Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

Robust output regulation problem with prescribed performance for nonlinear strict feedback systems

This paper investigates the problem of robust output regulation control with prospected transient property for strict feedback systems.By employing the internal model principle,the robust output regulation problem with a prospected property can be transformed to a robust stabilization problem with a new output constraint.Then,by constructing the speed function and adopting barrier Lyapunov function technique,the dynamic feedback controller can be designed not only to drive error output of the closed-loop system entering into a prescribed performance bound within a given finite time,but also to achieve that the error output converges to zero asymptotically.The effectiveness of the results is illustrated by a simulation example.

Robust Leader-Following Output Regulation of Uncertain Multi-Agent Systems With Time-Varying Delay

In this paper, the robust analysis and design of leader-following output regulation for multi-agent systems described by general linear models is given in presence of timevarying delay and model uncertainty. To this aim, a new regulation protocol for the closed-loop multi-agent system under a directed graph is proposed. An important specification of the proposed protocol is to guarantee the leader-following output regulation for uncertain multi-agent systems with both stable and unstable agents. Since many signals can be approximated by a combination of the stationary and ramp signals, the presented results work for adequate variety of the leaders. The analysis and design conditions are presented in terms of certain matrix inequalities. The method proposed can be used for both stationary and ramp leaders. Simulation results are presented to show the effectiveness of the proposed method.

Adaptive Output Regulation of a Class of Nonlinear Output Feedback Systems With Unknown High Frequency Gain

This paper presents an output feedback design approach based on the adaptive control scheme developed for nonlinearly parameterized systems,to achieve global output regulation for a class of nonlinear systems in output feedback form.We solve the output regulation problem without the knowledge of the sign and the value of the high frequency gain a priori.It is not necessary to have both the limiting assumptions that the exogenous signal co and the unknown parameter ju belong to a prior known compact set and the high frequency gain has a determinate lower and upper bounds.The effectiveness of the proposed algorithm is shown with the help of an example.

Distributed Robust Containment Control of Linear Heterogeneous Multi-Agent Systems:An Output Regulation Approach

In this paper,we consider the robust output containment problem of linear heterogeneous multi-agent systems under fixed directed networks.A distributed dynamic observer based on the leaders’measurable output was designed to estimate a convex combination of the leaders’states.First,for the case of followers with identical state dimensions,distributed dynamic state and output feedback control laws were designed based on the state-coupled item and the internal model compensator to drive the uncertain followers into the leaders’convex hull within the output regulation framework.Subsequently,we extended theoretical results to the case where followers have nonidentical state dimensions.By establishing virtual errors between the dynamic observer and followers,a new distributed dynamic output feedback control law was constructed using only the states of the compensator to solve the robust output containment problem.Finally,two numerical simulations verified the effectiveness of the designed schemes.

New Results in Cooperative Adaptive Optimal Output Regulation

This paper investigates the cooperative adaptive optimal output regulation problem of continuous-time linear multi-agent systems.As the multi-agent system dynamics are uncertain,solving regulator equations and the corresponding algebraic Riccati equations is challenging,especially for high-order systems.In this paper,a novel method is proposed to approximate the solution of regulator equations,i.e.,gradient descent method.It is worth noting that this method obtains gradients through online data rather than model information.A data-driven distributed adaptive suboptimal controller is developed by adaptive dynamic programming,so that each follower can achieve asymptotic tracking and disturbance rejection.Finally,the effectiveness of the proposed control method is validated by simulations.

Cooperative Output Regulation of Heterogeneous Systems over Directed Graphs:A Dynamic Adaptive Event-Triggered Strategy

This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication bandwidth.Given that there exists an exosystem whose state information is not available to all agents,the authors develop distributed adaptive event-triggered observers for the followers based on relative information between neighboring agents.It should be pointed out that,two kinds of time-varying gains are introduced to avoid relying on any global information associated with the network,and dynamic triggering conditions are designed to get rid of continuous communications.On the basis of the designed observers,the authors devise a local controller for each agent.Compared with the existing related works,the main contribution of the current paper is that the cooperative output regulation problem for general directed graphs is solved requiring neither global information nor continuous communications.

Asynchronous Output Regulation Control for Continuous-Time Markovian Jump Systems with Colored-Noise

The output regulation control problem for the Markovian jump systems with colored-noise is proposed in this paper.Since the modes mismatch problem often exists in the Markovian switching system,the authors design a modes asynchronous controller with an internal model form,which can not only deal with external disturbance,but also reduce the influence of stochastic noise on the system,such that the mean square of the regulation error can be made arbitrarily small by turning the designed parameters.Meanwhile,a criterion of the m-th moment noise-to-state exponential stability is presented for continuous-time Markovian jump systems under the asynchronous control.

Sampled-Data Semi-Global Robust Output Regulation for a Class of Nonlinear Systems

This paper investigates the semi-global robust output regulation problem for a class of uncertain nonlinear systems via a sampled-data output feedback control law.What makes the results interesting is that the nonlinearities of the proposed system do not have to satisfy linear growth condition and the uncertain parameters of our system are allowed to belong to some arbitrarily large prescribed compact subset.Two cases are considered.The first case is that the exogenous signal is constant.The second case is that the exogenous signal is time-varying and bounded.For the first case,the authors solve the problem exactly in the sense that the tracking error approaches zero asymptotically.For the second case,the authors solve the problem practically in the sense that the steady-state tracking error can be made arbitrarily small.Finally,an example is given to illustrate the effectiveness of our approach.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

Adaptive output regulation for cyber-physical systems under time-delay attacks

In this paper,we present an output regulation method for unknown cyber-physical systems(CPSs)under time-delay attacks in both the sensor-to-controller(S-C)channel and the controller-to-actuator(C-A)channel.The proposed approach is designed using control inputs and tracking errors which are accessible data.Reinforcement learning is leveraged to update the control gains in real time using policy or value iterations.A thorough stability analysis is conducted and it is found that the proposed controller can sustain the convergence and asymptotic stability even when two channels are attacked.Finally,comparison results with a simulated CPS verify the effectiveness of the proposed output regulation method.

Output Feedback Regulation for 1-D Anti-Stable Wave Equation with External System Disturbance

This paper has studied the output feedback regulation problem for 1-D anti-stable wave equation with distributed disturbance and a given reference signal generated by a finite-dimensional exosystem. We first design an observer for both exosystem and auxiliary PDE system to recover the state. Then we show the well-posedness of the regulator equations and propose an observer-based feedback control law to regulate the tracking error to zero exponentially and keep all the states bounded.

Semi-global weighted output average tracking of discrete-time heterogeneous multi-agent systems subject to input saturation and external disturbances

In this paper,we revisit the semi-global weighted output average tracking problem for a discrete-time multi-agent system subject to input saturation and external disturbances.The multi-agent system consists of multiple heterogeneous linear systems as leader agents and multiple heterogeneous linear systems as follower agents.We design both the state feedback and output feedback control protocols for each follower agent.In particular,a distributed state observer is designed for each follower agent that estimates the state of each leader agent.In the output feedback case,state observer is also designed for each follower agent to estimate its own state.With these estimates,we design low gain-based distributed control protocols,parameterized in a scalar low gain parameter.It is shown that,for any bounded set of the initial conditions,these control protocols cause the follower agents to track the weighted average of the outputs of the leader agents as long as the value of the low gain parameter is tuned sufficiently small.Simulation results illustrate the validity of the theoretical results.

Mean-Square Output Consensus of Heterogeneous Multi-Agent Systems with Multiplicative Noises in Dynamics and Measurements

This paper studies the output consensus problem of heterogeneous linear stochastic multiagent systems with multiplicative noises in system parameters and measurements,where the system noise in each agent is allowed to be different.By employing stochastic output regulation theory and the stochastic Lyapunov function approach,a composite controller embedded with stochastic output regulator equations(SOREs)and a stochastic dynamic compensator is designed to achieve the meansquare output consensus of the multi-agent systems.To implement the consensus algorithm,a sufficient condition for feasible solutions of the SOREs is first established in terms of Lyapunov and Selvester equations.Then the time-varying SOREs are approximated by the Euler-Maruyama method combined with an a-posteriori partial estimation of the increments of the Brownian motion.A numerical example illustrates the theoretical results.

Periodic event-triggered secure consensus for networked mechanical systems under Denial-of-Service attacks

This paper concentrates on the secure consensus problem of networked mechanical/Euler–Lagrange systems.First,a new periodic event-triggered(PET)secure distributed observer is proposed to estimate the leader information.The proposed distributed observer only relies on the PET data from its neighbors,which can significantly reduce the communication and computational burden.More importantly,it is secure in the sense that it can work normally regardless of the Denial-of-Service(DoS)attacks.Second,based on the proposed distributed observer,an adaptive fuzzy control law is proposed for each Euler–Lagrange system.A PET mechanism is integrated into the controller,which can reduce the control update.This is helpful for both energy saving and fault tolerance of actuators.Moreover,the PET mechanism naturally makes the controller easy to be implemented in digital platform.The property of fuzzy logic systems and Gronwall inequality are skillfully utilized to show the stability of the closed-loop system.Finally,the proposed control scheme is verified on real Euler–Lagrange systems,which contain a robot manipulator and several servo motors.

Review and recent development of internal model design

Robust output regulation is a controller design theory for achieving reference tracking and disturbance rejection as well as system stability.The main feature of the theory is that the reference and/or disturbance signals have explicit structural description,especially by an exosystem.By decoding this structural information,the desired steady state/input of the system can be completely characterised,albeit not measurable or implementable due to system uncertainty.The steady state/input description simply copies the exosystem dynamics for simple systems,however,it relies on more complicated steady-state generators in most non-linear scenarios.Once the steady state/input is characterised,it can be estimated by an internal model.Proper construction of internal model can well compensate for the desired steady state/input resulting in a stabilisation problem about an equilibrium point.The control objective is achieved if such a stabilisation problem is solved.This paper does not ambitiously aim at a complete overview picture of the three decade development of output regulation theory.It attempts to show a small trail in the garden of vast collections of output regulation results that may lead the audience from simple to advanced internal model design techniques.In particular,it reviews the simple PID structure,the p-copy internal model for linear systems,various internal models for non-linear systems,and the recent development of generalised internal model to accommodate adaptive scheme.

A demand side controller of electrolytic aluminum industrial microgrids considering wind power fluctuations

Direct wind power purchase for large industrial users is a meaningful way to improve wind power consumption and decrease industrial production costs.Short-term wind power fluctuations may lead to large-scale wind power curtailment problems.To promote use of wind energy,a demand side control method is proposed based on output regulator theory for a grid-connected industrial microgrid with electrolytic aluminum loads to continuously track and respond to wind power fluctuations.The control model of the EALs and the dominant frequencies of the wind power fluctuation signals are analyzed and incorporated into the demand side control plant.The feedback control signals with active power deviations on the tie-line are used to design the demand side controller.Simulations are conducted for an actual industrial microgrid to validate the feasibility and effectiveness of the proposed method.The results demonstrate that the proposed controller based on output regulator theory is able to effectively track wind power fluctuations.