A metaheuristic particle swarm optimization approach to nonlinear model predictive control

This paper commences with a short review on optimal control for nonlinear systems, emphasizing the Model Predictive approach for this purpose. It then describes the Particle Swarm Optimization algorithm and how it could be applied to nonlinear Model Predictive Control. On the basis of these principles, two novel control approaches are proposed and anal- ysed. One is based on optimization of a numerically linearized perturbation model, whilst the other avoids the linearization step altogether. The controllers are evaluated by simulation of an inverted pendulum on a cart system. The results are compared with a numerical linearization technique exploiting conventional convex optimization methods instead of Particle Swarm Opti- mization. In both approaches, the proposed Swarm Optimization controllers exhibit superior performance. The methodology is then extended to input constrained nonlinear systems, offering a promising new paradigm for nonlinear optimal control design.peer-reviewe

Multilayer perceptron adaptive dynamic control of mobile robots : experimental validation

This paper presents experimental results acquired from the implementation of an adaptive control scheme for nonholonomic mobile robots, which was recently proposed by the same authors and tested only by simulations. The control system comprises a trajectory tracking kinematic controller, which generates the reference wheel velocities, and a cascade dynamic controller, which estimates the robot's uncertain nonlinear dynamic functions in real-time via a multilayer perceptron neural network. In this manner precise velocity tracking is attained, even in the presence of unknown and/or time-varying dynamics. The experimental mobile robot, designed and built for the purpose of this research, is also presented in this paper.peer-reviewe

Integrated waste management as a climate change stabilisation wedge for the Maltese islands

The continuous increase in anthropogenic greenhouse gas emissions occurring since the Industrial Revolution is offering significant ecological challenges to Earth. These emissions are leading to climate changes which bring about extensive damage to communities, ecosystems and resources. The analysis in this article is focussed on the waste sector within the Maltese islands, which is the largest greenhouse gas emitter in the archipelago following the energy and transportation sectors. This work shows how integrated waste management, based on a life cycle assessment methodology, acts as an effective stabilisation wedge strategy for climate change. Ten different scenarios applicable to the Maltese municipal solid waste management sector are analysed. It is shown that the scenario that is most coherent with the stabilisation wedges strategy for the Maltese islands consists of 50% landfilling, 30% mechanical biological treatment and 20% recyclable waste export for recycling. It is calculated that 16.6Mt less CO2-e gases would be emitted over 50 years by means of this integrated waste management stabilisation wedge when compared to the business-as-usual scenario. These scientific results provide evidence in support of policy development in Malta that is implemented through legislation, economic instruments and other applicable tools.peer-reviewe

Dual adaptive dynamic control of mobile robots using neural networks

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.peer-reviewe

Parametric Modelling of EEG Data for the Identification of Mental Tasks

Electroencephalographic (EEG) data is widely used as a biosignal for the identification of different mental states in the human brain. EEG signals can be captured by relatively inexpensive equipment and acquisition procedures are non-invasive and not overly complicated. On the negative side, EEG signals are characterized by low signal-to-noise ratio and non-stationary characteristics, which makes the processing of such signals for the extraction of useful information a challenging task.peer-reviewe

Neural control of nonlinear systems with composite adaptation for improved convergence of Gaussian networks

The use of composite adaptive laws for control of the ane class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in better system performance. To ensure global stability despite the inevitable network approximation errors, the control law is augmented with a low gain sliding mode component and deadzone adaptation is used for the indirect part of the composite law. The stability of the system is analyzed and the effectiveness of the method is demonstrated by simulation.peer-reviewe

Unscented transform-based dual adaptive control of nonlinear MIMO systems

The paper proposes a multilayer perceptron neural network controller for dual adaptive control of a class of stochastic MIMO nonlinear systems subject to functional uncertainty. The neural network parameters are adjusted in real-time using the Unscented Kalman filter algorithm and no pre-operational training phase is required. Dual adaptive control aims to strike a compromise between the two control characteristics of caution and probing, leading to an improved overall performance. The system is evaluated through numerical simulations and Monte Carlo analysis. The resulting performance of the dual adaptive controller is not only consistently superior to non-dual adaptive control schemes, but also surpasses the performance of similar controllers that are based on Extended Kalman filter estimators. This reflects the enhanced accuracy of the Unscented Kalman filter estimator, despite being a local estimation method. In addition, unlike use of other estimators, the proposed approach neither requires the computation of complex Jacobian matrices as part of the design, nor the evaluation of such matrices in real-time. This renders the proposed controller inherently amenable and practical for real-time implementation.peer-reviewe

Experimental evaluation of haptic control for human activated command devices

Haptics refers to a widespread area of research that focuses on the interaction between humans and machine interfaces as applied to the sense of touch. A haptic interface is designed to increase the realism of tactile and kinesthetic sensations in applications such as virtual reality, teleoperation, and other scenarios where situational awareness is considered important, if not vital. This paper investigates the use of electric actuators and non-linear algorithms to provide force feedback to an input command device for providing haptics to the human operator. In particular, this work involves the study and implementation of a special case of feedback linearization known as inverse dynamics control and several outer loop impedance control topologies. It also investigates the issues concerned with force sensing and the application of model based controller functions in order to vary the desired inertia and the desired mass matrix. Results of the controllers’ abilities to display any desired impedance and provide the required kinesthetic constraint of virtual environments are shown on two experimental test rigs designed for this purpose.peer-reviewe

Trajectory tracking of a differentially driven wheeled mobile robot in the presence of obstacles

A trajectory following and obstacle avoidance mechanism for a mobile robot is presented for situations where the robot has to follow a specific target trajectory but the task might not be completely possible due to obstacles in the way, which the robot must avoid. After avoiding an obstacle, the robot should catch up with the target trajectory. In the proposed system, this objective is reached by combining a nonlinear control method with an Artificial Potential Function method, leading to trajectory tracking control with obstacle avoidance capabilities.peer-reviewe

Trajectory tracking in the presence of obstacles using the limit cycle navigation method

This paper proposes a system for effecting trajectory tracking in combination with obstacle avoidance in mobile robotic systems. In robotics research, these two situations are typically considered as separate problems. This work approaches the problem by integrating classical trajectory following control schemes with Kim et al.’s Limit Cycle Navigation method for obstacle avoidance. The use of Artificial Potential Function methods for obstacle avoidance is purposely avoided so as to prevent the well-known problems of local minima associated with such schemes. The paper also addresses the problem of non-global obstacle sensing and proposes modifications to Kim et al.’s method for handling multiple, overlapping obstacles under local sensing conditions.peer-reviewe