Output-feedback Algorithm Research Articles

Output synchronization of a class of complex dynamic networks: A reinforcement learning method

In this paper, to achieve the synchronization control for a class of complex dynamic networks with completely unknown system dynamics, a reinforcement learning output feedback algorithm based on state reconstruction is proposed. Given the high cost and complexity associated with obtaining the full state information, an output-based node state reconstruction method is employed for the first time in complex dynamic networks. The proposed method utilizes a sequence composed of a finite number of output data to reconstruct the current state. At the same time, the overall error system is constructed to handle the coupling relationship between nodes, to facilitate the controller design. Thereafter, considering the system dynamics are unknown, an algorithm based on reinforcement learning is proposed to ensure rapid synchronization of node outputs, and the convergence of proposed method is proven. Finally, the feasibility of proposed algorithm is corroborated through a simulation example and a multi-vehicle system.

Model-free low-power observer based robust trajectory tracking control of UAV quadrotor with unknown disturbances

PurposeThe purpose of this paper is to design an output-feedback algorithm based on low-power observer (LPO), robust chattering-free controller and nonlinear disturbance observer (DO) to achieve trajectory tracking of quadrotor in the Cartesian plane.Design/methodology/approachTo achieve trajectory tracking control, firstly the decoupled rotational and translational model of quadrotor are modified by introducing backstepped state-space variables. In the second step, robust integral sliding mode control is designed based on the proportional-integral-derivative (PID) technique. In the third step, a DO is constructed. In next step, the measurable outputs, i.e. rotational and translational state variables, are used to design the LPO. Finally, in the control algorithm all state variables and its rates are replaced with its estimates obtained using the state-observer.FindingsThe finding includes output-feedback control (OFC) algorithm designed by using a LPO. A modified backstepping model for rotational and rotational systems is developed prior to the design of integral sliding mode control based on PID technique. Unlike traditional high-gain observers (HGO), this paper used the LPO for state estimation of quadrotor systems to solve the problem of peaking phenomenon in HGO. Furthermore, a nonlinear DO is designed such that it attenuates disturbance with unknown magnitude and frequency. Moreover, a chattering reduction criterion has been introduced to solve the inherited chattering issue of controllers based on sliding mode technique.Practical implicationsThis paper presents input and output data-driven model-free control algorithm. That is, only input and output of the quadrotor model are required to achieve the trajectory tracking control. Therefore, for practical implementation, the number of on-board sensor is reduced.Originality/valueAlthough extensive research has been done for designing OFC algorithms for quadrotor, LPO has never been implemented for the rotational and translational state estimations of quadrotor. Furthermore, the mathematical model of rotational and translational systems is modified by using backstepped variables followed by the controller designed using PID and integral sliding mode control technique. Moreover, a DO is developed for attenuation of disturbance with unknown bound, magnitude and frequency.

Efficient Mode Transition Control for DM-PHEV With Mechanical Hysteresis Based on Piecewise Affine H ∞ Strategy

A dual motor plug-in hybrid electric vehicle (DM-PHEVs) can achieve higher power and better fuel economy through Mode Transition Process (MTP) from pure electric to hybrid driving modes. In a DM-PHEV, the MTP is more complex, with more components to be managed. As well as being a combination of a discrete stage transition and a system with continuous state evolution, the several actuators exhibit significant discontinuous dynamics and different characteristics from each other, particularly mechanical hysteresis. This makes the design of a coordinated controller challenging. In this paper, a two-layer coordinated control strategy is proposed. The upper layer is based on a stage-dependent piecewise-affine (PWA) model which is used to develop a PWA-static output feedback H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> strategy (PWA-SOF). The lower layer is based on a simplified actuator lag model, and a H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> design technique is used to develop a robust torque controller that reduces the effect of mechanical hysteresis. The resulting strategy is described as a piecewise-affine modified static output feedback (PW-MSOF) algorithm. (While the individual elements are not novel contributions to control theory, the combination and application to this problem is.) Performance indices are defined and hardware-in-the-loop (HiL) test shows that the new controller can effectively suppress the vehicle jerk without adversely affecting other aspects of system behaviour.

Adaptive bipartite output containment control of heterogeneous multi-agent systems with leaders bounded unknown inputs

This paper researches the adaptive bipartite output containment problem, for a signed graph of heterogeneous multi-agent systems (HMASs). The dynamics of leaders are subject to unknown but bounded inputs, which cannot be measured by followers. Therefore, an containment control protocol based on distributed observer is proposed. An adaptive state and an output feedback algorithm are designed to ensure that cooperative and antagonistic agents enter different convex hull. Then, based on the output regulation approach, Lyapunov stability analysis and some other methods, the bipartite output containment control is realized. Finally, we validate the analytical results by numerical simulations.

Design of Optimal Passive Tuned Mass Damper with Static Output Feedback and Updating Iterative Procedure

In this study, the optimal design issue of a passive tuned mass damper (TMD) was transformed into the nonsparse control gain matrix optimization problem, and a general passive TMD optimization design method to minimize structural mean square responses or impulse response is therefore proposed. The proposed optimization procedure combines the static output feedback (also known as direct output feedback) algorithm and the updating iterative procedure. The proposed method can be applied to variant design scenario, whether the main structure is single-degree-of-freedom (SDOF) or multidegree-of-freedom (MDOF) structures, undamped or damped structures, subjected to wind disturbances or earthquake excitations. In addition, the proposed method is capable to consider the excitation shaping filter, so the design results are more suitable for practical application. The design procedure of the proposed method is presented, and all the required weighting matrices are introduced and derived in detail. Firstly, the SDOF structures are used as the main structure to demonstrate the correctness of the proposed method. The numerical simulation results verify that the obtained optimal design parameters of TMD were found identical to some cases which contain the analytic solution. The accuracy and feasibility of the proposed design method are confirmed. Finally, a passive TMD is optimally designed for a 5-story MDOF structure subjected to Kanai–Tajimi spectrum comparable earthquakes and a 60-story high-rise MDOF structure subjected to Davenport spectrum comparable wind loads for demonstration.

Prescribed-time Closed-loop Performance Assessment: Comparative Case Study

Control loop performance assessment is addressed in the context of the output feedback stabilization in fixed-time. Existing fixed- and prescribed-time output feedback control algorithms are reviewed and their pros and cons are discussed for performing the afore-mentioned task. The control performance assessment is based on integral indices. These indices are computed from the available data obtained from numerical simulations with a simple pendulum system affected by matched disturbances and measurement noise. Simulations results corroborates the pros and cons of the fixed-time output feedback algorithms in question.

Reinforcement Learning Based Optimal Tracking Control Under Unmeasurable Disturbances With Application to HVAC Systems.

This paper presents the design of an optimal controller for solving tracking problems subject to unmeasurable disturbances and unknown system dynamics using reinforcement learning (RL). Many existing RL control methods take disturbance into account by directly measuring it and manipulating it for exploration during the learning process, thereby preventing any disturbance induced bias in the control estimates. However, in most practical scenarios, disturbance is neither measurable nor manipulable. The main contribution of this article is the introduction of a combination of a bias compensation mechanism and the integral action in the Q-learning framework to remove the need to measure or manipulate the disturbance, while preventing disturbance induced bias in the optimal control estimates. A bias compensated Q-learning scheme is presented that learns the disturbance induced bias terms separately from the optimal control parameters and ensures the convergence of the control parameters to the optimal solution even in the presence of unmeasurable disturbances. Both state feedback and output feedback algorithms are developed based on policy iteration (PI) and value iteration (VI) that guarantee the convergence of the tracking error to zero. The feasibility of the design is validated on a practical optimal control application of a heating, ventilating, and air conditioning (HVAC) zone controller.

Adaptive Bipartite Output Consensus for Heterogeneous Multi-Agent Systems via State/Output Feedback Control

This brief discusses bipartite output consensus problem of heterogeneous multi-agent systems (MASs) with a signed graph. First, by applying the output regulation method, an adaptive state feedback algorithm together with an adaptive output feedback algorithm are presented respectively for the heterogeneous systems. The system matrix of the leader is known only by its neighbors, not all followers. Second, some sufficient conditions are acquired to achieve bipartite output consensus based upon graph theory, Lyapunov stability theory and other related methods. Finally, the accuracy of our theory analysis is testified by simulations.

A Deep Reinforcement Learning Framework for Fast Charging of Li-Ion Batteries

One of the most crucial challenges faced by the Li-ion battery community concerns the search for the minimum time charging without damaging the cells. This goal can be achieved by solving a large-scale constrained optimal control problem, which relies on accurate electrochemical models. However, these models are limited by their high computational cost, as well as identifiability and observability issues. As an alternative, simple output-feedback algorithms can be employed, but their performance strictly depends on trial and error tuning. Moreover, particular techniques have to be adopted to handle safety constraints. With the aim of overcoming these limitations, we propose an optimal-charging procedure based on deep reinforcement learning. In particular, we focus on a policy gradient method to cope with continuous sets of states and actions. First, we assume full state measurements from the Doyle–Fuller–Newman (DFN) model, which is projected to a lower dimensional feature space via the principal component analysis. Subsequently, this assumption is removed, and only output measurements are considered as the agent observations. Finally, we show the adaptability of the proposed policy to changes in the environment’s parameters. The results are compared with other methodologies presented in the literature, such as the reference governor and the proportional–integral–derivative approach.

Practical constrained output feedback formation control of underactuated vehicles via the autonomous dynamic logic guidance

This paper investigates the practical leader-follower formation control issue of underactuated vehicles. To achieve the waypoints-based formation navigation, the autonomous dynamic logic (ADL) guidance is proposed by incorporating the marine practice into the virtual ship-based formation guidance strategy. In the proposed guidance, only a dominant virtual leader is required for constructing the waypoints-based formation reference framework, which shows the simplicity and the practicability. As for the control part, a constrained output feedback algorithm is developed by means of the linear extended state observer (LESO). By constructing the augmented variable, the model uncertainty and unknown disturbances are integrated to be estimated and compensated together. In addition, a second-order dynamic auxiliary system is designed to handle the problem of actuator saturation, where two additional saturation compensation terms are introduced to stabilize the kinematics and the kinetics error dynamics, respectively, and the smoothness of constrained control signals can be guaranteed owing to the modification of Gaussian error function. Using the Lyapunov direct method, all signals in the closed-loop system are proved to be semi-global uniformly ultimately bounded (SGUUB). Finally, two simulation experiments, including the comparative experiment and the formation navigation experiment in the presence of simulated ocean disturbances, are carried out to illustrate the feasibility and the superiority of proposed scheme.

Adaptive fuzzy output regulation of uncertain pure‐feedback non‐linear systems with prescribed performance

The output regulation problem for pure-feedback non-linear systems with prescribed performance (PP) driven by an external linear system with unknown parameter are investigated. First, an observer with a disturbance signal is designed to observe unmeasured system states. Secondly, fuzzy logic systems (FLS) are used to approximate unknown non-linear non-affine functions. Thirdly, a fuzzy internal model is obtained to reject disturbance, and an adaptive fuzzy output feedback algorithm is proposed based on dynamic surface control (DSC) technique and backstepping method. Simultaneously, an L function is constructed so that the overall property (steady-state property and dynamic property) of the tracking error are constrained by PP functions. The results explain that the proposed control algorithm can make sure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SUUB), and the tracking error is semi-globally PP uniformly ultimately bounded (SPPUUB). Finally, two examples are given to show the validity of the results.

Adaptive Dynamic Programming for Model-Free Global Stabilization of Control Constrained Continuous-Time Systems.

This article addresses the problem of global stabilization of continuous-time linear systems subject to control constraints using a model-free approach. We propose a gain-scheduled low-gain feedback scheme that prevents saturation from occurring and achieves global stabilization. The framework of parameterized algebraic Riccati equations (AREs) is employed to design the low-gain feedback control laws. An adaptive dynamic programming (ADP) method is presented to find the solution of the parameterized ARE without requiring the knowledge of the system dynamics. In particular, we present an iterative ADP algorithm that searches for an appropriate value of the low-gain parameter and iteratively solves the parameterized ADP Bellman equation. We present both state feedback and output feedback algorithms. The closed-loop stability and the convergence of the algorithm to the nominal solution of the parameterized ARE are shown. The simulation results validate the effectiveness of the proposed scheme.

Fractional-order robust model reference adaptive control of piezo-actuated active vibration isolation systems using output feedback and multi-objective optimization algorithm

Improving the control performance of active vibration isolation systems is crucial to provide an ultra-quiet environment for precision instruments. This paper presents a new fractional-order robust model reference adaptive controller for the piezo-actuated active vibration isolation systems with a relative-degree-one model. One advantage of the proposed controller lies in that its controller parameters are adjusted online by fractional proportional–integral-type adaptive laws, which not only speeds up the convergence of the closed-loop system, but also improves the control energy efficiency. Moreover, the proposed controller only uses the measurable input and output of the plant as feedback signals, which is convenient for controller implementation. The stability of the closed-loop system is proved based on the Lyapunov theory in detail. The optimal values of the fractional order and adaptive gains for adaptive laws are determined using the multi-objective genetic algorithm through off-line simulation. Comparative experiments on the piezo-actuated active vibration isolation systems are conducted to verify the effectiveness of the proposed controller. Results show that the proposed controller achieves satisfactory isolation performance in a wider frequency band of 20–500 Hz, and simultaneously reduces the control effort compared with the traditional MRAC methods.

Dynamic Output Feedback Predictive Control With One Free Control Move for the Takagi–Sugeno Model With Bounded Disturbance

In this paper, the fuzzy model-predictive control for a nonlinear system represented by a discrete-time Takagi–Sugeno model with norm-bounded disturbance is studied. An output feedback algorithm is proposed by parameterizing the infinite horizon control moves and estimated states into one free control move and one free estimated state followed by a dynamic output feedback law. Since the introduced free control move and free estimated state are decision variables that bring more degrees of freedom for the optimization, a larger feasibility region and better control performance can be achieved. By properly designing the constraints in the optimization problem, the recursive feasibility and the convergence of the closed-loop system to the neighborhood of the equilibrium are guaranteed. A numerical example is given to illustrate the effectiveness of the proposed algorithm.

An iterative Q‐learning scheme for the global stabilization of discrete‐time linear systems subject to actuator saturation

SummaryIn this paper, we propose a model‐free algorithm for global stabilization of linear systems subject to actuator saturation. The idea of gain‐scheduled low gain feedback is applied to develop control laws that avoid saturation and achieve global stabilization. To design these control laws, we employ the framework of parameterized algebraic Riccati equations (AREs). Reinforcement learning techniques are developed to find the solution of the parameterized ARE without requiring any knowledge of the system dynamics. In particular, we present an iterative Q‐learning scheme that searches for a low gain parameter and iteratively solves the parameterized ARE using the Bellman equation. Both state feedback and output feedback algorithms are developed. It is shown that the proposed scheme achieves model‐free global stabilization under bounded controls and convergence to the optimal solution of the ARE is achieved. Simulation results are presented that confirm the effectiveness of the proposed method.

Output Feedback Algorithm for Nonlinear Systems with Compensation of Bounded Disturbances and Measurement Noises

The output feedback algorithm for dynamic plants with compensation of parametric uncertainty, external disturbances and measurement noises is synthesized. The plants are described by a nonlinear system of differential equations with vector input and output signals. Unlike most existing control schemes in this paper the dimensions of the measurement interference and the output signal are equal, the sources of the signals of disturbances and disturbances are different, parametric and external disturbances can be present in any equation of the plant model. For simultaneous compensation of disturbances and measurement noises it is proposed to consider two channels. On the first channel a part of the measurement noises will be estimated which will allow partial recovery the information about the plant noisy output. On the second channel the disturbances will be compensated. Thus, at least two independent measurement channels are required for simultaneous compensation of disturbances and measurement noises. Sufficient conditions for calculating the parameters of the algorithm in the form of solvability of the linear matrix inequality are obtained. It is shown that the equation of a closed-loop system obtained on the basis of the proposed algorithm depends on the disturbances and the smallest component of the measurement noise. However, if the smallest component cannot be identified a priory, the results of the transients depend on the component of the noise that will be selected in the synthesis of the control system. Thus, unlike most existing control schemes, where the equation of a closed-loop system depends on disturbance and noise, the resulting algorithm provides better transients, because they do not depend on the entire noise vector, but only on its smallest (one) component. The simulations for a third-order nonlinear plant and the synchronization of an electrical generator connected to the power grid are presented. Numerical examples illustrate the effectiveness of the proposed scheme and the robustness with respect to random components in the noises and disturbances.

Optimal control of laminated plate integrated with piezoelectric sensor and actuator considering TSDT and meshfree method

Abstract In this paper, the element free galerkin (EFG) method based on third-order shear deformation theory (TSDT) is used to investigate shape and vibration control of piezoelectric laminated plate bonded with piezoelectric actuator and sensor layers. The electric potential distributions through the thickness for each piezoelectric layer are assumed to vary linearly. In addition, a closed-loop velocity feedback control and optimal steady-state regulator with output feedback algorithm is used for the active control of the static deflection as well as the dynamic response of the plates with bonded distributed piezoelectric sensors and actuators. Furthermore, the effects of the size of support and nodal density on the numerical accuracy are also investigated. The results indicate that, the accuracy and reliability of presented work have an excellent agreement with those of other available numerical approaches such as finite element and FSDT meshfree method.

Decentralized adaptive output feedback fuzzy controller for a class of large-scale nonlinear systems

In the previous work of Huang et al., a coordinated decentralized hybrid adaptive output feedback fuzzy control scheme of large-scale nonlinear systems is obtained predicated upon this prerequisite assumption that the local controllers can share the a priori information about their individual reference models. In this note, we concentrate in the absence of the coordination assumption on developing a classical decentralized combined indirect and direct adaptive fuzzy controller for a class of uncertain large-scale nonlinear systems. The output feedback and adaptation mechanisms proposed for each subsystem hinges just upon its individual output, regardless of any other output reference. Neither the famous strictly positive real (SPR) condition nor a high-gain observer (HGO) is required to realize the overall output feedback algorithm. The tracking errors of the closed-loop large-scale system are shown to converge to tunable neighborhoods of the origin. Simulation results on correlated inverted pendulums verify the validity of the decentralized controller modification.

Robust Output Feedback Using Sliding Mode Control

A general sliding mode output feedback algorithm is developed that takes into account parametric uncertainty and external disturbances provided certain bounds are known. It has been assumed that the number of states is greater than the number of sensors and that there can be a mismatched disturbance. The details of an eigenvalue perturbation analysis for an uncertain matrix are presented. A boundary layer has been introduced around each sliding surface to eliminate the chattering phenomenon. The results from numerical simulations are presented to corroborate the validity of the proposed controller.

Sliding Mode Control of Mechanical Systems with Bounded Disturbances via Output Feedback

In this paper, a new sliding mode output feedback algorithm is developed to control the vibration of mechanical systemssubjectedto disturbanceforces.Ithasbeenassumed thatthenumberofstatesisgreaterthan thenumberof sensors.Theslidingmodecontrollawisdesignedto berobusttoexternaldisturbancesprovidedtheirupperbounds are known. A boundary layer has been introduced around each sliding hyperplane to eliminate the chattering phenomenon. The results from numerical simulations are presented to corroborate the validity of the proposed controller.