In this paper,iterative learning control(ILC) is applied to network-based control problems in which communication channels are subject to random transport delay and data *** averaging ILC algorithm is used to overcome...
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In this paper,iterative learning control(ILC) is applied to network-based control problems in which communication channels are subject to random transport delay and data *** averaging ILC algorithm is used to overcome the random *** analysis,it is shown that ILC can perform well and achieve asymptotical convergence in ensemble average along the iteration axis,as far as the probability of the transmission delay and data dropout are known a priori.A unique contribution in this work is to illustrate the applicability of ILC to nonlinear systems while both the one-step delay and the data-dropout phenomena are taken into *** analysis and simulations validate the effectiveness of the ILC algorithm for network-based control tasks.
We address robust stabilization problem for networked controlsystems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Ly...
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ISBN:
(纸本)9781424445233
We address robust stabilization problem for networked controlsystems with nonlinear uncertainties and packet losses by modelling such systems as a class of uncertain switched systems. Based on theories on switched Lyapunov functions, we derive the robustly stabilizing conditions for state feedback stabilization and design packet-loss dependent controllers by solving some matrix inequalities. A numerical example and some simulations are worked out to demonstrate the effectiveness of the proposed design method.
A partial feedback control scheme with a probabilistic fuzzy estimator (PFE) is presented for the robust control of quantum systems. In this scheme, a probabilistic fuzzy simulator is trained to estimate the quantum s...
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ISBN:
(纸本)9781424438716
A partial feedback control scheme with a probabilistic fuzzy estimator (PFE) is presented for the robust control of quantum systems. In this scheme, a probabilistic fuzzy simulator is trained to estimate the quantum states for feedback control of quantum systems. Usually, the estimated state is fed back to design a controller. However, when the estimated quantum state is an almost-eigenstate, a projective measurement will be triggered for the quantum system and the measurement results will be fed back to construct the controller and regulate the fuzzy estimator. This scheme is a partial feedback strategy with controlled discontinuous measurement, where the quantum measurement serves as a control tool and is helpful for driving the quantum system to a desired state tracking even in the presence of unknown disturbances and stochastic noises. An example of a two-spin-¿ system is also presented to demonstrate the proposed approach.
This paper proposes a hierarchical formation stabilization method for vehicles having nonlinear dynamics. Supposing that the formation control problem is already solved for the case of linear vehicle dynamics, the met...
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In this paper the robustness analysis of the hierarchical formation stabilization control proposed by [7] is performed. The analysis is based on the nonlinear small gain theorem and exploits the strict passivity of th...
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A qualitative control method using reinforcement learning (RL) and grey system is developed for mobile robot navigation in an unknown environment. New representation and computation mechanisms are key approaches for l...
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As the technology of sensor and actuator production has rapidly developed in recent years and the computation power of embedded processors has increased, unmanned aerial vehicles (UAVs) have become attractive means of...
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ISBN:
(纸本)9789633130117
As the technology of sensor and actuator production has rapidly developed in recent years and the computation power of embedded processors has increased, unmanned aerial vehicles (UAVs) have become attractive means of testing complex control algorithms. Apart from the development of new controlcontrol algorithms, it is crucial how they can be implemented and tested on the target platforms before flights. This article presents two methods that are suitable for rapid prototype design, test and efficient code generation. One of them provides a graphical interface and automatic code generation, which reduces me time required to develop a control algorithm. The other makes use of a highly configurable real-time operating system specially designed for embedded systems. The two approaches are illustrated by case studies, namely, the implementation of the control system of an autonomous aeroplane and a quadrotor helicopter.
In this paper, the property of practical input-to-state stability and its application to stability of cascaded nonlinear systems are investigated in the stochastic framework. Firstly, the notion of （practical） stoch...
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In this paper, the property of practical input-to-state stability and its application to stability of cascaded nonlinear systems are investigated in the stochastic framework. Firstly, the notion of （practical） stochastic input-to-state stability with respect to a stochastic input is introduced, and then by the method of changing supply functions, （a） an （practical） SISS-Lyapunov function for the overall system is obtained from the corresponding Lyapunov functions for cascaded （practical） SISS subsystems.
In [1] a new solution is given for the discrete time, LQ optimal, infinite horizon output tracking problem. It applies one step preview of the reference signal originally, but for smooth references it can be used with...
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ISBN:
(纸本)9789633130117
In [1] a new solution is given for the discrete time, LQ optimal, infinite horizon output tracking problem. It applies one step preview of the reference signal originally, but for smooth references it can be used with extrapolation. The properties of the tracker for constant reference signals are examined and an application is presented which uses time varying (ramp) references also in [1]. The tracking error is satisfactorily small for both constant and ramp signals (using either preview or extrapolation). So, the need to examine the properties for time varying references directly arises. This article examines these properties for ramp-type, bounded (l∞), l1 and l2 signals. 1. For ramp-type signals the existence of finite tracking error is examined and proven. 2. For bounded inputs, the boundedness of controlled system states, outputs and tracking error are proven. This means BIBO stability of the controlled system. 3. For l1 and l2 signals the l1 and l2 stability of system states, outputs and tracking error are proven. Results were derived with both one step preview and extrapolation. Here, only the results with extrapolation are published because this is the useful case. Simulations with a simple system are done to compare the derived finite upper bounds (for states, outputs and tracking error) with simulation results. All these results prove that the suggested method is useful in trajectory tracking control, but unfortunately its optimality was lost during the derivation. So, in the form given in [1] it is not LQ optimal even for constant references. This means that it should be further developed to achieve optimality.
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