Desired Tracking Accuracy Research Articles

Estimation of control loop bandwidth for desired tracking accuracy in a fast-ramped power converter for electromagnets in particle accelerators

Estimation of control loop bandwidth for desired tracking accuracy in a fast-ramped power converter for electromagnets in particle accelerators

Estimation of control loop bandwidth for desired tracking accuracy in a fast-ramped power converter for electromagnets in particle accelerators.

Estimation of control loop bandwidth for desired tracking accuracy in a fast-ramped power converter for electromagnets in particle accelerators.

Sampled-Data Practical Tracking Control for Nonlinear Time-Delay Systems

This brief proposes a sampled-data practical tracking control scheme for nonlinear time-delay systems. A novel technique lemma is firstly proposed to effectively estimate the trajectory growth between two successive sampling points. The output feedback domination approach, as a powerful tool, is utilized in this brief. After constructing appropriate observer as well as sampled-data controller, the desired tracking accuracy can be realized. Finally, we present an electromechanical system simulation example to verify the effectiveness of the proposed control approach.

Iterativno upravljanje učenjem u ograničenom prostoru stanja robotskog manipulatora sa 3 stepena slobode

In this paper, the trajectory tracking problem of a nonlinear robotic system with 3DOFs under the control signal obtained through nonlinearly constrained state spaceIterative Learning Control (ILC) methods is considered. The focus of this paper is the analysis of different control system parameters on the convergence rate of two constrained state space ILCalgorithms: Bounded Error Algorithm (BEAILC) and Constrained Output algorithm (COILC), as well as the comparison between these two algorithms through simulations. The obtained results have shown that COILC algorithm converges faster than BEAILC algorithm when compared with the same learning and feedback parameters, due to lower trajectory restrictions. Also, it has been shown that an increase in feedback gains can decrease the number of iteration terminations during the learning process, thus allowing for more of the trajectory error information to be learned from during the single iteration. Moreover, simulations have shown that the decrease in learning parameter values will increase the number of iterations required to obtain the desired tracking accuracy.

Stability analysis for nonlinear closed loop system structure

In this paper, stability analysis is studied for the nonlinear closed loop system with a nonlinear plant and a nonlinear controller. Without the linearizing process for the nonlinear plant and the nonlinear controller, a Lipschitz continuous assumption of nonlinear plant is added and the notion of finite gain stability is defined. Then some inequalities about Lipschitz constants are derived to guarantee that the nonlinear system outputs are finite gain stability. To avoid the construction of the Lyapunov function, the convergence properties about the geometric series are applied to obtain two inequality conditions on the defined finite gain stability. Furthermore, one more complicated system, coming from engineering practice, is used to extend the nonlinear closed loop system, where the inner loop is applied to help another two controllers in achieving the desired tracking accuracy. Then the controller design for one flight simulation platform with six degrees of freedom confirms the theoretical results.

Nonmyopic sensor scheduling for target tracking with emission control

SummaryActive sensors obtain the measurements of targets by emitting energy that can be intercepted by enemy surveillance sensors. To satisfy the target tracking requirement and control the whole system emission, we propose a nonmyopic sensor scheduling to minimize the emission cost while maintaining a desired tracking accuracy. The processes of target tracking and emission control are formulated as a partially observable Markov decision process. Then, we translate our scheduling problem to a discrete unconstrained optimization problem, which consists of multistep emission cost and multistep tracking accuracy cost. Furthermore, the cubature Kalman filter is utilized to update the target belief state and predict the multistep tracking accuracy cost, whereas the multistep emission cost is obtained by hidden Markov model filter. Scheduling is implemented efficiently by constructing a decision tree and using a search algorithm, which combines uniform cost search with augmented branch and bound technique. Simulation results demonstrate the effectiveness of our proposed method.

Adaptive Sequential Stochastic Optimization

A framework is introduced for sequentially solving convex stochastic minimization problems, where the objective functions change slowly, in the sense that the distance between successive minimizers is bounded. The minimization problems are solved by sequentially applying a selected optimization algorithm, such as stochastic gradient descent, based on drawing a number of samples in order to carry the iterations. Two tracking criteria are introduced to evaluate approximate minimizer quality: one based on being accurate with respect to the mean trajectory, and the other based on being accurate in high probability. An estimate of a bound on the minimizers’ change, combined with properties of the chosen optimization algorithm, is used to select the number of samples needed to meet the desired tracking criterion. A technique to estimate the change in minimizers is provided along with analysis to show that eventually the estimate upper bounds the change in minimizers. This estimate of the change in minimizers provides sample size selection rules that guarantee that the tracking criterion is met for sufficiently large number of time steps. Simulations are used to confirm that the estimation approach provides the desired tracking accuracy in practice, while being efficient in terms of number of samples used in each time step.

Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system

The choice of the control techniques has a positive impact on the traction chain particularly disturbance rejection ability and the energy management in the electric vehicle (electric motor, inverter, transmission, ect.). In this paper, a robust neuro-fuzzy-sliding mode control (RNFSMC) with extended state observer (ESO) technique is applied on the traction chain of the electric vehicle (Permanent magnet synchronous motor PMSM, Inverter, Transmission). However, most of the existing strategies of control that are applied on the traction chain lead to chattering phenomena, reducing the electric motor performance and disturbance rejection ability without forgetting the bad energy management on board the electric vehicle. To further enhance the performance of the traction chain, a hybrid control scheme is used to severally decrease the chattering phenomena in the PMSM electric motor and evolve the disturbance rejection ability which employs two types of controllers: Neuro-fuzzy sliding mode control on the direct current loop and ESO controller on both speed, and quadrature current loops taking into account the dynamic of the vehicle. Simulations by Matlab/Simulink are used to indicate the validity of the planed scheme on the closed-loop system. The simulation results show the effectiveness of the proposed control strategy with desired tracking accuracy.

Inverse error analysis and adaptive output feedback control of uncertain systems preceded with hysteresis actuators

The development of control approaches for systems preceded with hysteresis non-linearities has received great attentions in recent decades. The most common approach is the construction of an inverse model as the compensator to mitigate hysteresis effects. However, most of the developed schemes are state-based, requiring the availability of states of systems, which may not be the case for some practical systems. In this study, output control with inverse compensation will be addressed. By using the inverse as a feedforward compensator for the model described by the modified generalised Prandtl–Ishlinskii model, an corresponding analytical expression of the inverse compensation error is first obtained. Then, an observer-based robust adaptive output feedback controller is developed. It is shown that the proposed output feedback control scheme can not only guarantee the stability of the control systems, but also can achieve the desired tracking accuracy.

Energy-Balanced Scheduling for Target Tracking in Wireless Sensor Networks

A novel energy-balanced task-scheduling method is proposed that extends the lifespan of wireless sensor networks (WSNs) for collaborative target tracking using an unscented Kalman filter (UKF) algorithm. It is shown that the tracking accuracy is approximately proportional to the number of active sensor nodes participating in collaborative tracking. Excessive sensor nodes thus may be put to sleep mode to conserve energy provided there are a sufficient number of active sensor nodes. It is then shown that the lifespan of a WSN is dictated by the distribution of residue energy of sensor nodes. Specifically, we have shown that an energy-balanced WSN is likely to maximize its lifespan. As such, at each step of the tracking task, the head node must judiciously select active nodes from all sensors within the sensing range to minimize residue energy variations (energy balanced) while achieving desired tracking accuracy. This is formulated as a subset selection problem, which is shown to have a complexity that is NP-hard. Several energy-balanced scheduling for tracking (EBaST) heuristic algorithms are proposed to solve this problem with polynomial execution complexities. Extensive simulations have been conducted to compare EBaST against some state-of-the-art scheduling algorithms. It is observed that EBaST is more capable of significantly extending the WSNs lifespan than competing algorithms while delivering comparable or better tracking accuracy.

Adaptive Inverse Output Feedback Control of Uncertain Systems Preceded with Hysteresis Actuators

The development of control approaches for systems preceded with hysteresis nonlinearities has received great attentions in recent decades. The most common approach is the construction of an inverse model as the compensator to mitigate hysteresis effects. However, most of the developed schemes are state-based, requiring the availability of states of systems, which may not be the case for some practical systems. In this paper, output control with inverse compensation will be addressed. By using the inverse as a feedforward compensator for the model described by the modified generalized Prandtl-Ishlinskii (MGPI) model, an corresponding analytical expression of the inverse compensation error is first obtained. Then, an observer-based robust adaptive output feedback controller is developed. It is shown that the proposed output feedback control scheme can not only guarantee the stability of the control systems, but also can achieve the desired tracking accuracy.

Multivariable norm optimal iterative learning control with auxiliary optimisation

The paper describes a substantial extension of norm optimal iterative learning control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously converging to the solution of a constrained quadratic optimisation problem. The theory is presented in a general functional analytical framework using operators between chosen real Hilbert spaces. This is applied to solve problems in continuous time where tracking is only required at selected intermediate points of the time interval but, simultaneously, the solution is required to minimise a specified quadratic objective function of the input signals and chosen auxiliary (state) variables. Applications to the discrete time case, including the case of multi-rate sampling, are also summarised. The algorithms are motivated by practical need and provide a methodology for reducing undesirable effects such as payload spillage, vibration tendencies and actuator wear whilst maintaining the desired tracking accuracy necessary for task completion. Solutions in terms of NOILC methodologies involving both feedforward and feedback components offer the possibilities of greater robustness than purely feedforward actions. Results describing the inherent robustness of the feedforward implementation are presented and the work is illustrated by experimental results from a robotic manipulator.

Variable Neural Direct Adaptive Robust Control of Uncertain Systems

Direct adaptive robust state and output feedback controllers are proposed for the output tracking control of a class of uncertain systems. The proposed controllers incorporate a variable structure radial basis function (RBF) network to approximate unknown system dynamics, where the RBF network can determine its structure online dynamically. Radial basis functions can be added or removed to ensure the desired tracking accuracy and to prevent the network redundancy simultaneously. The closed-loop systems driven by the direct adaptive robust controllers are characterized by the guaranteed transient and steady-state tracking performance. The performance of the proposed output feedback controller is illustrated with numerical simulations.

A Simple Robust Controller for the Approximate Regulation of Almost Periodic Signals for Linear Systems

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> By using a delay line as a signal generator, we design a simple robust controller for the approximate regulation (i.e., approximate asymptotic tracking and rejection) of almost periodic signals for linear systems. As opposed to the related existing results, our controller design does not require online parameter tuning. Besides the usual assumptions about stabilizability, detectability, and nonexistence of transmission zeros for the plant, the controller design algorithm of this paper only requires knowledge of the desired tracking accuracy <formula formulatype="inline"><tex>$\epsilon&gt;0$</tex></formula>. Based on this information alone, we can only design a robust controller achieving, in the presence of unknown trigonometric polynomial disturbances and additive perturbations to the plant and the controller, <formula formulatype="inline"><tex>$\lim\sup_{t\rightarrow\infty}\Vert y(t)-y_{ref}(t)\Vert≪\epsilon\Vert y_{ref}\Vert$</tex></formula>. Here, <formula formulatype="inline"><tex>$y$</tex></formula> is the output of the plant and <formula formulatype="inline"><tex>$y_{ref}$</tex></formula> is an arbitrary almost periodic reference signal taken from an infinite-dimensional generalized Sobolev space of almost periodic functions. In this paper, we also study robust output regulation in the limiting case where <formula formulatype="inline"><tex>$\epsilon\rightarrow 0$</tex></formula>. It turns out that, due to the general loss of exponential closed loop stability, asymptotic tracking/rejection can also be lost as <formula formulatype="inline"><tex>$\epsilon\rightarrow 0$</tex></formula>, unless the exogenous signals are smooth enough. This last result extends some recent theorems on (nonrobust) open loop output regulation for infinite-dimensional exosystems. The results of this paper are new and potentially useful for finite-dimensional plants, but the main results are also true for infinite-dimensional systems with bounded control and observation operators. </para>

Practical output regulation for bounded linear infinite-dimensional state space systems

We develop the mathematical foundations of practical state space output regulation for bounded infinite-dimensional linear systems. By practical output regulation we mean asymptotic tracking of references and rejection of disturbances with a given accuracy. Our main results are general upper bounds for the norms perturbations to the parameters of the exosystem, the plant and a controller which achieves exact output regulation. These bounds depend explicitly on the desired tracking accuracy ε > 0 . In this paper, all perturbations are assumed to be bounded, additive and linear. Our results apply for both feedforward and error feedback controllers, and for arbitrary bounded uniformly continuous reference/disturbance signals.

Reinforcement Adaptive Fuzzy Control for a Class of Nonlinear Uncertain Systems

The paper describes the application of reinforcement learning techniques to feedback control for a class of nonlinear systems using adaptive fuzzy system. The adaptive fuzzy system approximates the non linear characteristics of the plant on-line to achieve the desired tracking accuracy without any preliminary off-line learning or training phase. The reinforcement signal from a critic is used for finding proper fuzzy rules to approximate the non linear dynamics in the plant. The approximation of the nonlinear dynamics in the plant is performed on-line without knowing any knowledge of the nonlinear terms in the plant, whence comes the term Reinforcement Adaptive Learning (RAL). The fuzzy system based on the RAL algorithm is referred to as Reinforcement Adaptive Fuzzy (RAF) system. The RAL algorithm is derived from Lyapunov stability analysis, so that both tracking stability and error convergence can be guaranteed in the closed-loop system.

Thermal-Noise Errors in Simultaneous-Lobing and Conical-Scan Angle-Tracking Systems

Relationships for rms angular errors are developed for certain common active space probe and satellite angle-tracking systems. The only source of error considered is the thermal and shot noise of the receiver, bandlimited by the tracking servo noise bandwidth. If additional smoothing after angular readout is performed, only the special case of many samples averaged over a time long compared to the reciprocal servo noise bandwidth is considered. These thermal-noise errors are by no means the usual practical accuracy limitations of an angle-tracking system. They do, however, set bounds on minimal signal strength allowable for the desired tracking accuracy. The received signals were assumed to be sinusoidal of constant peak amplitude with the information, if any, contained in phase or frequency modulation. This is the most common signal in space probe or satellite tracking.