Yiqi Liu

Hybrid feedback feedforward

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost.

Simplified adaptive neural control of strict-feedback nonlinear systems

This paper presents a simplified adaptive backstepping neural network control (ABNNC) strategy for a general class of uncertain strict-feedback nonlinear systems. In the backstepping design, all unknown functions at intermediate steps are passed down such that only a single neural network is needed to approximate a lumped uncertainty at the last step. The closed-loop system achieves practical asymptotic stability in the sense that all involved signals are bounded and the tracking error converges to a small neighborhood of zero. The contribution of this study is that the complexity growing problem of the traditional ABNNC design is substantially eliminated for a general class of uncertain strict-feedback nonlinear systems, where the constraints of control parameters that guarantee closed-loop stability is clearly demonstrated. An illustrative example has verified effectiveness of our approach.

Composite learning from adaptive backstepping neural network control

In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is applied to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods.In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights. The influence of NN approximation errors on the control performance is also clearly shown. The distinctive feature of the proposed NN composite learning is that NN parameter convergence is guaranteed without the requirement of the trajectory of NN inputs being recurrent. Illustrative results have verified effectiveness and superiority of the proposed method compared with existing NN learning control methods.

Composite Learning Fuzzy Control of Uncertain Nonlinear Systems

Function approximation accuracy and computational cost are two major concerns in approximation-based adaptive fuzzy control. In this paper, a model reference composite learning fuzzy control strategy is proposed for a class of affine nonlinear systems with functional uncertainties. In the proposed approach, a modified modeling error that utilizes data recorded online is defined as a prediction error, a linear filter is applied to estimate time derivatives of plant states, and both the tracking error and the prediction error are exploited to update parametric estimates. It is proven that the closed-loop system achieves semiglobal practical exponential stability by an interval-excitation condition which is much weaker than a persistent-excitation condition. Compared with a concurrent learning approach that has the same aim as this study, the computational cost of the proposed approach is significantly reduced for the guarantee of accurate function approximation. An illustrative example of aircraft wing rock control has been provided to verify effectiveness of the proposed control strategy.

Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment

Mainly due to the hostile environment in wastewater plants WWTPs, the reliability of sensors with respect to important qualities is often poor. In this work, we present the design of a semiadaptive fault diagnosis method based on the variational Bayesian mixture factor analysis VBMFA to support process monitoring. The proposed method is capable of capturing strong nonlinearity and the significant dynamic feature of WWTPs that seriously limit the application of conventional multivariate statistical methods for fault diagnosis implementation. The performance of proposed method is validated through a simulation study of a wastewater plant. Results have demonstrated that the proposed strategy can significantly improve the ability of fault diagnosis under fault-free scenario, accurately detect the abrupt change and drift fault, and even localize the root cause of corresponding fault properly.

Prediction of concrete corrosion in sewers with hybrid Gaussian processes regression model

Concrete corrosion is a major concern for sewer authorities due to the significantly shortened service life, which is governed by the corrosion rate and the corrosion initiation time. This paper proposes a hybrid Gaussian Processes Regression (GPR) model to approach the evolution of the corrosion rate and corrosion initiation time, thereby supporting the calculation of service life of sewers. A major challenge in practice is the limited availability of reliable corrosion data obtained in well-defined sewer environments. To enhance the predictability of the hybrid GPR model, an interpolation technique was implemented to extend the limited dataset. The trained model was able to estimate the corrosion initiation time and corrosion rates very close to those measured in Australian sewers.

Improved degradation of anaerobically digested sludge during post aerobic digestion using ultrasonic pretreatment

Aerobic digestion has been recently studied to further treat anaerobically digested sludge (ADS) at sewage treatment plants. However, the process, which is called post aerobic digestion (PAD), only achieves limited degradation of the ADS. Here, we present a new study using ultrasonic pretreatment to enhance full-scale ADS degradation during PAD. The experiments examined the aerobic digestion of ultrasonically pretreated ADS and untreated ADS using activated sludge as digesting sludge. The ADS was degraded by 28%, 35% and 44% within the 5 day PAD period, when pretreated ultrasonically (20 kHz, 10 min) at 25 W, 50 W and 100 W, respectively. In contrast, in the absence of prior sonication, the ADS was only degraded by 24% in the same PAD period. Increased inorganic nitrogen generation and increased percentage of dead cells occurred in the ultrasonically pretreated ADS, indicating that endogenous respiration resulted in the reduction of volatile solids. The ultrasonic pretreatment significantly improved the aerobic digestion efficiency, which resulted in higher ADS degradation. Additionally, we show that the ultrasonic pretreatment could be an economically favorable technology when the cost of sludge transport and disposal is above

Improving dewaterability of anaerobically digested sludge by combination of persulfate and zero valent iron

55 per wet tonne.