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In this study, a distributed model reference adaptive control architecture is developed to achieve the cooperative tracking of uncertain dynamical multi-agent systems, where the reference model serves as a virtual leader for the group to track. Two adaptive laws, with one adjusting the coupling weights and the other adjusting the neural network weights, are designed based on the relative state information of neighbouring agents. The proposed controller guarantees that the state of each agent synchronizes to that of the reference model over any undirected connected communication graphs, and all signals in the closed-loop network are uniformly ultimately bounded. In contrast to the existing results, the developed controller can be implemented in a fully distributed manner by each agent without using any global information and the accurate model of each agent. An extension to asymptotic stability is further studied.

The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system's maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.

This work introduces the application of a new adaptive control structure, which is a modification of the robust model reference adaptive controller (RMRAC) and adaptive super-twisting sliding mode (ASTSM). This controller was previously proposed in the literature and applied to the current control of a grid-connected converter under uncertain grid environments. However, its STSM structure used the tracking error signal function as a sliding surface, which tends to impose considerable chattering in the system. The proposed controller maintains the characteristics of the known structure but replaces the signal function in the STSM equations with a sigmoid function, reducing the current tracking error and improving the system regulation since it is smoother. As the control structure lies in RMRAC theory and some core equations change, stability analysis of the adaptation algorithm is also carried out. Experimental results in a 7.5 kW converter are presented in which the known RMRAC-ASTSM controller presents 2.45% total harmonic distortion while the modified adaptive structure obtains 2.22% total harmonic distortion and better regulation performance.

A novel and extremely effective fuzzy Model Reference Adaptive Control (MRAC) based on Maximum Power Point Tracking (MPPT) with a boost converter is presented in this study. It goes into detail on the adaptive gain selection procedure and MRAC design. The paper proposes a simplified fuzzy MRAC process and describes the adaptation gains adjustment using a Fuzzy Logic (FL) subsystem. To test the durability and flexibility of the suggested approach, extensive simulations in MATLAB/Simulink are conducted, considering a variety of scenarios and environmental variables. Findings demonstrate the extreme robustness of the MRAC-Fuzzy MPPT control, with up to 99.98% tracking efficiency. It also keeps the photovoltaic systems operating at or near the Maximum Power Point (MPP), effectively reducing oscillations, improving energy efficiency, and boosting power production.

The well known back-propagation algorithm has revolutionized machine learning and artificial intelligence, particularly in neural network applications. Although gradient descent-based algorithms are utilized in control applications, they are not as prevalent as in neural network applications. This discrepancy can be attributed to the successful development of various adaptation laws which ensure system stability while meeting the required design criteria. Many of these laws can be found in model reference adaptive control (MRAC) and adaptive sliding mode control (ASMC). This paper investigates the applicability of the Brandt-Lin (B-L) learning algorithm, mathematically equivalent to the back-propagation algorithm, in adaptive control applications. We find that combining the B-L learning algorithm with SMC yields a robust controller suitable for model reference adaptive sliding mode control (MRA-SMC). The controller is applicable to linear and a class of nonlinear dynamic systems and is suitable for efficient implementation. We derive the stability criteria for this controller and conduct simulations to study the adaptation’s impact on chattering. Our work exemplifies one approach to adopt the back-propagation algorithm in control applications.

This manuscript suggests an advanced high-step-up-gain Luo converter-fed brushless DC motor (BLDCM) drive for enhancing the power quality by using artificial neural network (ANN)-based model reference adaptive control algorithm. To overcome these issues, designing a competent intelligent controller by using an effective design approach is essential to utilize the brushless DC motor (DCM) to its maximum potential. The model reference adaptive controller system has a parameter modification tool added with the regular feedback loop which accord better results when there are modifications in process parameters. The ANN is significant tool for reckoning and controlling system, with the key factors of intrinsic parallelism, fault tolerance and learning fitness. To address the issue of adapting the nonlinearity, parameters and loads in BLDC motor drive, an integration of the MRAS with ANN features is employed. The proposed adaptive system-based speed control for BLDC motor drive system effectiveness is compared with existing PID-based speed control strategy by utilizing the MATLAB Simulink platform. However, the obtained result highlights the proposed model reference adaptive control (MRAC)-based model in optimizing BLDC motor drives for applications requiring high precision and adaptability.

The permanent magnet synchronous motor (PMSM) is the heart of the electric drive system in electric vehicle technology. The effects of load variation and motor parameter changes are the important key challenges, which deteriorate the dynamic performances of interior PMSM (IPMSM) drives. To overcome these issues, this study suggests the development of an efficient new control drive system by integrating the Model Reference Adaptive Control (MRAC) with a fuzzy logic controller (FLC) using a finite-element model optimized motor model. The proposed cascaded system comprises two loops: a main outer loop that runs MRAC to mitigate the effects of load variation, and a secondary inner loop with FLC for resilient performance against parametric fluctuations of the IPMSM drive system. The proposed controller uses the hybrid space vector pulse width modulation technique to regulate the switching components of the inverter. It also reduces total harmonic distortion (THD) and torque ripple during the startup of the motor. The overall examination of the PMSM drive system is accomplished by co-simulation using MATLAB and Simcenter MAGNET software. The simulated results demonstrate the superiority of the proposed fuzzy adaptive controller in terms of higher maximum torque and improved speed tracking accuracy. A prototype of the proposed PMSM is developed and validated by experiment, which shows the robustness of the proposed methodology against load and speed fluctuations by reducing THD and torque ripples.

•Adaptive binary controller via output feedback for exact tracking of multivariable uncertain plants with nonuniform arbitrary relative degrees.•Multivariable generalization of the global finite-time differentiators with dynamic gains and higher-order sliding modes.•Global asymptotic stability of the closed-loop system and ultimate exponential convergence to small residual sets are guaranteed.•Fast transient responses, improved tracking precision and chattering-free control signals.•Engineering application to inertially stabilized platforms with numerical results based on data acquired from experiments in real-world conditions. This paper presents an extension of the Binary Model Reference Adaptive Control (BMRAC) for uncertain multivariable (square) systems with non-uniform arbitrary relative degrees using only output feedback and its application to inertially stabilized platforms using a two degree of freedom gimbal as actuator. The BMRAC is a robust adaptive strategy with good transient performance, thus useful for uncertain systems, and the multivariable framework is suitable to deal with mechanical unbalances. Using a newly proposed differentiator with dynamic gains based on higher-order sliding mode, the proposed controller achieves global and exact tracking. To illustrate the effective of the proposed solution, simulations are presented using real-word data obtained from an instrumented vehicle in an irregular ground.

•A new method is proposed to determine drug dose for mixed therapy in cancer treatment.•Optimal drug dose for treatment of reference patient is determined via SDRE method.•The SDRE based MRAC design method is introduced for nonlinear MIMO systems.•Personalized drug delivery for an unknown patient is determined using the MRAC method.•The continuous and bang-bang drug delivery protocols are achieved and compared. The paper presents Model Reference Adaptive Control (MRAC) design strategy to determine personalized drug delivery protocol for mixed therapy with chemotherapy and immunotherapy in cancer treatment. We consider a nonlinear mathematical ODE set for cancer dynamics that includes tumor, natural killers, circulating lymphocytes and cytotoxic T-cells population together with the interaction of chemotherapy and immunotherapy. For researchers and physicians, the main challenge in mathematical models is the determination of the exact model parameters. In order to have a drug administration policy for a patient with unknown parameter set, we develop State Dependent Riccati Equations (SDRE) based MRAC design approach to determine the personalized drug delivery protocol for patients with unknown model parameters. First of all, we determine the optimal drug delivery scenario for a reference patient with known dynamics parameters using SDRE approach. Then for any patient with unknown parameters, the personalized mixed therapy protocol is determined based on the treatment regimen of the reference patient. In the proposed methodology, unknown patients are considered as a black-box simulator in the design and the mathematical model parameters of the patient are not essential for the design of drug administration protocol. In addition, the Bang-Bang and continuous drug delivery regimens could be obtained using proper adaptation gains in the presented MRAC methodology. The simulation results demonstrate the effectiveness of the proposed MRAC approach for prescribing a treatment regimen of chemo-immunotherapy.

Cooperative control of multi-agent systems (MASs) is essential in engineering applications. However, malicious attacks and uncertainties can drive MASs to failure. Regrettably, prior work on resilient control of MASs rarely addresses uncertainties and malicious attacks concurrently. In this article, the resilient leader–follower consensus control problem is studied for non-linear MASs with cyber-physical attacks and uncertainties, and a novel resilient model reference adaptive sliding mode control (MRASMC) strategy is proposed. The stability of the MASs is proven via the Lyapunov theory, and the effectiveness of the proposed control framework is validated by numerical simulations.