Adaptive Model Learning of Neural Networks with UUB Stability for Robot Dynamic Estimation

Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification. This paper suggests stable updating rules to drive neural networks inspiring from model reference adaptive paradigm. Network structure consists of three parallel self-driving neural networks which aim to estimate robot dynamic terms individually. Lyapunov candidate is selected to construct energy surface for a convex optimization framework. Learning rules are driven directly from Lyapunov functions to make the derivative negative. Finally, experimental results on 3-DOF Phantom Omni Haptic device demonstrate efficiency of the proposed method.Comment: 6 pages, 12 figure

Adaptive output feedback tracking control for bilinear systems

This paper deals with the trajectory tracking control problem for a class of bilinear systems with unmeasurable states and unknown parameters. Firstly, a full-information controller is suggested that guarantees global tracking under a persistency of excitation (PE) condition on the desired trajectories. Next, a model-based observer is designed for the system, which is further developed into an adaptive observer through dynamic regressor and mixing (DREM) parameter estimator. This enables global estimation under a weaker convergence condition where the regressor is PE. The estimated states and parameters are then replaced in the full-information controller, instead of their respective unavailable states and parameters, to construct the output feedback controller and its adaptive version. The proposed algorithm is applied to control lossless power factor precompensator (PFP) circuit with an unmeasurable input current and an unknown load conductance

Time-domain Classification of the Brain Reward System: Analysis of Natural- and Drug-Reward Driven Local Field Potential Signals in Hippocampus and Nucleus Accumbens

Addiction is a major public health concern characterized by compulsive reward-seeking behavior. The excitatory glutamatergic signals from the hippocampus (HIP) to the Nucleus accumbens (NAc) mediate learned behavior in addiction. Limited comparative studies have investigated the neural pathways activated by natural and unnatural reward sources. This study has evaluated neural activities in HIP and NAc associated with food (natural) and morphine (drug) reward sources using local field potential (LFP). We developed novel approaches to classify LFP signals into the source of reward and recorded regions by considering the time-domain feature of these signals. Proposed methods included a validation step of the LFP signals using autocorrelation, Lyapunov exponent and Hurst exponent to assess the meaningful stability of these signals (lack of chaos). By utilizing the probability density function (PDF) of LFP signals and applying Kullback-Leibler divergence (KLD), data were classified to the source of the reward. Also, HIP and NAc regions were visually separated and classified using the symmetrized dot pattern technique, which can be applied in real-time to ensure the deep brain region of interest is being targeted accurately during LFP recording. We believe our method provides a computationally light and fast, real-time signal analysis approach with real-world implementation.Comment: 12 pages, 7 figures first two authors contributed equally to this wor

Tomography-assisted control for the microwave drying process of polymer foams

This paper presents the integration of electrical capacitance tomography (ECT) with a moisture controller for the microwave drying of polymer foam. The proportional–integral (PI) control and the linear quadratic Gaussian (LQG) control are employed in designing the controller. The control objective in this process is that the moisture of polymer foam after the drying process reaches the desired set point. The permittivity distribution of polymer foam after the drying process is estimated in real-time using a designed ECT sensor and transferred as feedback to the controller. Since the permittivity and the moisture are strongly correlated, the material moisture can be controlled by controlling the permittivity. A state-space model is derived for the microwave drying process based on a system identification approach using the experimental data from the process. The derived model is employed in designing the LQG controller and adjusting the parameters of the PI controller. The designed controllers are implemented on a testbed microwave oven, and the experimental results show that the designed controllers are able to follow the desired set point moisture. The performance of the system with both controllers is compared, and their advantages and disadvantages are discussed. Moreover, the benefits of having a moisture controller for the microwave drying process are shown in simulation studies compared to an uncontrolled system

System identification of conveyor belt microwave drying process of polymer foams using electrical capacitance tomography

The microwave drying process has a wide application in industry, including drying polymer foams after the impregnation process for sealings in the construction industry. The objective of the drying process is to reach a certain moisture in the foam by adjusting the power levels of the microwave sources. A moisture controller can be designed to achieve this goal; however, a process model is required to design model-based controllers. Since complex physics governs the microwave drying process, system identification tools are employed in this paper to exploit the process input and output information and find a simplified yet accurate model of the process. The moisture content of the foam that is the process output is measured using a designed electrical capacitance tomography (ECT) sensor. The ECT sensor estimates the 2D permittivity distribution of moving foams, which correlates with the foam moisture. Experiments are conducted to collect the ECT measurements while giving different inputs to the microwave sources. A state-space model is estimated using one of the collected datasets and is validated using the other datasets. The comparison between the model response and the actual measurements shows that the model is accurate enough to design a controller for the microwave drying process