Intelligent modelling and control with fatigue reduction for fes induced knee joint of hemiplegic for rehabilitation

Functional electrical stimulation (FES) is one of the treatment for the people with stroke such as hemiplegic body (half body paralysed) by applying small charges of electricity to the muscle to induce the movement. FES can be applied during rehabilitation stage to enhance the healing process. The development of the intelligent hemiplegic model of the knee joint and control strategies with fatigue reduction for the FES control application are the main concern of this thesis. Modelling the musculoskeletal is significantly challenging due to the complexity of the system. Development of the knee joint model that is capable of relating FES parameters is the first aim of this study. The knee joint model comprising of equations of motion to represent the segmental dynamics and PSO optimised Neural Network - ARX to represent quadriceps muscle properties was formulated. The results show that the muscle model developed gives an accurate dynamic characterisation. Development of the FES-induced extension and flexion motions control is the second aim of this study. To control the motor function of muscle by using external devices such as FES is one of the crucial issues. High nonlinearity and rapid change of muscle properties due to fatigue are the major problems of the FES control system. PSO optimised Fuzzy Logic Control (FLC) has been proposed to handle this complex nonlinear system. A natural trajectory control strategy by using the proposed control system has been assessed. There are two control strategies; knee movement control with and without minimised electrical stimulation were developed. The control problem was to design a FLC such that the knee joint track the desired trajectory as closely as possible. Then, both control strategies were investigated in terms of muscle fatigue. Multi objective PSO optimised FLC was used to minimise the amount of electrical stimulation in order to reduce the muscle fatigue. This control strategy has shown up to 32.6% minimisation of the electrical stimulation in the simulation studies and 35.89 % reduction the muscle fatigue in the experimental work. Therefore, this control strategy can be applied as FES control system for the treatment in rehabilitation to enhance the healing process for the stroke subjects such as hemiplegic patients

Adaptive particle swarm optimization

An adaptive particle swarm optimization (APSO) that features better search efficiency than classical particle swarm optimization (PSO) is presented. More importantly, it can perform a global search over the entire search space with faster convergence speed. The APSO consists of two main steps. First, by evaluating the population distribution and particle fitness, a real-time evolutionary state estimation procedure is performed to identify one of the following four defined evolutionary states, including exploration, exploitation, convergence, and jumping out in each generation. It enables the automatic control of inertia weight, acceleration coefficients, and other algorithmic parameters at run time to improve the search efficiency and convergence speed. Then, an elitist learning strategy is performed when the evolutionary state is classified as convergence state. The strategy will act on the globally best particle to jump out of the likely local optima. The APSO has comprehensively been evaluated on 12 unimodal and multimodal benchmark functions. The effects of parameter adaptation and elitist learning will be studied. Results show that APSO substantially enhances the performance of the PSO paradigm in terms of convergence speed, global optimality, solution accuracy, and algorithm reliability. As APSO introduces two new parameters to the PSO paradigm only, it does not introduce an additional design or implementation complexity

Intelligent renewable energy storage and management system for rural household

The battery in conventional standalone photovoltaic (PV) system frequently undergoes deep cycles and irregular charging patterns, which can significantly reduce the battery lifetime and increase the replacement cost of the system. Battery-Supercapacitor Hybrid Energy Storage System (HESS) is the most promising solution to prolong the lifespan of the battery. The control strategy is implemented to distribute the resources of the HESS based on the real-time operating conditions. Despite the intelligent control strategy, such as Fuzzy Logic Controller (FLC), is more effective than the classical control strategies, there is limited studies on intelligent control strategy that is optimized based on predicted power demand. Also, it is challenging to integrate the intelligent control strategy with high computation complexity into an actual system with commercial non-programmable charge controller. Therefore, two intelligent control strategies, namely Particle Swarm Optimization (PSO) optimized FLC (PSO-optimized FLC) and PSO-optimized Power Distribution Algorithm (PSO-PDA), are proposed to prolong the battery lifespan in a standalone PV system with Battery-Supercapacitor HESS. The objectives of this research work are the development of prediction models for very short term prediction of power demand using basic features, the development of PSO-optimized FLC and PSO-PDA, as well as the implementation of PSO-PDA in actual standalone PV system with Battery-Supercapacitor HESS. The PSO-optimized FLC is novel in terms of its structure as a moving average filter is used to first extract the high frequency power from the power demand then subsequently a FLC decides the sharing ratio of the supercapacitor and the battery. The membership functions of the FLC are optimized using Particle Swarm Optimization (PSO) based on predicted power demand to reduce the battery peak power. It outperforms conventional systems in terms of battery lifetime improvement (12.81 %) and peak power reduction (64.26 %). However, this system is impractical for real-life implementation as optimizing the membership functions requires high optimization complexity. An alternative novel control strategy, PSO-PDA, is developed to achieve the research aim. The parameters of power distribution algorithm are optimized every minute based on predicted h-mins (h=30, h=60, and h=90) ahead power demand and state-of-charge of the supercapacitor to mitigate peak demand in battery power. With short optimization interval, it can compensate for the prediction error of power demand from basic prediction models and adapt to the varying power demand. The PSO-PDA outperforms the PSO-optimized FLC, in term of battery lifetime improvement (up to 22 %) and battery peak demand reduction (up to 57 %) and requires significantly shorter optimization time per optimization (not more than 20.647 s) than PSO-optimized FLC. Moreover, the PSO-PDA requires only one day of pre-training to be fully implemented in an actual 2-kW rated standalone PV system with Battery-Supercapacitor HESS. Also, it can be tuned to work with the commercial non-programmable charge controller. The experimental results show that the PSO-PDA can improve the battery lifetime by 16.50 % and reduce the battery peak power by 58.58 %. Moreover, the experimental results are compared with simulation results where the findings highlight that the results of the Simulink model only overestimate the battery lifetime improvement by 7.66 %

Controller design for synchronization of an array of delayed neural networks using a controllable

This is the post-print version of the Article - Copyright @ 2011 ElsevierIn this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the U.K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Orthogonal learning particle swarm optimization

Particle swarm optimization (PSO) relies on its learning strategy to guide its search direction. Traditionally, each particle utilizes its historical best experience and its neighborhood’s best experience through linear summation. Such a learning strategy is easy to use, but is inefficient when searching in complex problem spaces. Hence, designing learning strategies that can utilize previous search information (experience) more efficiently has become one of the most salient and active PSO research topics. In this paper, we proposes an orthogonal learning (OL) strategy for PSO to discover more useful information that lies in the above two experiences via orthogonal experimental design. We name this PSO as orthogonal learning particle swarm optimization (OLPSO). The OL strategy can guide particles to fly in better directions by constructing a much promising and efficient exemplar. The OL strategy can be applied to PSO with any topological structure. In this paper, it is applied to both global and local versions of PSO, yielding the OLPSO-G and OLPSOL algorithms, respectively. This new learning strategy and the new algorithms are tested on a set of 16 benchmark functions, and are compared with other PSO algorithms and some state of the art evolutionary algorithms. The experimental results illustrate the effectiveness and efficiency of the proposed learning strategy and algorithms. The comparisons show that OLPSO significantly improves the performance of PSO, offering faster global convergence, higher solution quality, and stronger robustness