Performance Of Closed-loop System Research Articles

Decentralized Event-triggered Control for Interconnected Systems with Unknown Disturbances

In this paper, an event-trigger based adaptive control scheme is developed for a class of interconnected systems with unknown external disturbance. Under the proposed control scheme, the effects caused by external disturbance and unknown measurement error can be compensated by constructing the estimator of its unknown constant upper bound. The effect caused by unknown interactions occurred in all subsystems which exist in every channel can be dealt with signals zi,1ϕi added in virtual control αi,1. In addition, the introduction of smooth function sgi(·) in controller make the approximation of sign(·) become more accurate. So that the performance of closed-loop system can be improved. Finally, The proof of avoiding the Zeno behavior under such event-trigger based adaptive controller are finished and simulation results shown that the designed event-trigger based adaptive controller ensures the stability of closed-loop system.

Robust Discrete-Time Spatial Repetitive Controller

This brief proposes a novel discrete-time realization of the spatial repetitive controller topology, an internal model principle-based regulator, which deals with the exogenous position-dependent periodic signals. A simple procedure is proposed in order to estimate the time-varying delay required to implement this spatial controller, resulting in a single recursive equation. A robust synthesis methodology based on a convex optimization problem constrained by linear matrix inequalities is also presented in order to guarantee the closed-loop system stability and performance in the presence of uncertain plant dynamics. The proposed design and implementation methods are validated on a direct current motor subject to angle-dependent disturbances.

Improved UDE and LSO for a Class of Uncertain Second-Order Nonlinear Systems Without Velocity Measurements

In recent years, the uncertainty and disturbance estimator (UDE) technique has been widely used for robustness improvement in state-feedback control systems. This article extends the design of the UDE to output feedback scenarios by considering the robust control problem of a class of Euler–Lagrange systems with a nonlinear velocity term but having no velocity measurements. We propose a simple feedback-control scheme that includes a modified Luenberger state observer (LSO) to estimate the velocity and a modified UDE to estimate exogenous disturbances. The novel feature of the scheme is that a mutual coupling between LSO and UDE is introduced to improve the estimation and control precision. To analyze the performance of the fifth-order nonlinear closed-loop system, we decompose it into a second-order tracking-error subsystem and a third-order estimation-error subsystem and propose a linear nonsingular state transformation and an ingenious parameter mapping for the estimation-error subsystem. Finally, by the singular perturbation theory, we derive a simple stability condition and a single-parameter tuning approach to reduce the steady-state estimation errors and tracking errors. The performance improvement resulting from the mutual coupling effect and the effectiveness of the tuning approach are demonstrated by numerical simulations and experimental verifications on a 3-DOF helicopter platform.

Reinforcement learning for human-robot shared control

PurposeThis paper aims to propose a general framework of shared control for human–robot interaction.Design/methodology/approachHuman dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.FindingsSimulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.Originality/valueCompared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.

Sensor Networks for Aerospace Human-Machine Systems.

Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator’s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator’s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator’s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.

Multi-loop Model Reference Adaptive PID Control for Fault-Tolerance

This study demonstrates application of multi-loop model reference adaptive control (MRAC) structure to enhance fault tolerance performance of closed-loop PID control systems. The proposed multi-loop MRAC-PID structure can transform a conventional PID control system to an adaptive control system by combining an outer adaptation loop that employs MIT rule. This control structure can be used to improvement of fault tolerance and fault detection performance of the existing closed-loop PID control systems. One of the advantages of this structure originates from the reference input shaping technique that allows adaptive control without modifying any coefficients of the existing PID controllers. Numerical and experimental studies are presented to illustrate the application of the multi-loop MRAC-PID control structure for rotor control applications.

The Fastest Response Algorithm for Thermal Plants with a Time Delay: Application in a Hybrid System Containing a PID Controller and an Automatic Tuning Unit

The article discusses matters concerned with improving the performance of automatic closed-loop control systems of thermal plants with a time delay in responding to a change in the setpoint. The improvement is achieved by applying the fastest response algorithm (FRA) according to the Pontryagin maximum principle and using linear prediction of the controlled variable. It is shown that, during operation with switching the maximum control outputs applied to a plant with a time delay, linear prediction is inefficient, and self-oscillations may occur in the system. The technical solution proposed for eliminating self-oscillations implies the use of a hybrid closed-loop control system comprising an FRA, a PID controller, and an automatic controller tuning (ACT) unit, which performs the function of determining the plant model parameters and optimizing the controller parameters. The actuator is considered as a proportional section that is used as part of the plant. The control limitations are related to the level of the control output applied to the plant. The ACT unit comprises accelerated controller tuning algorithms that use active plant identification methods based on analyzing the response to an impulse input and two cycles of the excited self-oscillations. These algorithms make it possible to determine four parameters of the second-order plant model with a time delay. The self-oscillations occurring in systems with the FRA and plants with a time delay are eliminated at the end of the transient by making a switchover to PID control. Four embodiment versions of a system with the FRA are analyzed, specifically, those with and without control output reversal and also with using the plant simulation model without a time delay that is obtained from the ACT operating in parallel with the plant. For practical embodiment of the MSRA as part of a hybrid system, it is recommended to use its version without control output reversal. Relations for calculating the controlled variable prediction coefficient in terms of the plant model parameters in a wide range are obtained. Two examples of using the hybrid system equipped with industry-grade controllers for a temperature control system are given: one with the electric heater power controlled using a pulse-width modulator and the other with a constant speed actuator.

Finite-time event-triggered non-fragile control and fault detection for switched networked systems with random packet losses

This paper investigates the problem of event-triggered observer-based fault detection for a class of uncertain switched nonlinear network control systems (SNNCSs) with random communication packet losses and infinite distributed delay. The main objective of this work is to design an event-triggered state feedback non-fragile H∞ controller such that the resulting closed-loop form of the considered SNNCSs is robustly finite-time bounded and satisfies a prescribed H∞ performance constraint in the finite-time interval. By employing average dwell time approach, the problem of finite-time boundedness and residual H∞ performance analysis is discussed. Specifically, a new set of conditions in the form of LMIs are derived to ensure the finite-time boundedness with the prescribed residual H∞ performance for the relevant closed-loop system. Moreover, the desired observer-based state feedback controller gain matrices and residual weighting matrices can be expressed in an explicit form. Finally, the applicability and effectiveness of the developed results are examined via numerical examples with simulation results.

Mixed ICC/ℋ∞ control for systems with sensors aging

A faulty sensor may lead to degraded system performance, system instability or even a fatal accident. On the other hand, the increasing need for safety and reliability has motivated the development of fault-tolerant control techniques. In this work, the sensor performance degradation due to its aging is modelled by the increment of sensor measurement noise covariance. The main contribution of this paper is the characterisation of the control synthesis conditions using parametrised linear matrix inequalities (PLMIs) for a multi-objective gain-scheduled noisy output-feedback controller that minimises the output cost on performance with satisfactory system stability, performance and control input covariance constraints ( constraints on the control inputs) in the presence of sensor aging. The closed-loop system stability and performance, in terms of mixed / performances, relative improvement, numerical complexity, computation time, and initial conditions response, are studied, and a numerical example is used to illustrate the effectiveness of the proposed control scheme. The synthesised controller guarantees not only the stability but also the closed-loop mixed / performances, and it is feasible for real-time applications.

An Algorithm for Performance Evaluation of Closed-Loop Spare Supply Systems With Generally Distributed Failure and Repair Times

This paper proposes an algorithm for evaluating the performance of a closed-loop spare machines supply system when the time between failures as well as the repair times is generally distributed. The closed-loop system with finite number of machines and the generic time distribution assumption well represents real-life spare machines supply systems but at the cost of higher complexity. The increased complexity prohibits the use of existing models, which evaluate the performance of closed-loop systems assuming exponentially distributed failure and repair times. The Extended Bottleneck (EBOTT) algorithm in the literature estimates the performance measures of a closed-loop system with generally distributed repair service times. However, as our analysis indicates, the EBOTT algorithm may fall into an infinite loop even in assessing the performance of a simple spare machines supply system. The Modified EBOTT algorithm presented in this paper overcomes the shortcomings of the EBOTT algorithm and exhibits superior performance. Finally, through tradeoff analysis between spare machines inventory level and repair shop capacities, insights into the performance of such systems are presented.

Stabilization of Discrete-Time Linear Systems With an Unknown Time-Varying Delay by Switched Low-Gain Feedback

In this note, we investigate the stabilization of discrete-time linear systems with an unknown time-varying delay. Existing literature on the stabilization of time-delay systems require some knowledge, such as an upper bound, of the delay. In this paper, by proposing a switched low-gain feedback control law, we are able to achieve asymptotic stabilization of the system without any knowledge of the delay. In addition, the proposed switched low-gain feedback law also significantly improves the closed-loop system performance in terms of overshoot and convergence speed in comparison with the traditional fixed gain design. Simulation study illustrates the effectiveness of our theoretical results.

Adaptive amplitude fast proportional integral phasor extremum seeking control for a class of nonlinear system

In this paper, we present a modification of the fast phasor extremum-seeking control for the fast optimization of a class of Wiener-Hammerstein nonlinear dynamical systems. The proposed technique provides a significant improvement of the closed-loop system's performance. This study introduces a new adaptive amplitude technique that is used to adaptively adjust the perturbation amplitude to a small predetermined value in a neighbourhood of the system's unknown optimal equilibrium. An analysis of the system demonstrates that semi-global practical stability analysis of the overall system to the unknown optimum is achieved. The effectiveness of the proposed approach is illustrated using numerical examples. The approach is also implemented for the optimal operation of a lean burn combustion system.

Optimal adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control method for some classes of nonlinear systems

In this research, a novel adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control (AIT2FFOBSMC) method is presented for some classes of nonlinear fully-actuated and under-actuated mechanical systems with uncertainty. The AIT2FFOBSMC method exploits the advantages of backstepping and sliding mode methods to improve the performance of closed-loop control systems by lowering the tracking error and increasing robustness. To mitigate chattering and the tracking error, a fractional sliding surface is designed. In addition to the fractional sliding surface, an adaptive interval type-2 fuzzy compensator is used to estimate the uncertainty and perturbation of the nonlinear system in order to further reduce chattering caused by switching term as well as to enhance the perturbation rejection. In order to achieve an optimal performance, the multi-tracker optimization algorithm (MTOA) is used. Finally, a number of simulations and experimental tests are carried out to examine the performance of the AIT2FFOBSMC method.

Lyapunov model predictive control to optimise computational burden, reference tracking and THD of three‐phase four‐leg inverter

Due to the evolution of high processing microprocessors, the model predictive control (MPC) has been widely used in power electronic applications. In spite of simplicity, flexibility and fast dynamic response, the conventional MPC (C-MPC) has a drawback of computational burden. This study focuses on Lyapunov MPC (L-MPC) method, in which Lyapunov control law is employed in the cost function to minimise the error between the desired control variables and the actual control variables of three-phase four-leg inverter to optimise the closed-loop system performance. The proposed control algorithm takes advantage of a predefined Lyapunov control law which minimises the required calculation time by the Lyapunov model equations just once in each control loop to predict the future variables. L-MPC technique improves the digital speed by 23.8% as compared to C-MPC. It reduces current tracking error and total harmonic distortion (THD) in the variation of inverter control parameters. The stability of the system is established through Lyapunov function with the help of backsteping control method. The LabVIEW field programmable gate array (FPGA) rapid prototyping controller is used to validate the proposed control method. The experimental results showed that the proposed system has better performance as compared to C-MPC.

Performance evaluation of a hybrid fuzzy logic controller based on genetic algorithm for three phase induction motor drive

<p class="Author"><span>It is known that controlling the speed of a three phase Induction Motor (IM) under different operating conditions is an important task and this can be accomplished through the process of controlling the applied voltage on its stator circuit. Conventional Proportional- Integral- Differeantional (PID) controller takes long time in selecting the error signal gain values. In this paper a hybrid Fuzzy Logic Controller (FLC) with Genetic Algorithm (GA) is proposed to reduce the selected time for the optimized error signal gain values and as a result inhances the controller and system performance. The proposed controller FL with GA is designed, modeled and simulated using MATLAB/ software under different load torque motor operating condition. The simulation result shows that the closed loop system performance efficiency under the controller has a maximum value of 95.92%. In terms of efficiency and at reference speed signal of 146.53 rad/sec, this system performance shows an inhancement of 0.67%,0.49% and 0.05% with respect to the closed loop system efficiency performance of the PID, FL, and PID with GA controllers respectively. Also the simulation result of the well designed and efficient GA in speeding up the process of selecting the gain values, makes the system to have an efficiency improvement of 14.42% with respect to the open loop system performance.</span></p>

A Fault Tolerant Control Scheme Using the Feasible Constrained Control Allocation Strategy

This paper investigates the necessity of feasibility considerations in a fault tolerant control system using the constrained control allocation methodology where both static and dynamic actuator constraints are considered. In the proposed feasible control al-location scheme, the constrained model predictive control (MPC) is employed as the main controller. This considers the admissible region of the control allocation problem as its constraints. Using the feasibility notion in the control allocation problem provides the main controller with information regarding the actuator′s status, which leads to closed loop system performance improvement. Several simulation examples under normal and faulty conditions are employed to illustrate the effectiveness of the proposed methodology. The main results clearly indicate that closed loop performance and stability characteristics can be significantly degraded by neglecting the actuat-or constraints in the main controller. Also, it is shown that the proposed strategy substantially enlarges the domain of attraction of the MPC combined with the control allocation as compared to the conventional MPC.

Circulation algorithm for partial eigenstructure assignment via state feedback

This paper treats the partial eigenstructure assignment problem for multi-variable linear systems by state feedback. Both the open-loop and the closed-loop eigenvalues are allowed to have arbitrary geometric and algebraic multiplicities. Based on a previously proposed result on partial eigenstructure assignment, a new complete parametric solution is derived, which gives a complete general parameterization of the feedback gain and also provides the general parametric expression for all the admissible eigenvector matrices. The solution involves, instead of a matrix inverse of full-dimension, the inverse of a matrix with a much smaller dimension. By using this new solution repeatedly, a circulation algorithm is proposed, which is simple and dramatically reduces the computational load in the case of large scale systems. Furthermore, two important special cases are especially examined, and simple and direct solutions are proposed, which no longer needs computation of matrix inverses, and suits best in the proposed circulation algorithm. The approach provides all the design degrees of freedom which can be further utilized to achieve additional closed-loop system performance. An illustrative example is thoroughly examined, which demonstrates the procedure and the advantages of the proposed parametric approach.

Reduced Order Observer for Two-Connected Machine Drive

The paper presents a study of a reduced order observer with the vector control of a series-connected two-motor drive supplied by a single inverter. The multimachine system consists of a six-phase induction machine and a three-phase induction machine. The stators windings of both machines are connected in series in appropriate manner. The dynamic decoupling of each machine from the group is obtained using the indirect vector control algorithm. Modeling of the multi-machine system showed the possibility of independent control of each machine in the group. The independent control is demonstrated by analyzing the characteristics of the torque and speed of each machine obtained via simulation of multi-machine system under indirect vector control scheme. A reduced-order observer is applied to sensor-less control of multimachine. The simulation results show that the closed-loop system performances are satisfactory using the proposed algorithm.

Modeling and Control of an Irrigation Station Process Using Heterogeneous Cuckoo Search Algorithm and Fuzzy Logic Controller

Water is becoming a precious and very scarce resource in many countries due to an increasing demand in agricultural and industrial fields as well as population growth. Therefore, we have to optimize the water resources from hydraulic systems, in order to decrease water losses. In this context, many research studies focused on modeling, identifying, and controlling of hydraulic systems, especially for agricultural use. In this paper, we will investigate the problem of identification and control design for an irrigation process through an academic irrigation station (IS) system. Two main objectives will be reached. On the one hand, we will provide a new optimal Takagi–Sugeno (T–S) fuzzy model of our IS process. The optimal model is obtained through using a fuzzy parameters searching strategy, named intelligent T–S modeling. This last, is associated with a heterogeneous cuckoo search (HeCoS) strategy based on the quantum mechanism. On the other hand, we will devote a lot to the synthesis of an optimal control law by using fuzzy logic control (FLC) to ensure the global stability of the closed-loop system. The proposed FLC has two major advantages. First, it does not require a complex process to find a common Lyapunov function for a large number of fuzzy subsystems. Therefore, the designed procedure of the proposed FLC is much simpler. Second, by employing the FLC, the closed-loop system performance can be designed. Finally, and as a result, the HeCoS strategy is well adopted to find an optimal model for the real processes with high accuracy and strong generalization ability. Experimental results applied to the IS demonstrate the merits of the proposed FLC.

Transient Performance Improvement in Reduced-Order Model Reference Adaptive Control Systems

The objective of model reference adaptive control systems is to drive the trajectories of an uncertain dynamical system to the trajectories of a given reference model capturing a desired closed-loop system performance. To this end, most adaptive control signals take the form ua(t) = −ŴT(t)σ(x(t)), where x(t) ∈ ℝn denotes the state vector of an uncertain dynamical system, σ:ℝn → ℝs denotes a known basis function, and Ŵ(t) ∈ ℝs×m denotes an estimation of the unknown weight matrix W ∈ ℝs×m satisfying sm update laws (here m denotes the number of control inputs). In this paper, we focus on a class of reduced-order, computationally less expensive, model reference adaptive control systems that are only predicated on a scalar update law. Specifically, our contribution is to utilize a command governor architecture in order to improve transient performance of this class of adaptive control systems. We prove the stability of the overall closed-loop system using Lyapunov stability theory and we also present an illustrative numerical example for demonstrating the efficacy of the proposed architecture.