Model-free adaptive consensus tracking control for unknown nonlinear multi-agent systems with sensor saturation

The design of a cascade controller is demonstrated for a buck–boost converter that is combined with two control loops consisting of inner and outer controllers. The outer loop is implemented by a fractional-order proportional-integrated-derivative (FO-PID) controller that works as a voltage controller and generates a reference current for the inner control loop. To provide faster dynamic performance for inner loop, a self-tuning regulator adaptive controller, which tries to regulates the current with the help of a novel improved exponential regressive least square identification in an online technique, is designed. Moreover, in the outer loop, to tune the gains of the FO-PID controller, a novel algorithm of antlion optimizer algorithm is used that offers many benefits in comparison with other algorithms. The system provided by the boost mode is a non-minimum phase system, which creates challenges for designing a stable controller. In addition, a single loop controller is proposed based on a PID controller tuned by a particle swarm optimization algorithm to be compared with the cascade controller. Cascade loop can present significant benefits to the controller such as better disturbance rejection. Finally, the strength of the presented cascade control scheme is verified in different performing situations by real-time experiments.

A novel adaptive cascade controller design on a buck–boost DC–DC converter with a fractional-order PID voltage controller and a self-tuning regulator adaptive current controller

This brief investigates the data-driven control problem for a class of nonlinear systems. An incremental triangular dynamic linearization (ITDL) model is presented to equivalently describe these systems, and it contains three parameters including a single time-varying parameter, all of which have clear physical meanings. The ITDL model has obvious advantages compared with the existing partial form dynamic linearization (PFDL) model, although it is a special case of the PFDL model in form. An online estimation algorithm is designed for the single time-varying parameter, and then an adaptive control law is designed. The convergence and stability of the closed-loop system are analyzed, and the corresponding explicit conditions are derived. Finally, a comparative simulation study is conducted to illustrate the effectiveness of the proposed method. 

Data-Driven Adaptive Control: An Incremental Triangular Dynamic Linearization Approach

The nonminimum-phase property of the dc–dc boost converter can create difficulties in designing a stable, robust, and fast control for the output voltage. In this article, these difficulties are reduced by modifying the state model of the boost converter in such a way that it behaves like a minimum-phase system. More specifically, the control input entering the state equation for the output voltage is treated as an unknown disturbance to make the relative degree of the system equal the system’s order. As a result, the modified model does not exhibit zero dynamics, so that it can be treated as a minimum-phase system for the control design. The modified model is, then, used to derive a dynamic compensator for the output voltage regulation. More explicitly, the dynamic compensator is designed based on combining linearizing feedback control with a disturbance observer. The latter is employed to compensate for model uncertainties and unknown load with a view to ensure asymptotic regulation under the composite controller. The asymptotic regulation is achieved due to the integral action property characterizing the disturbance observer. More interestingly, after simple algebraic manipulation, it turns out that the composite controller reduces to a dynamic state feedback control plus an antiwindup scheme to mitigate the effect of control saturation during transients. The performance of the proposed controller is verified by experimental tests. The experimental results demonstrate the ability of the proposed controller to achieve good transient and steady-state performances. 

Disturbance-Observer-Based Feedback Linearization Control for Stabilization and Accurate Voltage Tracking of a DC–DC Boost Converter

The nonminimum-phase property of the dc–dc boost converter can create difficulties in designing a stable, robust, and fast control for the output voltage. In this article, these difficulties are reduced by modifying the state model of the boost converter in such a way that it behaves like a minimum-phase system. More specifically, the control input entering the state equation for the output voltage is treated as an unknown disturbance to make the relative degree of the system equal the system’s order. As a result, the modified model does not exhibit zero dynamics, so that it can be treated as a minimum-phase system for the control design. The modified model is, then, used to derive a dynamic compensator for the output voltage regulation. More explicitly, the dynamic compensator is designed based on combining linearizing feedback control with a disturbance observer. The latter is employed to compensate for model uncertainties and unknown load with a view to ensure asymptotic regulation under the composite controller. The asymptotic regulation is achieved due to the integral action property characterizing the disturbance observer. More interestingly, after simple algebraic manipulation, it turns out that the composite controller reduces to a dynamic state feedback control plus an antiwindup scheme to mitigate the effect of control saturation during transients. The performance of the proposed controller is verified by experimental tests. The experimental results demonstrate the ability of the proposed controller to achieve good transient and steady-state performances.

This article presents the application of a data-driven control design to the output voltage regulation of dc–dc converters. Using only data collected on the plant, a proportional-integral-derivative (PID) controller is designed in order to achieve a desired closed-loop response, without deriving a mathematical model for the plant. Since an inverse response is typical on voltage signals of different dc–dc converters, the virtual reference feedback tuning (VRFT) method with flexible criterion is applied to estimate the controller gains, so the limitation due to the nonminimum phase zero, if it exists, is taken into account by the algorithm. We present appropriate choices for data collection, controller structure, and reference model definition in order to safely apply the VRFT method to design a controller for dc–dc converters. A practical application on a boost converter shows the effectiveness of the proposed method.

Virtual Reference Feedback Tuning Applied to DC–DC Converters

impulseest: A Python package for non-parametric impulse response estimation with input-output data

This work presents a Linear Quadratic Gaussian (LQG) controller in cascade loop applied to a Two‐Switch Forward Converter (2SFC). This arrange is suitable for low power battery chargers an...

Chrystian Lenon Remes

Papers

Virtual Reference Feedback Tuning Applied to DC–DC Converters

impulseest: A Python package for non-parametric impulse response estimation with input-output data

LQG controller in cascade loop tuned by PSO applied to a DC–DC converter

Virtual reference feedback tuning with robustness constraints: A swarm intelligence solution

Data-Driven Approach for Current Control in DC-DC Boost Converters