Estimation and Direct Adaptive Control of Delay-Differential Systems

Estimation and direct adaptive control of delay-differential systems

The force tracking error is dependent on both the position tracking error and the estimated parameter error so that, in constrained adaptive robot control, the convergence of the estimated parameter error becomes more important than in unconstrained adaptive robot control. However, in the direct adaptive controller, the parameter adaptation is driven only by the tracking error in the joint motion, while in the indirect adaptive controller, the parameter estimation is driven only by the prediction error in the joint torque. Based on this observation a new combined direct and indirect adaptive controller driven by both the tracking error and the prediction error is developed for constrained robots with inertia parameter uncertainty. Stability of the combined adaptive controllers is established in the Lyapunov sense. The combined control improves both position and force tracking performance compared with direct adaptive control. The computer simulations are presented to demonstrate the proposed schemes.

Stable Adaptive Predictive Control System Design via Adaptive Output Predictor for Multi-rate Sampled Systems

Event-triggered model-free adaptive predictive control for multi-area power systems under deception attacks

An adaptive integral plus states neural control of aerobic continuous stirred tank reactor

This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.

A parameterization for a linear system is presented to design a direct model reference adaptive pole placement controller. This parameterized model is one of the structured nonminimal models. The exponentially weighted least-squares algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference model.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Robust and adaptive control of an unknown plant: A benchmark of new format

This paper is concerned with application of a game-theoretic formulation for direct adaptive optimal control of nonlinear systems affected by bounded noise/disturbances. Almost all of the reinforcement learning (RL) approaches employ a Markov decision process (MDP) setting, which is inadequate for handling noise/disturbances as it disallows an explicit representation of noise/disturbances. The work in this paper addresses this problem by showing how a game theoretic RL formulation can effectively cope with disturbances with no preliminary offline learning. The work demonstrates the application of neural networks (NN) in a Markov game setup. We provide empirical results of applying the approach on an inverted pendulum swing-up task and compare its performance against Q learning. The results bring out the superiority of the Markov game formulation for direct adaptive optimal control over the MDP setting. Further, the results demonstrate that NN could be used as an effective tool for speeding up learning in large Markov games and for extending the game theoretic direct adaptive optimal control to a continuous state space.

In this paper, we propose a direct adaptive iterative learning control for a class of nonaffine nonlinear discrete-time systems with unknown control direction. The fuzzy neural network is firstly used as approximator to compensate for the unknown certainty equivalent controller. Then, in order to solve the uncertainties from approximation errors and random input disturbances, a dead zone like auxiliary error with a time-varying boundary layer is introduced. The auxiliary error is designed for the construction of adaptive laws and the time-varying boundary layer is applied as a bounding parameter. Based on a Lyapunov like analysis, it is shown that the internal signals are bounded and the norm of output tracking error will asymptotically converge to a residual set which is bounded by the width of the boundary layer. Finally, a simulation example is given to verify effectiveness of the proposed direct adaptive iterative learning control.

The problem of direct adaptive neural control for a class of nonlinear systems with an unknown gain sign and nonlinear uncertainty is discussed in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks (MNNs), and using Nussbaum-type function, a novel design scheme of direct adaptive neural control is proposed. By adopting the adaptive compensation term of the upper bound function of the sum of residual and approximation error, the closed-loop control system is shown to be globally stable, with tracking error converging to zero. Simulation results show the effectiveness of the proposed approach.

The problem of direct model reference adaptive control for multi input multi output (MIMO) uncertain plants with unknown high frequency gain matrix (HFGM) is addressed in the paper. Two schemes of adaptive control algorithms using augmented error estimators and relaxed assumption concerning HFGM are proposed. It is assumed that the minimum of modulus of HFGM determinant is known. The first scheme involves the resettable approximation of the inversion of HFGM estimate in control law. The second scheme is based on modification of conventional augmented error estimator by introduction of a switched projection mechanism. The proposed theoretical ideas are wrapped up by simulation examples.

This paper is concerned with the problem of adaptive neural control for uncertain nonlinear strict-feedback time-delay systems with unknown virtual control coefficients. Radial basis function (RBF) neural networks are employed to directly approximate unknown virtual control signals, and then the adaptive neural control law is constructed by Lyapunov-Krasovskii functionals and backstepping. In order to avoid encountering a large number of adaptive parameters when using RBF neural networks as function approximators, an unknown constant, instead of unknown neural weights themselves, is employed as the estimated parameter. This technique makes only one adaptive parameter tuned online, thus significantly alleviating the burdensome computation. Meanwhile, some continuous functions are introduced to overcome the design difficulty originating from the use of one adaptive parameter. The proposed adaptive control guarantees the boundedness of all the signals in the closed-loop system. Simulation studies are presented to illustrate the effectiveness of the scheme.

Intelligent switching adaptive control for uncertain nonlinear dynamical systems

The inevitable presence of non-ideal communication conditions in open wireless communication networks leads to insufficient coordination and optimization among mixed traffic flows during cloud-based centralized cooperative scheduling and control in intersection systems, thereby compromising traffic safety and operational efficiency. This situation imposes heightened requirements for coordinated operation between mixed vehicle platoons composed of conventional Human-Driven Vehicles (HDVs) and Intelligent Connected Vehicles (ICVs) under imperfect communication conditions. To address these challenges, this paper proposes a multi-vehicle model-free adaptive prediction and cooperative control method for intersection scenarios that considers time-varying delays and false data injection attacks. By employing a compact-form dynamic linearization prediction scheme and an enhanced model-free receding horizon predictive control technique, we construct a networked adaptive prediction and cooperative controller for mixed vehicle clusters. This framework enables the estimation and compensation of multi-vehicle state information within mixed vehicle groups, followed by the distribution of calculated adjustment speeds to target controlled ICVs. The proposed approach guides mixed vehicle platoons to achieve dynamic formation adjustments in response to environmental variations while enhancing coordinated control performance under time-varying delays and false data attacks through regulation of vehicle queue speeds. Simulation results verify that this methodology effectively improves the traffic efficiency at intersections based on safety.