Adaptive optimal control of under-actuated robotic systems using a self-regulating nonlinear weight-adjustment scheme: Formulation and experimental verification

This paper formulates an innovative model-free self-organizing weight adaptation that strengthens the robustness of a Linear Quadratic Regulator (LQR) for inverted pendulum-like mechatronic systems against perturbations and parametric uncertainties. The proposed control procedure is devised by using an online adaptation law to dynamically adjust the state weighting factors of LQR's quadratic performance index via pre-calibrated state-error-dependent hyperbolic secant functions (HSFs). The updated state-weighting factors re-compute the optimal control problem to modify the state-compensator gains online. The novelty of the proposed article lies in adaptively adjusting the variation rates of the said HSFs via an auxiliary model-free online self-regulation law that uses dissipative and anti-dissipative terms to flexibly re-calibrate the nonlinear function's waveforms as the state errors vary. This augmentation increases the controller's design flexibility and enhances the system's disturbance rejection capacity while economizing control energy expenditure under every operating condition. The proposed self-organizing LQR is analyzed via customized hardware-in-loop (HIL) experiments conducted on the Quanser's single-link rotational inverted pendulum. As compared to the fixed-gain LQR, the proposed SR-EM-STC delivers an improvement of 52.2%, 16.4%, 55.2%, and 42.7% in the pendulum's position regulation behavior, control energy expenditure, transient recovery duration, and peak overshoot, respectively. The experimental outcomes validate the superior robustness of the proposed scheme against exogenous disturbances

Analysis of Nonlinear Behaviors, Design and Control of Sigma Delta Modulators

M PhilSigma delta modulators (SDMs) have been widely applied in analogue-to-digital (A/D) conversion for many years. SDMs are becoming more and more popular in power electronic circuits because it can be viewed and applied as oversampled A/D converters with low resolution quantizers. The basic structure of an SDM under analytical investigation consists of a loop filter and a low bit quantizer connected by a negative feedback loop. Although there are numerous advantages of SDMs over other A/D converters, the application of SDMs is limited by the unboundedness of the system states and their nonlinear behaviors. It was found that complex dynamical behaviors exist in low bit SDMs, and for a bandpass SDM, the state space dynamics can be represented by elliptic fractal patterns confined within two trapezoidal regions. In all, there are three types of nonlinear behaviors, namely fixed point, limit cycle and chaotic behaviors. Related to the unboundedness issue, divergent behavior of system states is also a commonly discovered phenomenon. Consequently, how to design and control the SDM so that the system states are bounded and the unwanted nonlinear behaviors are avoided is a hot research topic worthy of investigated. In our investigation, we perform analysis on such complex behaviors and determine a control strategy to maintain the boundedness of the system states and avoid the occurrence of limit cycle behavior. For the design problem, we impose constraints based on the performance of an SDM and determine an optimal design for the SDM. The results are significantly better than the existing approaches

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

Robust Control

The need to be tolerant to changes in the control systems or in the operational environment of systems subject to unknown disturbances has generated new control methods that are able to deal with the non-parametrized disturbances of systems, without adapting itself to the system uncertainty but rather providing stability in the presence of errors bound in a model. With this approach in mind and with the intention to exemplify robust control applications, this book includes selected chapters that describe models of H-infinity loop, robust stability and uncertainty, among others. Each robust control method and model discussed in this book is illustrated by a relevant example that serves as an overview of the theoretical and practical method in robust control

Design of a High-Speed Architecture for Stabilization of Video Captured Under Non-Uniform Lighting Conditions

Video captured in shaky conditions may lead to vibrations. A robust algorithm to immobilize the video by compensating for the vibrations from physical settings of the camera is presented in this dissertation. A very high performance hardware architecture on Field Programmable Gate Array (FPGA) technology is also developed for the implementation of the stabilization system. Stabilization of video sequences captured under non-uniform lighting conditions begins with a nonlinear enhancement process. This improves the visibility of the scene captured from physical sensing devices which have limited dynamic range. This physical limitation causes the saturated region of the image to shadow out the rest of the scene. It is therefore desirable to bring back a more uniform scene which eliminates the shadows to a certain extent. Stabilization of video requires the estimation of global motion parameters. By obtaining reliable background motion, the video can be spatially transformed to the reference sequence thereby eliminating the unintended motion of the camera. A reflectance-illuminance model for video enhancement is used in this research work to improve the visibility and quality of the scene. With fast color space conversion, the computational complexity is reduced to a minimum. The basic video stabilization model is formulated and configured for hardware implementation. Such a model involves evaluation of reliable features for tracking, motion estimation, and affine transformation to map the display coordinates of a stabilized sequence. The multiplications, divisions and exponentiations are replaced by simple arithmetic and logic operations using improved log-domain computations in the hardware modules. On Xilinx\u27s Virtex II 2V8000-5 FPGA platform, the prototype system consumes 59% logic slices, 30% flip-flops, 34% lookup tables, 35% embedded RAMs and two ZBT frame buffers. The system is capable of rendering 180.9 million pixels per second (mpps) and consumes approximately 30.6 watts of power at 1.5 volts. With a 1024×1024 frame, the throughput is equivalent to 172 frames per second (fps). Future work will optimize the performance-resource trade-off to meet the specific needs of the applications. It further extends the model for extraction and tracking of moving objects as our model inherently encapsulates the attributes of spatial distortion and motion prediction to reduce complexity. With these parameters to narrow down the processing range, it is possible to achieve a minimum of 20 fps on desktop computers with Intel Core 2 Duo or Quad Core CPUs and 2GB DDR2 memory without a dedicated hardware

Nonlinear and factorization methods for the non-invasive investigation of the central nervous system

This thesis focuses on the functional study of the Central Nervous System (CNS) with non-invasive techniques. Two different aspects are investigated: nonlinear aspects of the cerebrovascular system, and the muscle synergies model for motor control strategies. The main objective is to propose novel protocols, post-processing procedures or indices to enhance the analysis of cerebrovascular system and human motion analysis with noninvasive devices or wearable sensors in clinics and rehabilitation. We investigated cerebrovascular system with Near-infrared Spectroscopy (NIRS), a technique measuring blood oxygenation at the level of microcirculation, whose modification reflects cerebrovascular response to neuronal activation. NIRS signal was analyzed with nonlinear methods, because some physiological systems, such as neurovascular coupling, are characterized by nonlinearity. We adopted Empirical Mode Decomposition (EMD) to decompose signal into a finite number of simple functions, called Intrinsic Mode Functions (IMF). For each IMF, we computed entropy-based features to characterize signal complexity and variability. Nonlinear features of the cerebrovascular response were employed to characterize two treatments. Firstly, we administered a psychotherapy called eye movement desensitization and reprocessing (EMDR) to two groups of patients. The first group performed therapy with eye movements, the second without. NIRS analysis with EMD and entropy-based features revealed a different cerebrovascular pattern between the two groups, that may indicate the efficacy of the psychotherapy when administered with eye movements. Secondly, we administered ozone autohemotherapy to two groups of subjects: a control group of healthy subjects and a group of patients suffering by multiple sclerosis (MS). We monitored the microcirculation with NIRS from oxygen-ozone injection up 1.5 hours after therapy, and 24 hours after therapy. We observed that, after 1.5 hours after the ozonetherapy, oxygenation levels improved in both groups, that may indicate that ozonetherapy reduced oxidative stress level in MS patients. Furthermore, we observed that, after ozonetherapy, autoregulation improved in both groups, and that the beneficial effects of ozonetherapy persisted up to 24 hours after the treatment in MS patients. Due to the complexity of musculoskeletal system, CNS adopts strategies to efficiently control the execution of motor tasks. A model of motor control are muscle synergies, defined as functional groups of muscles recruited by a unique central command. Human locomotion was the object of investigation, due to its importance for daily life and the cyclicity of the movement. Firstly, by exploiting features provided from statistical gait analysis, we investigated consistency of muscle synergies. We demonstrated that synergies are highly repeatable within-subjects, reinforcing the hypothesis of modular control in motor performance. Secondly, in locomotion, we distinguish principal from secondary activations of electromyography. Principal activations are necessary for the generation of the movement. Secondary activations generate supplement movements, for instance slight balance correction. We investigated the difference in the motor control strategies underlying muscle synergies of principal (PS) and secondary (SS) activations. We found that PS are constituted by a few modules with many muscles each, whereas SS are described by more modules than PS with one or two muscles each. Furthermore, amplitude of activation signals of PS is higher than SS. Finally, muscle synergies were adopted to investigate the efficacy of rehabilitation of stiffed-leg walking in lower back pain (LBP). We recruited a group of patients suffering from non-specific LBP stiffening the leg at initial contact. Muscle synergies during gait were extracted before and after rehabilitation. Our results showed that muscles recruitment and consistency of synergies improved after the treatment, showing that the rehabilitation may affect motor control strategies

Evolution, Monitoring and Predicting Models of Rockburst: Precursor Information for Rock Failure

Load/unload response ratio predicting of rockburst; Three-dimensional reconstruction of fissured rock; Nonlinear dynamics evolution pattern of rock cracks; Bayesian model for predicting rockburs