THE International Labour Organisation remains almost alone of the institutions framed to promote international co–operation, understanding and world order after the War of 1914—18. Although its staff has been drastically reduced and all but a small group of officials have been transferred to Montreal, the Organisation continues to function effectively. From its new working centre it is issuing the International Labour Review, its studies and reports, its Legislative Series and from time to time organizes a series of meetings and conferences. The recent Labour Conference in New York was the most outstanding of these meetings, and to many it was a surprising but impressive witness both of the contribution which the Organisation is rendering to the practical improvement of labour conditions throughout the world and of its importance in the tasks of reconstruction which already lie near to our hand.

The International labour organisation

This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-of-freedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg-Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.

/pdf/neural-network-based-feedback-linearization-control-of-a-2a95pmuj08.pdf

Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system

Since their introduction, anti-lock braking systems (ABS) have mostly relied on heuristic, rule-based control strategies. ABS performance, however, can be significantly improved thanks to many recent technological developments. This work presents an extensive review of the state of the art to verify such a statement and quantify the benefits of a new generation of wheel slip control (WSC) systems. Motivated by the state of the art, as a case study, a nonlinear model predictive control (NMPC) design based on a new load-sensing technology was developed. The proposed ABS was tested on Toyota’s high-end vehicle simulator and was benchmarked against currently applied industrial controller. Additionally, a comprehensive set of manoeuvres were deployed to assess the performance and robustness of the proposed NMPC design. The analysis showed substantial reduction of the braking distance and better steerability with the proposed approach. Furthermore, the proposed design showed comparable robustness against external factors to the industrial benchmark.

/pdf/survey-on-wheel-slip-control-design-strategies-evaluation-50np3xyldu.pdf

Survey on Wheel Slip Control Design Strategies, Evaluation and Application to Antilock Braking Systems

Unmanned Aircraft Systems(UAS) have seen unprecedented levels of growth during the last two decades. Although many challenges still exist, one of the main UAS focus research areas is in navigation and control. This paper provides a comprehensive overview of helicopter navigation and control, focusing specifically on small-scale traditional main/tail rotor configuration helicopters. Unique to this paper, is the emphasis placed on navigation/control methods, modeling techniques, loop architectures and structures, and implementations. A `reference template' is presented and used to provide a basis for comparative studies and determine the capabilities and limitations of algorithms for unmanned/autonomous flight, as well as for navigation, and control. A detailed listing of related research is provided, which includes model structure, helicopter platform, control method and loop architecture, flight maneuvers and results for each. The results of this study was driven by and has led to the development of a `one-fits-all' comprehensive and modular navigation controller and timing architecture applicable to any rotorcraft platform.

Survey of Unmanned Helicopter Model-Based Navigation and Control Techniques

A Hamiltonian formulation of classes of distributed-parameter systems is presented, which incorporates the energy flow through the boundary of the spatial domain of the system, and which allows to represent the system as a boundary control Hamiltonian system. The system is Hamiltonian with respect to an infinite-dimensional Dirac structure associated with the exterior derivative and based on Stokes' theorem. The theory is applied to the telegraph equations for an ideal transmission line, Maxwell's equations on a bounded domain with non-zero Poynting vector at its boundary, and a vibrating string with traction forces at its ends. Furthermore the framework is extended to cover Euler's equations for an ideal fluid on a domain with permeable boundary. Finally, some properties of the Stokes-Dirac structure are investigated, including the analysis of conservation laws.

Hamiltonian formulation of distributed-parameter systems with boundary energy flow

This paper presents the system identification and design of a neural network based Proportional, Integral and Derivative (PID) controller for a two degree of freedom (2DOF), quarter-car active suspension system. The controller design consists of a PID controller in a feedback loop and a neural network feedforward controller for the suspension travel to improve the vehicle ride comfort and handling quality. Nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model. A SISO neural network (NN) model was developed using the input-output data set obtained from the mathematical model simulation. Levenberg-Marquardt algorithm was used to train the NN model. The NN model achieved fitness values of 99.98%, 99.98% and 99.96% for sigmoidnet, wavenet and neuralnet neural network structures respectively. The proposed controller was compared with a constant gain PID controller in a suspension travel setpoint tracking in the presence of a deterministic road disturbance. The NN-based PID controller showed better performances in terms of rise times and overshoots.

http://eolstoragewe.blob.core.windows.net/wm-418498-cmsimages/Publications/ARJ_documents/2010no3/101_3_2.pdf

System identification and neural network based pid control of servo - hydraulic vehicle suspension system

This paper presents the design of a multi-layer feedforward neural network-based model predictive controller (NNMPC) for a two degree-of-freedom (DOF), quarter-car servo-hydraulic vehicle suspension system. The nonlinear dynamics of the servo-hydraulic actuator is incorporated in the suspension model and thus a suspension travel controller is developed to indirectly improve the ride comfort and handling quality of the suspension system. A SISO feedforward multi-layer perceptron (MLP) neural network (NN) model is developed using input-output data sets obtained from the mathematical model simulation. Levenberg-Marquandt algorithm was employed in training the NN model. The NNMPC was used to predict the future responses that are optimized in a sub-loop of the plant for cost minimization. The proposed controller is compared with an optimally tuned constant-gain PID controller (based on Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road input disturbance. Simulation results demonstrate the superior performance of the NNMPC over the generic PID - based in adapting to the deterministic road disturbance.

Neural network-based model predictive control of a servo-hydraulic vehicle suspension system

This paper presents the design of a neural network-based feedback linearisation (NARMA-L2) slip controller for an anti-lock braking system (ABS). The dynamics of the electro-mechanical based braking system are incorporated in the ABS model and thus a slip controller is developed to minimise the braking distance. The proposed controller is compared with an optimally-tuned PID controller. Simulation results demonstrate the superiority of the NN-based controller over the generic PID-based controller.

http://ieeexplore.ieee.org/iel5/5256114/5276086/05276197.pdf

Neural network based feedback linearisation slip control of an anti-lock braking system

A direct adaptive neural network-based feedback linearization (NNFBL) slip control scheme for an antilock braking system (ABS) is presented. The NNFBL slip controller is developed to minimise the vehicle braking distance and to simultaneously improve its overall ride comfort and road handling. The comprehensive vehicle model incorporates the passive suspension dynamics, the dynamics of the electro-mechanical based braking system and air drag and wheel bearing friction. A feedforward, multilayer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is selected to represent the ABS with passive suspension. The NN model was trained using Levenberg-Marquardt optimization algorithm. The controlled signal was further boosted using a genetic algorithm generated gain. The effectiveness of the proposed controller is demonstrated by simulation results, in the presence of deterministic road disturbance input to the suspension and varying road conditions. The results are superior with respect to braking distance minimization and also to reference slip tracking, especially on the dry asphalt road.

Direct adaptive neural control of antilock braking systems incorporated with passive suspension dynamics

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/1860.pdf

Otis T. Nyandoro

Papers

System identification and neural network based pid control of servo - hydraulic vehicle suspension system

Neural network-based model predictive control of a servo-hydraulic vehicle suspension system

Neural network based feedback linearisation slip control of an anti-lock braking system

Direct adaptive neural control of antilock braking systems incorporated with passive suspension dynamics

Adaptive Sliding Backstepping Control of Quadrotor UAV Attitude