Observer-based fuzzy adaptive control for strict-feedback nonlinear systems

In this paper, an adaptive fuzzy output feedback control approach is proposed for single-input-single-output nonlinear systems without the measurements of the states. The nonlinear systems addressed in this paper are assumed to possess unmodeled dynamics in the presence of unstructured uncertainties and dynamic disturbances, where the unstructured uncertainties are not linearly parameterized, and no prior knowledge of their bounds are available. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a state observer is developed to estimate the unmeasured states. By combining the backstepping technique with the small-gain approach, a stable adaptive fuzzy output feedback control method is proposed. It is shown that by applying the proposed adaptive fuzzy control approach, the closed-loop systems are semiglobally uniformly ultimately bounded. The effectiveness of the proposed approach is illustrated from simulation results.

A Combined Backstepping and Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control

A robust adaptive fuzzy control approach is developed for a class of multi-input-multi-output (MIMO) nonlinear systems with modeling uncertainties and external disturbances by using both the approximation property of the fuzzy logic systems and the backstepping technique. The MIMO systems are composed of interconnected subsystems in the strict-feedback form. The main characteristics of the developed approach are that the online computation burden is alleviated and the robustness to dynamic uncertainties and external disturbances is improved. It is proven that all the signals of the resulting closed-loop system are uniformly bounded and that the tracking errors converge to a small neighborhood around zero. Two simulation experiments are presented to demonstrate the feasibility of the approach developed in this paper.

Robust Adaptive Tracking Control for Nonlinear Systems Based on Bounds of Fuzzy Approximation Parameters

Fuzzy adaptive observer backstepping control for MIMO nonlinear systems

Observer-based adaptive fuzzy tracking control for a class of uncertain nonlinear MIMO systems

This paper presents a fuzzy adaptive control suitable for motion control of multi-link robot manipulators with structured and unstructured uncertainties. When joint velocities are available, full state fuzzy adaptive feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. Further, it is shown that required stability conditions, in both cases, can be formulated as LMI problems and solved using dedicated software. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

Abstract The article presents two contributions: the first is an optimised control structure for photovoltaic grid connected systems (PVGCSs). The power chain is composed of two cascaded power converters, namely, a boost converter and a five-level T-type multilevel converter. Traditionally, each power converter is controlled by a separate mode control (SMC) from the other, which is computationally intensive since each converter requires its own control system, which is not practical. The suggested control, called integrated finite set model predictive control (IFS-MPC), allows controlling cascaded converters at the same time in one stage, instead of controlling them separately. Consequently, the overall implementation system is widely reduced. The second contribution of the article is a modified IFS-MPC called modified integrated finite set-model predictive control (M-IFS-MPC), which ensures the correct functioning of the grid-tied PV system under certain faults in converter components. Indeed, when one of the DC-link capacitors fails or when one of the auxiliary switches breaks down, by selecting an appropriate choice of the DC-link capacitors’ voltage reference, the proposed design allows a normal operation without intervention on the power circuit.

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Integrated Model Predictive Control of a Single-Phase Multilevel T-type Converter for a Photovoltaic Grid Connected System under Failure Conditions

The paper presents the design and the implementation of a direct predictive control of a variable speed wind energy conversion system. The conversion chain uses a Doubly Fed Induction Generator DFIG whereas the control method is based on a Finite States Model Predictive Control FS-MPC. The proposed control method selects the optimal switching state of the two levels back to back power converter that minimizes the cost function, where this optimal voltage vector is applied on the output of the power converter in next sampling time. The proposed predictive control strategy uses only one sample time prediction and it is intuitive since it is very simple for implementation. In order to adjust the measured rotor currents to track their references, the error between orthogonal rotor current components predictions to their computed values used to select the optimal vector and applied on the power converter in rotor side CSR in next sampling time. On other side, based on the error between the active and reactive power prediction and their references of the electrical grid, the predictive algorithm control of the gird side converter CSG kept the Dc-link voltage constant and guarantee that the whole system functioning with unity power factor. The experimental results confirm the advantages of using this structure for wind energy conversion system and the effectiveness of the proposed control strategy.

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Experimental Validation of Direct Predictive Control of Variable Speed Wind Energy Conversion System Based on DFIG

The paper presents an optimized T-type multilevel converter topology following a systematic procedure. The topology is assessed according to the quality of the output energy, efficiency and the cost. A special attention is dedicated to the performance evaluation of the proposed topology under Finite Set Model Predictive Control FS-MPC modulation well known by its difficulty in quantifying the power losses since the switching frequency is variable. An approach to determine the switching frequency and the duty cycle for each switch is presented and explained. This approach permits accurate calculation of the losses using FS-MPC modulation technique. The extensive simulation results show that, when compared to other similar existing topologies, the performance of the proposed topology is better in terms of reduced power losses, higher output power quality and lower cost.

Optimized T-type multilevel converter under Finite Set Model Predictive Control

Energy storage systems are needed in islanded photovoltaic microgrids to ensure the load requirements when there is insufficient solar irradiation. The low cost and the high energy density make the lead acid batteries ideal for many applications in microgrids, however the major issue is their poor service life. Battery-Supercapacitor hybrid energy storage system with an optimal energy flow management strategy are proposed in this paper. Due to their high power density, supercapacitors are used to respond to quick load fluctuations and reduce the stress on batteries. The photovoltaic generator, batteries and supercapacitors are connected to the DC bus through DC-DC converters controlled using the model predictive control. Depending on solar irradiation, batteries state of charge and supercapacitors state of charge, many operation modes are simulated and discussed showing the reliability of the proposed power management strategy.

Kamel Barra

Papers

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

Integrated Model Predictive Control of a Single-Phase Multilevel T-type Converter for a Photovoltaic Grid Connected System under Failure Conditions

Experimental Validation of Direct Predictive Control of Variable Speed Wind Energy Conversion System Based on DFIG

Optimized T-type multilevel converter under Finite Set Model Predictive Control

Predictive Power Control and Power Management for Islanded PV Microgrid with Battery-Supercapacitor Hybrid Energy Storage System