Model Reference Adaptive Control Technique Research Articles

Maximum Power Point Tracking Improvement Using Model Reference Adaptive Control Scheme

This paper presents maximum power point tracking improvement using model reference adaptive control (MRAC) scheme. Solar photovoltaic (PV) energy system remains one of the renewable energy applications which operate by tracking energy from the sun and converting it into useful electrical energy. However, harnessing the generated energy has been the major concern of the engineers hence the adoption of various control strategies for efficient control of the generated power. The idea is to transfer the optimal generated power to the output considering the fact that PV energy produced may have been affected by irradiation and temperature conditions. To avoid the unstable output power, there is need to deploy an efficient control scheme. Most of the conventional control techniques are not optimal to resolve power inaccuracies in the system and they have shortcomings. This work is therefore aimed at analyzing solar photovoltaic energy system using MRAC technique to investigate the control performance. The results obtained by simulation on MATLAB/Simulink software, show an average tracking efficiency of 73.41%, the rise time of 0.7093s and a duty cycle of 0.7. Moreover, MRAC operates at small gain values and the range at which MRAC can operate was found to be 0.1 < 𝛤 < 2.

Framework of Maximum Solar Energy Conversion Using Lyapunov‐Based Model Reference Adaptive Control Technique in Stochastic Weather Conditions

The solar photovoltaic (PV) energy generation technology has grown rapidly. Herein, a novel continuous‐time Lyapunov‐based model reference adaptive control (LB‐MRAC) technique is suggested for determining the duty cycle of boost converter to maintain maximum power point (MPP) of the solar PV panel. For ensuring the fast‐tracking speed, the reference voltage of, respectively, MPP is calculated using perturb and observe (PO) and input to the MRAC technique. Using MATLAB/Simulink, the performances of the LB‐MRAC maximum power point tracking are comparatively analyzed with PO, incremental conductance, and adaptive neuro‐fuzzy inference system under diverse testing modes such as stand‐alone, partial shading, grid integrated, and real time. The proposed technique tracks MPP in just 3.7 ms having tracking efficacy between 99.15% and 99.59% under highly fluctuating irradiance and temperature with load uncertainties. In partial shading mode, four patterns (3 × 1 PV string) are devised and determined where that the suggested approach monitors the global MPP in merely 0.04 s with the highest power tracked and the least amount of shading loss. Next, the efficacy is checked in a three‐phase 50 kW grid‐integrated mode under realistic weather. Finally, the performance of the suggested continuous‐time LB‐MRAC technique is experimentally corroborated employing OPAL‐RT(OP4510) in the real‐time mode.

Optimizing the initial weights of a PID neural network controller for voltage stabilization of microgrids using a PEO-GA algorithm

This paper proposes an adaptive PID neural network (PIDNN) controller for direct and quadrature voltage control of three-phase inverters for islanded microgrids. A hybrid metaheuristic optimization algorithm is proposed for the initial weight selection of the proposed PIDNN controller using a discrete-time simulation model incorporating the nonlinearity of the insulated gate bipolar transistor (IGBT) or metal oxide semiconductor field effect transistor (MOSFET) switches of the inverter. The proposed hybrid optimization (HO) approach is a combination of population extremal optimization (PEO) and genetic algorithm (GA). It turned out that the proposed HO-PIDNN control scheme excelled the PEO, GA, and particle swarm optimization-based PIDNN controllers in terms of the objective function value, which is composed of the integral time absolute tracking error of the output voltage and a chattering penalty factor. Also, the control system outperformed the model reference adaptive PID control technique from the literature.

Towards Steering a High-Dose Rate Brachytherapy Needle With a Robotic Steerable Stylet

Steerable needles are desired for many applications within the medical field. One such application is high-dose rate (HDR) brachytherapy. In this paper, an adaptive control system is proposed to provide control of the planar bending angle or deflection distance of a tendon-driven continuum joint, which can be inserted as the stylet in a commercial hollow needle for HDR brachytherapy. Proportional–integral–derivative (PID) controllers are a common method of control. However, significant tuning may be required to achieve the desired dynamic response, and the tuned gains may not achieve the desired response in a different environment or if the system model varies. Steerable needles must navigate through tissues of differing viscoelastic properties and the use of nitinol for the joint and tendons of a steerable needle leads to change in the system parameters due to change in the phase of the nitinol material. In this article, we propose the real-time adaptation of PID gains using model reference adaptive control techniques to control the planar bending angle or deflection distance of a steerable stylet and needle system.

Design and implementation of a new adaptive MPPT controller for solar PV systems

This research provides an adaptive control design in a photovoltaic system (PV) for maximum power point tracking (MPPT). In the PV system, MPPT strategies are used to deliver the maximum available power to the load under solar radiation and atmospheric temperature changes. This article presents a new adaptive control framework to enhance the performance of MPPT, which will minimize the complexity in system control and efficiently manage uncertainties and disruptions in the environment and PV system. Here, the MPPT algorithm is decoupled with model reference adaptive control (MRAC) techniques, and the system gains MPPT with overall system stability. The simulation and design of the new MRAC for MPPT based on a boost converter are addressed here. Moreover, a mathematical model is formulated and an efficient MRAC is designed for MPPT. To validate the robustness of the controller, MATLAB/Simulink is utilized to compare with the state-of-the-art approach, which is incremental conductance (INC) and perturb & observe (P&O) under various operating conditions based on the convergence time, tracking efficiency, PV current & voltage ripple, overall efficiency, and error rates. The proposed controller’s average tracking efficiency is 99.77% and 99.69% under diverse temperature and radiation conditions, respectively. In addition, it takes only 3.6 msec to capture MPP, which is around ten times faster than INC and twelve times faster than the P&O approach. When compared to INC and P&O, the MPP error rates in the MRAC-MPPT scheme are significantly lower. The simulation outcomes indicate that the presented controller exhibits excellent tracking under varying circumstances like solar radiation and temperature.

Partial State Feedback Adaptive Disturbance Rejection Control

A unified disturbance rejection problem based on model reference adaptive control (MRAC) technique is addressed. When the full-state vector is not operational in feedback, we use partial-state signal y(t) to design a disturbance rejection controller. The parametrization and disturbance compensation techniques are necessary for designing adaptive control strategy to deal with systems uncertainties and unknown disturbances. This scheme guarantees the desired system performances, including closed-loop system stability as well as asymptotic output tracking. The proof progress of system stability is elaborated.

Adaptive output‐feedback resilient tracking control using virtual closed‐loop reference model for cyber‐physical systems with false data injection attacks

AbstractThis work studies output‐feedback resilient tracking control problems in cyber‐physical systems (CPSs) with false data injection attacks via closed‐loop model‐reference adaptive control (MRAC) techniques. The control input signals in CPSs, directly and indirectly influenced by sensor and actuator false data injection attacks, could degrade the system's control performance seriously. Then a virtual closed‐loop reference model is used as a mediator between the open‐loop reference and actual systems to improve the ability to suppress attacks, and by the MRAC techniques, a novel adaptive output‐feedback resilient tracking control scheme is proposed to ensure the reliability of the attacked systems. Different from the existing results, (a) the new one is with an adaptive attack compensator in a time‐varying gain output‐feedback form, which can automatically eliminate the impact of matched attacks by online adjusting the gain; and (b) the ‐gain rejection property of mismatched attacks is achieved by the virtual closed‐loop reference. Finally, an illustrative example validates the developed method.

Performance enhancement of grid‐connected photovoltaic systems using Ant Lion optimisation and Genetic Algorithm‐based optimisation techniques

One of the most common issues in renewable energy applications is modelling and regulating grid-connected PV systems. The model reference adaptive control (MRAC) technique has been analysed in this paper. It's difficult to develop an MRAC that performs well in both transient and steady-state conditions; hence, the primary goal of this paper is to create a modified MRAC that has excellent steady-state and transient responses. A modified MRAC scheme has been proposed, where an Ant lion optimisation technique and a well-known algorithm that is Genetic Algorithm (GA) is used to optimise the controller gain and compared it with each other and also with the conventional MRAC technique. The optimisation techniques are used to tune the controller parameters so that errors should be minimised. The time response analysis of MRAC, MRAC-GA, and MRAC-ALO controllers are shown and evaluated using common control criteria such as rising time, settling time, peak overshoot, steady-state, and error parameters. The simulation findings reveal that the proposed modified MRAC controller, both MRAC-ALO and MRAC-GA, outperforms in relation to transient performance and steady-state performance compared to conventional MRAC controllers.

Model reference adaptive control for aero-engine based on system equilibrium manifold expansion model

The role of control system for aero-engine is to provide sufficient thrust and maintain physical and operational limits. Model reference adaptive control (MRAC) technique has been paid more attention for advantages in eliminating the effects of uncertainties arising from nonlinearity or component degradation and satisfying both safety and performance requirements in the entire flight envelope. However, due to the accuracy of aero-engine models, the implementation of the MRAC algorithm is challenging. In this paper, an MRAC architecture based on equilibrium manifold expansion (EME) model for an aero-engine is presented. An EME model is taken as the reference model in this architecture, which has high accuracy in the aero-engine modelling and contains both dynamic and steady-state characteristics of aero-engine. By using Lyapunov stability method, an MRAC based on EME model is designed and used to control the aero-engine obtaining desired performance. In the designed controller, uncertainty arising from component degradation of the aero-engine is estimated and the values of the uncertainty are replaced by the estimator. In order to indicate the control performance of the method provided in this article, the proposed MRAC architecture has been tested on a hardware-in-loop simulation platform, the simulation results have shown that compared the control performance with other traditional controllers, the aero-engine could quickly provide the desired control requirements without steady-state errors under the control of designed MRAC.

Two-Degree-of-Freedom based Model Reference Adaptive Control of Non-minimum Phase System

This paper presents the design of the Two-Degree-of-Freedom (2DOF) based Model Reference Adaptive Control (MRAC) as a tracking control system for the Non-minimum Phase (NMP) system. Perfect set point tracking is possible by using a cascade-connected inverse and non-inverse transfer function model of the dynamic system. But this method is restricted to the minimum phase system only, whereas, NMP system produces unbounded output response using this inverse model technique. Though, adaptive inverse feed-forward control technique is an effective tracking control system, it also requires an inverse model of the system. To overcome this problem, a novel feed-forward MRAC scheme for the inverse NMP model and the State feedback controller of the non-inverse model has been proposed, and both the controller are decoupled in a 2DOF framework. The feed-forward control technique guaranteed the tracking performance using the Lyapunov stability theory, and the feedback counterpart enhances the steady-state performance of the NMP plant. The robustness of the asymptotic stability as well as tracking performance of the closed-loop NMP system has been ensured by the proposed 2DOF control based MRAC technique. The effectiveness of the proposed control structure initially has been verified by the MATLAB Simulink toolbox and, then the demand has been strengthened by the implementation of the same control logic in the real-time environment using analog simulation with Op-amp based hardware model.

Nonlinear SDRE based adaptive fuzzy control approach for age-specific drug delivery in mixed chemotherapy and immunotherapy

Determination of a near-optimal personalized drug delivery scenario in mixed chemotherapy and immunotherapy based on the age of cancer patients was presented in this paper. For this purpose, a mathematical model for cancer dynamics is considered in the form of ordinary differential equations (ODE) which contains cancer cells, tumor cells, and chemotherapy drug intervention. This model is modified by adding immunotherapy intervention effects to alter cancer dynamics. We consider two patients with known and unknown model parameters which are mentioned as a reference and unknown patients respectively. The nonlinear composite adaptive controller is introduced by compounding State Dependent Riccati Equation (SDRE) and Model Reference Adaptive Control (MRAC) techniques for achieving the drug delivery of an unknown patient. The drug delivery scenario for a reference patient with a known mathematical model and parameters is determined via the SDRE technique and then for any unknown patient, the personalized mixed therapy protocol is achieved using the treatment regimen of the reference patient as a reference model in MRAC. To reduce the side effects of chemotherapy drugs, we determine drug maximum dose limits using fuzzy control by considering the age of cancer patients. We regarded patients in four age groups of child, young, middle-aged, and old. In the proposed methodology, unknown patients are considered as a black-box simulator and the mathematical model parameters of the patient are not essential for the design of drug administration protocol and we only need patients’ age. After chemotherapy, the treatment is completed by immunotherapy to avoid cancer relapse. The effect of reference and unknown patient ages in obtaining drug delivery protocol is deeply surveyed. Numerical simulations confirm the effectiveness, robustness, and flexibility of the proposed strategy for patients of different ages. The Proposed algorithm can be an effective tool to aid oncologists in prescribing age-specific optimal treatment protocols for cancer patients.

Loop Control and Tuning in Distributed Control System Using Fuzzy Controller (Dept.E)

In this paper, the designed schemes for two fuzzey controllers, employing the scaling factor tuning, are p roposed. The first fuzzy logic controller is a normalized controller used to control the system. The tuning for the input and output-scaling factors of the first one is done through (le second fuzzy controller (the supervisory controller). This combination is used to ap propriately determine the control signal of the process. The supervisory fuzzy controller tunes the normalized fuzzy controller based on the model reference adaptive control technique. The great advantage of the proposed method is that, a supervisor as a fuzzy controller to tune the scaling factor of a normalized fuzzy controller can be used to supervise many control systems. The simplicity and modular structure of the controller makes it is more suitable to be applied to control most control loops in the distributed control systems (DCS). The normalized fuzzy controller and the supervisory fuzzy controller are organized with specific experience information about the controlled systems. The proposed (fuzzy controllers are applied experimentally to control an experimental p rocess, which simulate an LPG p rocess. The proposed controller is applied to two different control 100ps, temperature and level, where the controller gains are selected based on the process conditions and limitations. A comparison among scaling factor manual tuning, supervisor fuzzy and conventional adaptive fuzzy controller is done to verify the effectiveness of the proposed design. The results show that in the last case the system is forced to follow the desired response.

Active fault-diagnosis method using adaptive allocator and fault-tolerant adaptive control system design

A new approach to the active fault diagnosis (AFD) for input redundant plants is presented in this paper. A test signal which helps the diagnosis is injected to the plant in addition to nominal control input in the AFD typically. One feature of the proposed AFD is adaptive allocation of the test signal. The adaptive allocator which distributes the test signal and injects it to the redundant actuators is introduced in this paper. A fault-diagnosis (FD) system that estimates fault location and its magnitude from adaptive parameter of the adaptive allocator is constructed with a simple neural network (NN) model. Furthermore, a A fault-tolerant adaptive control system which includes the adaptive test signal allocator is designed based on the model reference adaptive control (MRAC) technique. The adaptive laws of the adaptive allocator and controller are derived by using a suitable Lyapunov function. By performing experiments using a two-input redundant plant, we show the effectiveness and applicability of the proposed AFD and MRAC system.

Robust Adaptive Control of a Multirotor with an Unknown Suspended Payload

Abstract This paper addresses the problem of a multirotor carrying an unknown suspended payload, allowed to swing in one axis. The payload is unknown in the sense that its parameters, such as the payload mass and cable length, are unknown and its state, the swing angles, are not available for measurement. The suspended payload alters the flight dynamics of the vehicle considerably and the flight controllers need to minimize this effect. A robust model reference adaptive control technique is designed and implemented to minimize the effect of the suspended payload on the vehicle while allowing the controller to adapt to account for the unknown payload. The controller is modified to reject external disturbances and to be robust in the presence of sensor noise and drift. In simulation, this technique proves to dampen the oscillations caused by the payload. A quadrotor was built to practically demonstrate the effectiveness of the controller. The PX4 flight control stack is considered for the firmware of the vehicle. The model reference adaptive controller was implemented and succeeded to dampen the oscillations caused by the payload in a practical flight.

Sliding Mode-Based Fuzzy Model Reference Adaptive Control Technique for an Unstable System

A comparative performance analysis of two adaptive control techniques for an unstable and disturbed system is presented. The first technique is model reference adaptive control (MRAC) that is based on Lyapunov theory, and second strategy is a combination of sliding mode (SM) and fuzzy logic theories and is known as sliding mode fuzzy model reference adaptive control (SM-FMRAC). In these schemes, asymptotic tracking of three reference models with different settling times and the different damping conditions is achieved. The expected advantage of the proposed SM-FMRAC is to improve the performance specifications over the MRAC technique.

Concurrent Learning-Based Global Exponential Tracking Control of Uncertain Switched Systems With Mode-Dependent Average Dwell Time

This paper investigates the problem of global exponential tracking control for switched nonlinear systems with linear uncertain parameters and without persistency of excitation. A switched model reference adaptive control technique, using the mode-dependent average dwell time method and a concurrent learning approach, is proposed for the first time. A sufficient condition is provided to ensure that the dynamics of the state tracking error and the adaptive weight error converge to zero exponentially rapidly. Consequently, the relationship among the average dwell time of each subsystem, the concurrent learning algorithm, and the performance of uncertain switched systems is established. Furthermore, the transient performance bounds of the state tracking error and the adaptive weight error are studied. Finally, an illustrative numerical example is provided to demonstrate the effectiveness of the proposed method.

Comparison between model reference discrete time indirect and direct adaptive controls

The discrete time Model Reference Adaptive Control (MRAC) techniques have some important properties. They can be used successfully to the control of different type of Single Input and Single Output (SISO) systems: Linear Time Invariant (LTI) systems with unknown parameters and Linear Parameter Varying (LPV) systems. This method can be considered also as a nonlinear control design, where the system parameters will be updated in each iteration.

Robust Control Allocation for Spacecraft Attitude Tracking Under Actuator Faults

This brief addresses attitude tracking problems for an over-actuated spacecraft in the presence of actuator faults, imprecise fault estimation, and external disturbances. First, a model reference adaptive control technique is used to design a high-level controller to produce the three-axis virtual control torque. Then, taking fault estimation uncertainties into account, a robust control allocation (RobCA) strategy is proposed to redistribute virtual control signals to the remaining actuators when an actuator fault occurs. The RobCA is formulated as a min–max optimization problem, which deals with actuator faults directly without reconfiguring the controller and ensures some robustness of system performances. Finally, simulation results are provided to show the effectiveness of the overall control strategy.

Dynamic Rig for Validation of Control Algorithms at High Angles of Attack

The dynamics of a modern aircraft at high angles of attack is complicated, with hazardous phenomena such as wing rock, stall, and spin. The paper presents a technique for cost-effective and safe studying of conventional and critical flight regimes and control validation using an autonomous scaled aircraft model mounted in a three-degree-of-freedom gimbal in a wind tunnel. The similarity of the dynamics at high angles of attack of the wind-tunnel model and the free-flying model is demonstrated. To suppress the wing rock and to prevent the stall, two control laws are designed using linear matrix inequalities and model reference adaptive control techniques. The controllers are tested in a semifree flight of the autonomous scaled model in the wind tunnel. Wing rock suppression and stall and spin prevention are demonstrated.

Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models

In this paper, a multiple model adaptive control (MMAC) method is proposed for a gas turbine engine. The model of a twin spool turbo-shaft engine driving a variable pitch propeller includes various operating points. Variations in fuel flow and propeller pitch inputs produce different operating conditions which force the controller to be adopted rapidly. Important operating points are three idle, cruise and full thrust cases for the entire flight envelope. A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed. Also, stability analysis using Lyapunov method is presented. The proposed method is compared with two conventional first level adaptation and model reference adaptive control techniques. Simulation results for JetCat SPT5 turbo-shaft engine demonstrate the performance and fidelity of the proposed method.