Set-point Regulation Research Articles

Flexible energy utilization potential of demand response oriented photovoltaic direct-driven air-conditioning system with energy storage

The surge in air conditioning electricity consumption exacerbates grid peak load. To counteract grid peaking pressures and accommodate a high penetration rate of renewable energy, a photovoltaic direct-driven air-conditioning system (PVACS) integrated with energy storage was suggested. The power response characteristics of the air conditioner based on indoor temperature set-point regulation were clarified with an on-site test. The test results indicated a significant flexible energy utilization potential through intelligent temperature management. Whereas, its flexibility is limited by high ambient temperature or inadequate air conditioner capacity. Then, the effects of demand response were simulated, aiming to achieve the synchronization between air conditioner power and photovoltaic (PV) power. Simulation results showed that the demand response strategies effectively reduced peak power by 45.2% and increased self sufficiency ratio and self consumption ratio to 0.83 and 0.91, respectively. Moreover, the role of short-term electricity energy storage was considered for further promoting solar energy accommodation and grid peak shaving. Optimized energy storage further reduced peak power by more than 23.4%, while mitigating the temporal supple-demand mismatch.

Robust adaptive predictive digital controller with integral action for set-point regulation of saturated uncertain systems

A robust adaptive digital controller is systematically investigated on set-point regulation in uncertain systems under input saturation. So, an optimization strategy will be derived to solve the reference regulation robustly in the discrete-time systems. To this purpose, a discrete-time integral controller is considered through a fixed state-feedback structure. Then, its coefficients would be numerically quantified at each sample time by means of a minimization. The formulation and merit of the addressed predictive control method are theoretically proved in the paper. Then the control technique is simulated in a discrete-time equation. Additionally, some numerical results are executed to declare the remarkability and vigor of the recommended algorithm against uncertainties, actuator saturation, and disturbance in contrast to the existing control ways.

Direct Current Motor Velocity Control With Integral Retarded Controller Under Unintentional Delay

Abstract Delay-based controllers have been recently revisited over proportional-integral-derivative (PID) controllers, with promising analytical tuning formulae and capabilities to effectively control plants with noisy measurements even without low-pass filters. One such controller is the integral retarded (IR) controller, which utilizes an intentional delay to reduce actuator chattering against noisy measurements while its integral part achieves zero steady-state error for set-point regulation of Type-0 open-loop systems. However, measurements can be unintentionally delayed in many applications. The integral retarded framework for such cases has not been studied in the literature. Here, we provide new analytical tuning rules of the controller in such cases, as well as validations through simulations and a direct current (DC) motor velocity control hardware experiment.

PSO and NNPC-based integrative control allocation for dynamic positioning ships with thruster constraints

In order to overcome the issues of thrust saturation, uneven energy consumption and suboptimal control associated with the traditional hierarchical control strategy employed in ship dynamic positioning (DP) systems, an integrative control allocation strategy is proposed by combining the low-level thrust allocation into the high-level motion controller. The proposed scheme is implemented via the neural network-based nonlinear predictive control (NNPC) and solved via an improved particle swarm optimization (PSO) algorithm. A data-driven recurrent neural network (RNN) ship motion and thrust allocation model is off-line identified based only on the current and historical measured output and thrust input data. Utilizing the established RNN model to predict the future multi-step outputs, and combining the motion control, the thruster energy consumption and the thruster wear and tear as an integrative control objective, the integrated NNPC online optimization problem with thruster constraints is established and solved by an improved evolution algorithm of PSO. Simulations are respectively carried out for setpoint regulation and trajectory tracking. Simulation results well demonstrate the feasibility and superiority of the proposed control strategy.

Stabilization of Physical Systems via Saturated Controllers With Partial State Measurements

This article provides a constructive passivity-based control (PBC) approach to solve the set-point regulation problem for input-affine continuous nonlinear systems while considering bounded inputs. As customary in PBC, the methodology consists of two steps: energy shaping and damping injection. In terms of applicability, the proposed controllers have two advantages concerning other PBC techniques: 1) the energy shaping is carried out without solving partial differential equations and 2) the damping injection is performed without measuring the passive output. As a result, the proposed methodology is suitable to control a broad range of physical systems, e.g., mechanical, electrical, and electromechanical systems, with saturated control signals. We illustrate the applicability of the technique by designing controllers for systems in different physical domains, where we validate the analytical results via simulations and experiments.

Robust fixed-time trajectory tracking control of the dynamic positioning ship with actuator saturation

This paper addresses the fixed-time trajectory tracking control and setpoint regulation problems for dynamic positioning (DP) ships subject to unknown time-varying disturbances, system uncertainties, and actuator saturation. To meet the resistance of the low-frequency disturbances and system uncertainties, a continuous fixed-time disturbance observer (FTDO) based on integral sliding mode and fixed-time control is constructed. A nonlinear auxiliary dynamic system is introduced to mitigate the inconsistency between command forces and actual control forces, in which the feedback of the bias Δτ guarantees the input constraints for the design of the proposed controller. The explosion-of-complexity problem inherent in traditional backstepping is addressed based on a nonlinear first-order filter. Furthermore, a robust fixed-time control law with constraints is developed to track the predefined trajectory and the desired setpoint. Theoretical analyses demonstrate the fixed-time stability of the closed-loop system via Lyapunov analysis. Numerical simulation results illustrate the effectiveness and robustness of the proposed control approach.

Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones

In critical healthcare units, such as operation theaters and intensive care units, healthcare workers require specific temperature environments at different stages of an operation, which depends upon the condition of the patient and the requirements of the surgical procedures. Therefore, the need for a dynamically controlled temperature environment and the availability of the required heating/cooling electric power is relatively more necessary for the provision of a better healthcare environment as compared to other commercial and residential buildings, where only comfortable room temperature is required. In order to establish a dynamic temperature zone, a setpoint regulator is required that can control the zone temperature with a fast dynamic response, little overshoot, and a low settling time. Thus, two zone temperature regulators have been proposed in this article, including double integral sliding mode control (DISMC) and integral terminal sliding mode control (ITSMC). A realistic scenario of a hospital operation theater is considered for evaluating their responses and performance to desired temperature setpoints. The performance analysis and superiority of the proposed controllers have been established by comparison with an already installed Johnson temperature controller (JTC) for various time spans and specific environmental conditions that require setpoints based on doctors’ and patients’ desires. The proposed controllers showed minimal overshoot and a fast settling response, making them ideal controllers for operation theater (OT) zone temperature control.

Koopman Model Predictive Control of an Integrated Thermal Management System for Electric Vehicles

AbstractThis paper is concerned with energy efficient operation of an integral thermal management system (ITMS) for electric vehicles using a nonlinear model predictive control (MPC). Driven by a heat pump (HP), this ITMS can handle battery thermal management (BTM) while serving the need for cabin cooling or heating need. The objectives of the ITMS MPC control strategy include minimization of power consumption and achieving temperature setpoint regulation for the battery and cabin space based on predictive information of traction power and cabin thermal load. The control design is facilitated by a gray-box modeling framework, in which the nonlinear dynamics of HP subsystem are characterized with a data-driven Koopman subspace model, while the BTM subsystem dynamic is a bilinear physics-based model. The computational efficiency of the proposed MPC framework is improved with two aspects of convexification for the underlying receding-horizon constrained optimization problem: the Koopman-operator lifting and the McCormick envelopes implemented for handling the bilinear dynamics. The proposed control method is evaluated with simulation study, by developing a Modelica-Python cosimulation platform via the functional mockup interface (FMI), where the electric vehicle (EV)-ITMS plant is modeled in Modelica with Dymola and the MPC design is implemented in Python. By benchmarking against a recurrent-neural-networks (RNN) model based nonlinear MPC, the simulation results validate the effectiveness and improved computational efficiency of the proposed method.

Contractivity-based variable gain dynamic motion control for a laser beam steering system: Synthesis and performance analysis

This paper deals with the synthesis and experimental performance evaluation of a contractivity-based nonlinear dynamic motion control scheme for a Laser-Beam Steering (LBS) system, which includes a saturated integral action and a variable gain. The variable gain, in the control law, is used to discriminate between “signal” and “noise” in the velocity measurements, allowing to do a trade-off between the low-frequency tracking and disturbance rejection properties and high-frequency measurement noise amplification, an effect known as waterbed effect. Then, the contractivity-based framework handles the stabilization problem together with the closed-loop performance, allowing one to generalize key properties of linear control systems to analyze transient and steady-state solutions performances in the nonlinear case. The proposed control scheme is evaluated on an experimental platform for the set-point regulation and trajectory tracking problems under different scenarios. Moreover, the effectiveness of the proposed control scheme is compared with linear controllers for the LBS system available in the literature.

Artificial homeostatic temperature regulation via bio-inspired feedback mechanisms

Homeostasis comprises one of the main features of living organisms that enables their robust functioning by adapting to environmental changes. In particular, thermoregulation, as an instance of homeostatic behavior, allows mammals to maintain stable internal temperature with tightly controlled self-regulation independent of external temperatures. This is made by a proper reaction of the thermoeffectors (like skin blood vessels, brown adipose tissue (BAT), etc.) on a wide range of temperature perturbations that reflect themselves in the thermosensitive neurons’ activity. This activity is being delivered to the respective actuation points and translated into thermoeffectors’ actions, which bring the temperature of the organism to the desired level, called a set-point. However, it is still an open question whether these mechanisms can be implemented in an analog electronic device: both on a system theoretical and a hardware level. In this paper, we transfer this control loop into a real electric circuit by designing an analog electronic device for temperature regulation that works following bio-inspired principles. In particular, we construct a simplified single-effector regulation system and show how spiking trains of thermosensitive artificial neurons can be processed to realize an efficient feedback mechanism for the stabilization of the a priori unknown but system-inherent set-point. We also demonstrate that particular values of the set-point and its stability properties result from the interplay between the feedback control gain and activity patterns of thermosensitive artificial neurons, for which, on the one hand, the neuronal interconnections are generally not necessary. On the other hand, we show that such connections can be beneficial for the set-point regulation and hypothesize that the synaptic plasticity in real thermosensitive neuronal ensembles can play a role of an additional control layer empowering the robustness of thermoregulation. The electronic realization of temperature regulation proposed in this paper might be of interest for neuromorphic circuits which are bioinspired by taking the basal principle of homeostasis on board. In this way, a fundamental building block of life would be transferred to electronics and become a milestone for the future of neuromorphic engineering.

Robust Global Asymptotic Stabilization of Linear Cascaded Systems With Hysteretic Interconnection

We address the problem of setpoint regulation for cascaded minimum-phase linear systems interconnected through a scalar hysteresis, modeled as a Prandtl-Ishlinskii operator. Employing well-posed constrained differential inclusions to represent the hysteretic dynamics, we formulate the control problem in terms of stabilization of a compact set of equilibria depending on the hysteresis states. For our design, we firstly consider a proportional-integral controller for linear systems with hysteretic input, and provide model-free sufficient conditions based on high-gain arguments for closed-loop stability. Then, the controller is dynamically extended to obtain an inversion-free stabilizer of the overall cascade. For the presented schemes, we prove robust global asymptotic stability of a compact set that ensures setpoint regulation, regardless of the hysteresis states.

Finite-Dimensional Observer-Based PI Regulation Control of a Reaction–Diffusion Equation

This article investigates the output feedback setpoint regulation control of a reaction–diffusion equation by means of boundary control. The considered reaction–diffusion plant may be open-loop unstable. The proposed control strategy consists of the coupling of a finite-dimensional observer and a PI controller in order to achieve the boundary setpoint regulation control of various system outputs such as the Dirichlet and Neumann traces. In this context, it is shown that the order of the finite-dimensional observer can always be selected large enough, with an explicit criterion, to achieve both the stabilization of the plant and the setpoint regulation of the system output.

Design and Testing of a Pid Controller on a Parrot Minidrone

This paper focuses on quadcopter control design and uses the Simulink Support Package for Parrot Minidrones to test various control approaches. This project's goal is to bridge the knowledge gap between control design and implementation, making it simpler to comprehend the fundamental ideas of control theory. Obtaining a dynamic model of the quadcopter, linearizing the system around an equilibrium point, designing PID controller on the linearized system, and then verifying the controllers on the non-linear model using simulations and test flights with the actual drone are the key components of this research. The tuned controllers are validated by trajectory tracking, setpoint regulation, and disturbance rejection.

Design and Testing of a Pid Controller on a Parrot Minidrone

This paper focuses on quadcopter control design and uses the Simulink Support Package for Parrot Minidrones to test various control approaches. This project's goal is to bridge the knowledge gap between control design and implementation, making it simpler to comprehend the fundamental ideas of control theory. Obtaining a dynamic model of the quadcopter, linearizing the system around an equilibrium point, designing PID controller on the linearized system, and then verifying the controllers on the non-linear model using simulations and test flights with the actual drone are the key components of this research. The tuned controllers are validated by trajectory tracking, setpoint regulation, and disturbance rejection.

Event-Triggered Adaptive Fuzzy Setpoint Regulation of Surface Vessels With Unmeasured Velocities Under Thruster Saturation Constraints

This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel’s uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel’s thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme.

TUMOR VOLUME GROWTH CONTROL USING BIFURCATION APPROACH

A tumor growth system with immune response and chemotherapy is put in a nonlinear dynamical system whose solutions are relative to the initial data. This study presents a phase space analysis of the system. Here, the basin of equilibrium points attraction is determined for a particular class of systems and is subjected to input and state constraints in which all points in phase space would be close to the equilibrium points according to the region of attraction it starts. The addition of a drug term to the system can move the solution trajectory to the desirable basin of attraction. The proposed method gives static output feedback controllers that guarantee the convergence of the generic solutions. Although such a set-point regulation problem is too challenging for general nonlinear systems, the standard surface is found by the proposed approach, which is called separatrix for the controller. This criterion of separating border can perform well even when the mentioned system has limited change parameters. The control is set by separatrix in which the output feedback controller therapy can take all solutions to the healthy state through a constrained chemotherapy protocol. Moreover, this protocol can enable globalization of healthy equilibria.

Robust [formula omitted] integral controller design for regulation problem of uncertain nonlinear systems with non-zero set-point

This paper addresses a robust H∞ integral control scheme for a class of nonlinear uncertain systems. Hence, disturbance rejection and set-point regulation are the main goals in the proposed method, and a state-feedback control design would be employed to satisfy the mentioned aims. Then, a linear matrix inequality (LMI) based procedure would be introduced to the H∞ controller synthesis. Consequently, a matrix transformation is presented to translate the controller design issue into an LMI feasibility checking problem. Thus, the controller’s gains are numerically calculated by a solution of LMI approach. To show the applicability of the method, the results are used in a numerical example. Comparing the disturbance attenuations and transient performance in none-zero regulation problem, the outcomes verify the efficiency of the proposed technique over the other method.

Dither extremum seeking control of a variable refrigerant flow system with equality constraint handling

In this paper, we apply an extremum-seeking control strategy to a variable refrigerant flow (VRF) system, where the total power consumption is minimized by adjusting the compressor discharge pressure setpoint and the outdoor unit fan mass flow rate, incorporating constraints on the zonal temperature setpoint regulation. A simulation study is conducted using a Modelica dynamic simulation model of a VRF system with one outdoor unit and four indoor units. Simulation results validate the effectiveness of the proposed method of power minimization with the constraint of zone-temperature regulation. The ESC strategy incorporates online penalty-weight estimation and loop-gain adaption, which are shown to improve convergence.

Event-Based, Intermittent, Discrete Adaptive Control for Speed Regulation of Artificial Legs

For artificial legs that are used in legged robots, exoskeletons, and prostheses, it suffices to achieve velocity regulation at a few key instants of swing rather than tight trajectory tracking. Here, we advertise an event-based, intermittent, discrete controller to enable set-point regulation for problems that are traditionally posed as trajectory following. We measure the system state at prior-chosen instants known as events (e.g., vertically downward position), and we turn on the controller intermittently based on the regulation errors at the set point. The controller is truly discrete, as these measurements and controls occur at the time scale of the system to be controlled. To enable set-point regulation in the presence of uncertainty, we use the errors to tune the model parameters. We demonstrate the method in the velocity control of an artificial leg, a simple pendulum, with up to 50% mass uncertainty. Starting with a 100% regulation error, we achieve velocity regulation of up to 10% in about five swings with only one measurement per swing.

Integral action for setpoint regulation control of a reaction–diffusion equation in the presence of a state delay

This paper is concerned with the regulation control of a one-dimensional reaction–diffusion equation in the presence of a state-delay in the reaction term. The objective is to achieve the PI regulation of the right Dirichlet trace with a command selected as the left Dirichlet trace. The control design strategy consists of the design of a PI controller on a finite dimensional truncated model obtained by spectral reduction. By an adequate selection of the number of modes of the original infinite-dimensional system, we show that the proposed control design procedure achieves both the exponential stabilization of the original infinite-dimensional system as well as the setpoint regulation of the right Dirichlet trace.