iteration-varying reference trajectories that are generated by high-order internal models (HOIM). An HOIM can be formulated as a polynomial operator between consecutive iterations to describe the changes of desired trajectories in the iteration domain. The classical ILC for tracking iteration-invariant reference trajectories,...
It is a difficult task to create a realistic human animation because of the high complexity of human motion. To address this problem, a new method is presented for producing physically valid motion with example motions. The core of our method is physics-based space-time optimization (PBSO). PBSO introduces physical constraints into conventional space-time optimization and then ensure the physical...
In this paper, a direct learning control method for a class of switched systems is proposed. The objective of direct learning is to generate the desired control profile for a newly switched system without any feedback, even if the system may have uncertainties. This is achieved by exploring the inherent relationship between any two systems before and after a switch. The new method is applicable to...
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