Desired Control Objectives Research Articles

Moment-Based Reinforcement Learning for Ensemble Control.

Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and control engineering. Owing to the severely under-actuated nature and the difficulty of placing large-scale sensor networks, ensemble systems are limited to being actuated and monitored at the population level. Moreover, mathematical models describing the dynamics of ensemble systems are often elusive. Therefore, it is essential to design broadcast controls that excite the entire population in such a way that the heterogeneity in system dynamics is robustly compensated. In this article, we propose a reinforcement learning (RL)-based data-driven control framework incorporating population-level aggregated measurement data to learn a global control signal for steering a dynamic population in the desired manner. In particular, we introduce the notion of ensemble moments induced by aggregated measurements and derive the associated moment system to the original ensemble system. Then, using the moment system, we learn an approximation of optimal value functions and the associated policies in terms of ensemble moments through RL. We illustrate the feasibility and scalability of the proposed moment-based approach via numerical experiments using a population of linear, bilinear, and nonlinear dynamic ensemble systems. We report that the proposed method achieves the desired control objectives of various ensemble control tasks and obtains significantly better averaged-reward when compared with three existing methods.

Output-constrained fixed-time coordinated control for multi-agent systems with event-triggered and delayed communication

In this study, we propose a fixed-time coordinated controller for high-order multi-agent systems dealing with output constraints and switching digraphs while considering event-triggered and delayed communication. To tackle the scenario where only a subset of agents have direct access to the leader, a novel event-triggered fixed-time observer is first devised. This observer effectively estimates the state of the leader, considering the influence of communication delays. Subsequently, a neural network-based fixed-time controller is formulated under output constraints, utilizing the estimated leader commands to achieve the desired control objectives. Finally, practical experiments are conducted using omni-mecanum-wheeled robots to demonstrate the practical feasibility and superior performance of the theoretical findings. This study contributes significantly by tackling the challenging problem of output-constrained fixed-time coordinated control in event-triggered and delayed communication within switching digraphs. This area has yet to be previously explored.

Estimated-State Feedback Fuzzy Compensator Design via a Decentralized Approach for Nonlinear-State-Unmeasured Interconnected Descriptor Systems

This paper investigates the decentralized fuzzy control problems for nonlinear-state-unmeasured interconnected descriptor systems (IDSs) that utilize the observer-based-feedback approach and the proportional–derivative feedback control (PDFC) method. First of all, the IDS is represented as interconnected Takagi–Sugeno (T–S) fuzzy subsystems. These subsystems can effectively capture the dynamic behavior of the system through fuzzy rules. For the stability analysis of the system, this paper uses the free-weighing Lyapunov function (FWLF), which allows the designer to set the weight matrix, to achieve the desired control performance and design the controller more easily. Furthermore, the control problem can be transformed into a set of linear matrix inequalities (LMIs) through the Schur complement, which can be solved using convex optimization methods. Simulation results confirm the effectiveness of the proposed method in achieving the desired control objectives and ensuring system stability.

Optimal tracking control of an Autonomous Underwater Vehicle: A PMP approach

This paper addresses the optimal tracking control problem of an Autonomous Underwater Vehicle (AUV) using Pontryagin’s Minimum Principle (PMP). The formulation uses a choice of Hamiltonian function using the desired control objective with AUV dynamics acting as dynamic constraints. We first develop PMP based on AUV kinematics and then extend it to the dynamics to arrive at optimal thrusts and moments. Necessary conditions for optimality are derived for both the models using PMP, which results in optimal trajectories that simultaneously minimize the tracking error as well as the control cost, thereby arriving at energy optimality. It was observed that the adjoint variables (costates) are indeed the momenta in the inertial and body-fixed frames. At the kinematic level, this forms a stable solution. The developed methodology is applied to both 2D and 3D AUV model with disturbances due to inputs and ocean currents. Numerical simulations are carried out with the derived control laws for a given trajectory tracking target. Quantitative evaluation of the performance and comparison of the controller is done using Mean Square Error(MSE) and Total Variation(TV) measures. The proposed control laws are found to achieve the desired control objectives.

On design of power sharing for VSC‐based islanded AC microgrids: An adaptive MIMO controller equipped with a predictor

AbstractIn an AC microgrid, the active/reactive power is usually shared among its distributed generators (DGs) based on the frequency‐active power () droop and the voltage‐reactive power () droop. By increasing the resistant/inductance ratio () of feeder lines; however, adverse effects of interactions between these two control loops are intensified. In this paper, an adaptive multi‐input multi‐output (MIMO) current control structure is proposed to tackle this problem in AC microgrids with arbitrary numbers of DGs in the primary control level. A deep analysis based on the relative gain array (RGA) matrix and the diagonal dominance concept is provided to systematically design MIMO controllers. The proposed technique is based on the Lyapunov's stability theory, and the asymptotic stability of the whole microgrid is guaranteed. For each DG, the suggested design procedure is started by defining a model reference in which the desired control objectives, including the settling time and the steady‐state error, are considered. Then, a feedback‐feedforward controller is established where its gains are adaptively tuned by some rules derived from a Lyapunov function. Moreover, a predictor is used to estimate the adverse effects of other DGs which are taken into account as external disturbances during the design process of the adaptive controller. By considering some realistic scenarios through time‐domain simulations in MATLAB/SIMULINK, it is shown that the proposed strategy can be successfully used to solve the power sharing problem in AC microgrids.

Event-based distributed resilient control strategy for microgrids subject to disturbances and hybrid attacks

As a typical class of Cyber-Physical Systems (CPSs), microgrids (MGs) are susceptible to malicious cyber attacks and disturbances that can compromise their security and reliability. To address this issue, an event-triggered distributed resilient control method is proposed in this paper to mitigate the impacts of disturbances, FDI and DoS attacks on MGs. The proposed approach employs a piecewise control strategy and utilizes adaptive method to achieve the secondary control objective of MGs. In order to conserve computing and communication resources, an event-triggered mechanism is adopted and its parameter selection is associated with attacks. This design ensures system stability while also eliminating Zeno behavior. By applying Lyapunov stability theory, it can be proven that the algorithm proposed in this paper is capable of achieving the desired control objective. Finally, the effectiveness and superiority of the developed method are demonstrated through simulation examples.

Control design-oriented modeling and µ-synthesis-based robust multivariate control of a turbocharged natural gas genset engine

This paper describes a comprehensive framework for the development and validation of a model-based robust multivariate controller, which is used to regulate the gas exchange processes of an advanced turbocharged natural gas genset engine. The natural gas genset engine involved in this study features a multi-input and multi-output structure and is highly nonlinear, making it a challenge for which to design a controller. A control design-oriented model describing the dynamics of the studied engine is first developed and validated, using modeling techniques commonly employed for modeling of turbocharged engines. A novel robust model-based multivariate controller is then synthesized to incorporate the significant coupling between the engine actuators (throttle valve, bypass valve, and fuel valve) and the desired control objectives (engine speed, throttle differential pressure, and air-to-fuel ratio). To verify the effectiveness of the synthesized controller, it is compared to a benchmark production controller using an experimentally-validated simulation model. Simulation results indicate the merits of the synthesized robust multivariate controller in terms of better tracking performance (robust multivariate controller reduces steady-state error of engine speed, throttle differential pressure, and AFR at most by 100%, 58.9%, and 100% respectively, compared to benchmark production controller), faster transient response (robust multivariate controller reduces settling time of engine speed, throttle differential pressure, and AFR at most by 74.5%, 47.8%, and 71.2% respectively, compared to the benchmark production controller), and finer coordination among multiple interacted actuators when faced with multifacted control objectives.

Transmission Power Policies for Energy-Efficient Wireless Control of Nonlinear Systems

We present an emulation-based controller and transmission policy design procedure for nonlinear wireless networked control systems. The objective is to ensure the stability of the closed-loop system, in a stochastic sense, together with given control performance, while minimizing the average power used for communications. The controller is designed by emulation, i.e., ignoring the network, and the transmission power is given by threshold policies. These policies involve waiting a given amount of time since the last successful transmission instant, as well as requiring that the measured wireless channel gain is above a given threshold, before attempting a new transmission. Two power control laws are investigated: i) a constant power and ii) a power level inversely proportional to the channel gain. We explain how to select the waiting time, the channel threshold and the power level to minimize the induced average communication power, while ensuring the desired control objectives.

Fixed-Time RBFNN-Based Prescribed Performance Control for Robot Manipulators: Achieving Global Convergence and Control Performance Improvement

This paper proposes a fixed-time neural network-based prescribed performance control method (FNN-PPCM) for robot manipulators. A fixed-time sliding mode controller (SMC) is designed with its strengths and weaknesses in mind. However, to address the limitations of the controller, the paper suggests alternative approaches for achieving the desired control objective. To maintain stability during a robot’s operation, it is crucial to keep error states within a set range. To form the unconstrained systems corresponding to the robot’s constrained systems, we apply modified prescribed performance functions (PPFs) and transformed errors set. PPFs help regulate steady-state errors within a performance range that has symmetric boundaries around zero, thereby ensuring that the tracking error is zero when the transformed error is zero. Additionally, we use a singularity-free sliding surface designed using transformed errors to determine the fixed-time convergence interval and maximum allowable control errors during steady-state operation. To address lumped uncertainties, we employ a radial basis function neural network (RBFNN) that approximates their value directly. By selecting the transformed errors as the input for the RBFNN, we can minimize these errors while bounding the tracking errors. This results in a more accurate and faster estimation, which is superior to using tracking errors as the input for the RBFNN. The design procedure of our approach is based on fixed-time SMC combined with PPC. The method integrates an RBFNN for precise uncertainty estimation, unconstrained dynamics, and a fixed-time convergence sliding surface based on the transformed error. By using this design, we can achieve fixed-time prescribed performance, effectively address chattering, and only require a partial dynamics model of the robot. We conducted numerical simulations on a 3-DOF robot manipulator to confirm the effectiveness and superiority of the FNN-PPCM.

High-performance isolation systems with rate-independent linear damping for seismic protection of high-rise buildings

There is currently growing concern within the earthquake engineering community regarding the potential for base-isolated structures to experience significant responses during extreme earthquakes. This concern is primarily driven by the observation that induced ground motions can contain influential long-period components, which may exacerbate the structural response. Supplementing damping using conventional damping devices contributed to a significant reduction in the responses whereas perhaps compromises the effects of base isolation itself when subjected to more frequently occurring short-period ground motions, particularly for base-isolated high-rise building structure. The present study employs rate-independent linear damping (RILD) to control the seismic response of high-rise building structures subjected to both short- and long-period ground motions. RILD is a damping mechanism that exerts a control force that is independent of the vibration frequency under harmonic deformation. This method is implemented in conjunction with isolators to achieve the desired control objectives. For passive realization of RILD, both the Maxwell-negative-stiffness (MNS) and modified tuned Maxwell–Wiechert (MTMW) models were considered. A regression technique is presented in this study to optimally design the properties of the MTMW model, and a recursive formulation is developed to conduct structural response history analyses efficiently. The proposed method for seismic protection of high-rise structures was demonstrated using a 20-story benchmark building structure as the analytical model. This approach serves to illustrate the advantages of the proposed method in a practical context. Both short- and long-period ground motions are used for examining the performance of rate-independent damping models incorporated into base-isolated high-rise structures. The findings suggest that the combined isolation systems, in conjunction with rate-independent linear damping (RILD), offer significantly enhanced protection against conventional short-period ground motions, without sacrificing performance during long-period ground motions. Compared to commonly utilized isolation systems, this approach demonstrates superior performance in safeguarding high-rise structures.

Control of a jet engine using predictor-based adaptive strategy in different flight modes

In this article, a predictor-based model reference adaptive control method is proposed for a JetCat SPT5 turboshaft engine in full thrust, cruise, and idle modes. In the predictor-based model reference adaptive control method, in addition to the tracking error, the predictor error is also utilized in adaptive laws to achieve the desired control objectives, such as improving the tracking performance and control signals, and reducing the tracking error. The proposed method is implemented on a turboshaft engine system with actual dynamic values. First, three properly separated equilibrium points are selected on the nominal plant equilibrium manifold to linearize the plant model at the equilibrium points. Then, the predictor-based model reference adaptive control controller is designed and investigated for the three equilibrium points generating three operating modes of the engine, that is, full thrust, cruise, and idle. The stability of the control system is proved by the Lyapunov method. To evaluate the efficiency of the state predictor method in the simulation scenarios, the proposed method is compared with the classic model reference adaptive control method. The simulation results illustrate the superiority of the predictor-based model reference method due to the reduction of unwanted fluctuations in tracking performance, faster convergence of the tracking error to zero, and smoother control signals for all the equilibrium points.

Dual-Mode Based Sliding Mode Control Approach for Nonlinear Chemical Processes.

This paper synthesizes a new sliding mode controller (SMC) approach to enhance tracking and regulation tasks by following dual-mode concepts. The new control law consists of two distinct types of operation, using the combination of higher gain to large error signals (transient) and lower gain to small error signals (the region around the set point). The design is presented from a dual-mode (PD-PID) sliding surface operating in concert, fulfilling desired control objectives to ensure stability and performance. Therefore, a new controller was established, and we called it a dual-mode based SMC. The proposed controller is tested by computer simulations applied to two nonlinear processes, a continuous stirred-tank reactor (CSTR) and a mixing tank with a variable dead time. The results are compared with two different alternatives of SMC. In addition, the merits and drawbacks of the control schemes are analyzed using radial graphs, comparing the control methods with various performance measures for set points and disturbances changes. The ITSE (integral of time multiplied by the squared error), TVu (total variation of control effort) indices, Mp (maximum overshoot), and ts (settling time) were the indices used for performance analysis and comparisons.

Explaining the “Mystery” of Periodicity in Inter-Transmission Times in Two-Dimensional Event-Triggered Controlled Systems

Motivated by scenarioswhere the communication or the computation resources are limited, event-triggered control consists of transmitting data between the plant and the controller according to the actual system needs and not the elapsed time since the last transmission instant as in traditional sampled-data control, so that the desired control objective is achieved. A range of techniques are nowadays available to design event-triggered controllers. However, we generally have only very little information about the actual behavior of the transmission instants and thus about the amount of transmissions being actually generated, though this is a key feature of the design. In this article, we analyze the inter-event times, i.e., the times between two successive transmission instants, when the plant is modeled as a two-dimensional linear time-invariant system. The controller is a state-feedback law and the triggering rule is the relative threshold policy, which is allowed to be time-regularized. One of the main results in this article is the explanation of the oscillatory behavior of the inter-event times when the constant used to define the threshold is small relative to 1, a phenomenon commonly observed in simulations but never explained so far. More generally, the presented results help to understand the behavior of the inter-event times, instead of solely relying on numerical simulations, and thereby can be exploited to rigorously evaluate the performance of the considered triggering condition in terms of (average) inter-transmission times.

Time-varying formation control for nonlinear multi-agent systems against actuator attacks

The formation control problem with time-varying characteristics is investigated for the time-delayed nonlinear multi-agent systems against actuator attacks. A neural-network-based adaptive control method is constructed to achieve the desired control objective, which is outputs of the followers can complete the desired transformation of formation configuration. To eliminate the influence of malicious attacks on the actuators, an actuator attacks defense strategy is proposed to resist false data injection attacks occurred in the actuator. The uncertainty of the dynamics caused by nonlinear functions is resolved by the neural-network approximate method. The problem of the time delay is handled by an improved Lyapunov-Krasovskii functional approach, which can also avoid the singularity problem that may occur during the construction of the control method. Based on the Lyapunov stability theory, it is proved that all signals of closed-loop systems are semi-globally stable and the formation error can converge to a small neighborhood of the origin. Finally, the results of simulations are provided to verify the feasibility of the theoretical analysis and the effectiveness of the proposed control method.

Wave-to-grid (W2G) control of a wave energy converter

Grid integration of wave energy involves various power train stages from device to grid, such as a power take-off stage, a power conversion stage, and a power conditioning stage. The coupled performance of the complete wave-to-grid system depends heavily on the dynamics of each stage and their respective controllers. However, the control objectives of various stages may not align with each other and pose a potential problem, in terms of economic performance and grid integration. This study presents a complete wave-to-grid control approach for a wave energy converter, ensuring that the system performs optimally under variable wave resource conditions. The proposed system comprises a point absorber wave energy converter oscillating in heave, a linear permanent magnet generator, and back-to-back power converters for connection to the grid. Additionally, short term energy storage, based on an ultra-capacitor, is also added to the DC link between the back-to-back converters for power quality improvement. In the paper, a mathematical model is derived for the individual components of the wave-to-grid system. Then, the controllers for each stage of the power train are designed. A LiTe-Con controller is used for maximum power extraction on the device side, while Lyapunov-based nonlinear controllers are designed for power converter control in order to achieve the full range of control objectives. The result shows that the proposed controllers accomplish the desired control objectives and perform well under various operating conditions.

A simplified sliding‐mode control method for multi‐level transformerless DVR

Here, a finite-control-set sliding-mode control (FCS-SMC) method is proposed for single-phase three-level T-type inverter-based transformerless dynamic voltage restorers (TDVRs). The inherent advantages of SMC (fast dynamic response and high robustness) are combined with FCS concept to increase the effectiveness of the method in achieving the desired control objectives. First, the use of FCS eliminates the modulator requirement, which reduces the design complexity in the practical implementation. Second, an effective method based on charging/discharging conditions of DC capacitors is proposed for balancing capacitor voltages using relevant switching state rather than combining DC voltage error with the inductor current error through a suitable weighting factor in forming the cost function. Therefore, the weighting factor necessity in the control algorithm is eliminated. Finally, the steady-state error in the compensation voltage is eliminated by the proportional-resonant (PR) controller. The studied T-type inverter-based TDVR topology offers an alternative solution to the existing DVR topologies. Experimental results are carried out to demonstrate the effectiveness of the proposed FCS-SMC method in maintaining the load voltage at the desired level despite the voltage sags, swells, and distortions occurring in the grid voltage.

Recurrent Neural-Network-Based Model Predictive Control of a Plasma Etch Process

In this article, we propose the development of a recurrent neural network (RNN)-based model predictive controller (MPC) for a plasma etch process on a three-dimensional substrate using inductive coupled plasma (ICP) analysis. Specifically, the plasma etch process is simulated by a multiscale model: (1) A macroscopic fluid model is applied to simulate the gas flows and chemical reactions of plasma. (2) A kinetic Monte Carlo (kMC) model is applied to simulate the etching process on the substrate. Subsequently, proper orthogonal decomposition (POD) is used to derive the empirical eigenfunctions of the plasma model. Then the empirical eigenfunctions are utilized as basis functions within a Galerkin’s model reduction framework to compute a low-order system capturing dominant dynamics of the plasma model. Additionally, RNN is introduced to approximate dynamics of both the reduced-order plasma system and the microscopic etch process. The training data for the RNN models are generated from discrete sampling of open-loop simulations. A probability distribution function is also involved to present the stochastic characteristic of the kMC model. The trained RNN models are then implemented as the prediction model in the development of MPC to achieve desired control objectives. Closed-loop simulation results are presented to compare the performance of the model predictive controller and a proportional-integral (PI) controller, which show that the proposed MPC framework is effective and exhibits better performance than does a PI controller.

Adaptive output-feedback robust active disturbance rejection control for uncertain quadrotor with unknown disturbances

PurposeIn this research paper, an adaptive output-feedback robust active disturbance rejection control (RADRC) is designed for the multiple input multiple output (MIMO) quadrotor attitude model subject to unwanted uncertainties and disturbances (UUDs).Design/methodology/approachIn order to achieve the desired control objectives in the presence of UUDs, the low pass filter (LPF) and extended high gain observer (EHGO) methods are used for the estimation of matched and mismatched UUDs, respectively. Furthermore, for solving the chattering incurred in the standard sliding mode control (SMC), a multilayer sliding mode surface is constructed. For formulating the adaptive output-feedback RADRC algorithm, the EHGO, LPF and SMC schemes are combined using the separation principle.FindingsThe findings of this research work include the design of an adaptive output-feedback RADRC with the ability to negate the UUDs as well as estimate the unknown states of the quadrotor attitude model. In addition, the chattering problem is addressed by designing a modified SMC scheme based on the multilayer sliding mode surface obtained by utilizing the estimated state variables. This sliding mode surface is also used to obtain the adaptive criteria for the switching design gain parameters involved in the SMC. Moreover, the requirement of high design gain parameters in the EHGO is solved by combining it with the LPF.Originality/valueDesigning the flight control techniques while assuming that the state variables are available is a common practice. In addition, to obtain robustness, the SMC technique is widely used. However, in practice, the state variables might not be available due to unknown parameters and uncertainties, as well as the chattering due to SMC reduces the performances of the actuators. Hence, in this paper, an adaptive output-feedback RADRC technique is designed to solve the problems of UUDs and chattering.

Disturbance observer-based robust adaptive control for uncertain actuated nonlinear systemwith disturbances

PurposeThe aim of this research paper is to design a disturbance observer (DO)-based robust adaptive tracking control of uncertain nonlinear system subject to unknown nonlinear disturbance.Design/methodology/approachTo achieve desired control objectives, i.e. nonlinear trajectory tracking and disturbance attenuation, firstly, a control scheme is designed based on the adaptive criteria integrated in sliding mode control (SMC). In the second step, the disturbance estimation criterion is designed followed by patching with the controller obtained in the first step. Following the control development, using the Lyapunov candidate function, the stability criterion is ensured by designing appropriate adaptive gains.FindingsIn this paper, a robust adaptive nonlinear tracking method is presented. The findings includes the design of adaptive gains for the control parameters involved in the robust SMC technique, i.e. adaptive criterion is designed for the switching gain as well as for the gain used in sliding mode surface. Furthermore, a disturbance estimation criterion is developed to attenuate nonlinear disturbances with variable frequency and magnitude. Finally, the disturbance estimation scheme is combined with the control technique to obtain DO-based control (DOBC) algorithm.Practical implicationsSliding mode control is a powerful robust control method. And, combining it with the DO achieves the control objectives of plants subject to disturbances and uncertainties. However, usually the uncertainties and disturbances are unknown and time varying. Thus, during practical implementation, designing the standard SMC is a challenging task due to the constant gains involved in the control design. Hence, it is important to have a criterion which adapts to the varying dynamics of plants due to the uncertainties and disturbances for achieving practical implementation of the control system.Originality/valueSliding mode control has been widely used for achieving the desired control objectives and robustness in the close-loop nonlinear systems. Besides, the SMC technique has been combined with the DOs as well. However, mostly the ideal conditions were considered during these developments, which required the control gains to be designed simply by manual tuning appropriately. However, by considering the real-time dynamics, uncertainties and disturbances, the constant control gain criteria can fail. Furthermore, due to external and internal disturbances, the model plant can vary with time. Thus, it is important to design the adaptive criteria for the control gains in DOBC schemes.

Purcell’s Three-Link Swimmer: Assessment of Geometry and Gaits for Optimal Displacement and Efficiency

This paper studies the displacement and efficiency of a Purcell’s three-link microswimmer in low Reynolds number regime, capable of moving by the implementation of a motion primitive or gait. An optimization is accomplished attending to the geometry of the swimmer and the motion primitives, considering the shape of the gait and its amplitude. The objective is to find the geometry of the swimmer, amplitude and shape of the gaits which make optimal the displacement and efficiency, in both an individual way and combined (the last case will be referred to as multiobjective optimization). Three traditional gaits are compared with two primitives proposed by the authors and other three gaits recently defined in the literature. Results demonstrate that the highest displacement is obtained by the Tam and Hosoi optimal velocity gait, which also achieves the best efficiency in terms of energy consumption. The rectilinear and Tam and Hosoi optimal efficiency gaits are the second optimum primitives. Regarding the multiobjective optimization and considering the two criteria with the same weight, the optimum gaits turn out to be the rectilinear and Tam and Hosoi optimal efficiency gaits. Thus, the conclusions of this study can help designers to select, on the one hand, the best swimmer geometry for a desired motion primitive and, on the other, the optimal method of motion for trajectory tracking for such a kind of Purcell’s swimmers depending on the desired control objective.