Robust active finite-time control of gas compressor system surge in the presence of unmatched disturbance and uncertainty

Active and robust control of surge instability is a special necessity for optimal and safe operation of centrifugal compressors, and for the purpose, this article presents a new hybrid scheme based on fuzzy and terminal sliding mode methods. The Greitzer model is used to design a novel controller when the disturbance instability in the flow and pressure alike the uncertainity in the compressor characteristic curve and throttle valve are embedded in it. The fuzzy approximator is used to estimate the effects of parametric uncertainty and the nonlinear terms, and the robustness of the proposed method is guaranteed using the terminal sliding mode control method. The Lyapunov criterion is utilized to verify the finite-time stability of the closed-loop system. The performance of the presented method is compared with other methods in the literature through simulations in MATLAB software. The results suggest that our designed controller outperforms the existing ones in terms of surge prevention and robustness against unmatched uncertainties and disturbances.

Optimal robust adaptive fuzzy backstepping control of electro-hydraulic servo position system

In this paper, an optimal robust adaptive fuzzy backstepping control is presented to the position control of the electro-hydraulic servo (EHS) system in the presence of structured and unstructured uncertainties. Initially, the robust control using the backstepping technique is presented to overcome the existing uncertainties in the dynamic equations. Mathematical proof demonstrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability. Then, to overcome the chattering problem, a very simple fuzzy approximator is presented where it approximates the bounds of the uncertainties. Although the proposed robust fuzzy backstepping control has a desirable performance, it has no mathematical analysis to prove the stability of the closed-loop system. Therefore, to solve this problem, the proposed fuzzy approximator has been transformed into a one-law adaptive fuzzy approximator with a single-input single-output fuzzy rule. Mathematical analysis illustrates that the closed-loop system in the presence of uncertainties has a global asymptotic stability under the proposed robust adaptive fuzzy backstepping control. Furthermore, a novel modified harmony search algorithm (MHSA) has been developed, by using the original harmony search algorithm (OHSA) as an optimization technique, to achieve the optimal values of the membership functions and the control coefficients. Finally, a comparative study has been conducted between the proposed control scheme under the MHSA and the OHSA, and other existing advanced control approaches to verify the effectiveness of the proposed control. Results show that the proposed control scheme under the MHSA can suppress the chattering problem and reduce the disturbances effectively while ensuring that the performance is tracked.

Adaptive Intelligent Sliding Mode Control of a Photovoltaic Grid-Connected Inverter

Adaptive intelligent sliding mode control methods are developed for a single-phase photovoltaic (PV) grid-connected transformerless system with a boost chopper and a DC-AC inverter. A maximum power point tracking (MPPT) method is implemented in the boost part in order to extract the maximum power from the PV array. A global fast terminal sliding control (GFTSMC) strategy is developed for an H-bridge inverter to make the tracking error between a grid reference voltage and the output voltage of the inverter converge to zero in a finite time. A fuzzy-neural-network (FNN) is used to estimate the system uncertainties. Intelligent methods, such as an adaptive fuzzy integral sliding controller and a fuzzy approximator, are employed to control the DC-AC inverter and approach the upper bound of the system nonlinearities, achieving reliable grid-connection, small voltage tracking error, and strong robustness to environmental variations. Simulation with a grid-connected PV inverter model is implemented to validate the effectiveness of the proposed methods.

DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING

One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART) neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.