Adaptive control of a submerged vehicle with sliding fuzzy relations

Shimmin, D.W.; Stephens, Michael; Swainston, J. R.

doi:10.1016/0165-0114(95)00288-x

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Once the network structure and the initial parameters are estimated using the previously described two-step procedure, learning can be continued by updating the weights according to error backpropagation principle, as new training examples are encountered. The weight updating equations are (6) for the weight between the output and the th hidden neuron and (7) for the weight between the th hidden neuron and the th input. In (6) and (7), is the target output and is the learning rate.…”

Section: Online Learningmentioning

confidence: 99%

“…Although it is relatively simple to obtain training data for learning a process (inputs: process inputs, output: process outputs), obtaining training data for a controller (inputs: process error, plant state, etc., output: desired control output) may be difficult or expensive in many real environments. The model reference adaptive control (MRAC) approach, where controller parameters are adjusted to minimize the error between the plant and a reference model, has been widely adopted for overcoming this problem [5]- [7]. However, difficulties may arise in specification of a suitable reference model for a given process: it may not be appropriate to force the process to follow a unique reference model under all circumstances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution

Rajapakse¹,

Furuta

Kondo

2002

IEEE Trans. Fuzzy Syst.

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This paper presents an adaptive control architecture, where evolutionary learning is applied for initial learning and real-time tuning of a fuzzy logic controller. The initial learning phase involves identification of an artificial neural network model of the process and subsequent development of a fuzzy controller with parameters obtained via a genetic search. The neural network model is utilized for evaluating trial fuzzy controllers during the genetic search. The proposed adaptive mechanism is based on the concept of perpetual evolution, where parameters of the fuzzy controller are updated at each time step with solutions extracted from a continuously evolving population of trials. There are two mechanisms that accommodate the real-time changes in the control task and/or the process into the continuous genetic search: a scheme that dynamically modifies the fitness evaluation criteria of the genetic algorithm (GA) and the online learning of the neural network model used for evaluating trial controllers. The potential for using evolutionary learning for real-time adaptive control is illustrated through computer simulations, where the proposed technique is applied for a chemical process control problem.

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Section: Online Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution

Rajapakse¹,

Furuta

Kondo

2002

IEEE Trans. Fuzzy Syst.

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“…Bui and Kim [10] used an FLC method that enabled AUVs to navigate safely through obstacles to a goal, with the optimal path proven by their simulation results. Shimmin et al [11] devised a self-tuning fuzzy controller for application in submerged ROVs. In simulations, the vehicle exhibited a generally conservative and safe behavior during the training period, as is required in real-time on-site applications.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

et al. 2020

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Autonomous underwater vehicles and remotely operated vehicles (ROVs) are unmanned underwater vehicles widely used in marine environments. Establishing an efficient obstacle avoidance approach in underwater environments remains a challenge for these vehicles. Most studies have relied on simulated results; few have been conducted with vehicles in a real environment. This study used an ROV equipped with a scanning sonar as an experimental platform and applied fuzzy logic control to solve nonlinear and uncertain problems, which are difficult to address using conventional control theory. Using data from the depth and inertial sensors, fuzzy logic control can output defuzzification command values that are passed through a fuzzy inference engine to control ROV motion. Fuzzy logic control was used to evaluate depth and heading degrees in navigation experiments. In heading navigation, scanning sonar was used to detect obstacles in the scanning range. An optimum navigation strategy was also developed to calculate appropriate headings to safely and stably navigate during a mission to attain a predetermined destination. The results indicated that the ROV with fuzzy logic control had superior control stability and obstacle avoidance in an underwater environment.

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“…Their performance is described as accurate when uncertainty and perturbations take place while performing a trajectory. Although the training periods are extremely long, there are also combinations of PID controls and a smart system aimed to auto-tune the gains of different systems such as: sub-aquatic [ 15 , 16 ], non linear [ 17 , 18 , 19 , 20 , 21 , 22 ], and others: [ 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

Hernández-Alvarado

García-Valdovinos

Salgado-Jiménez

et al. 2016

Sensors

157

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For decades, PID (Proportional + Integral + Derivative)-like controllers have been successfully used in academia and industry for many kinds of plants. This is thanks to its simplicity and suitable performance in linear or linearized plants, and under certain conditions, in nonlinear ones. A number of PID controller gains tuning approaches have been proposed in the literature in the last decades; most of them off-line techniques. However, in those cases wherein plants are subject to continuous parametric changes or external disturbances, online gains tuning is a desirable choice. This is the case of modular underwater ROVs (Remotely Operated Vehicles) where parameters (weight, buoyancy, added mass, among others) change according to the tool it is fitted with. In practice, some amount of time is dedicated to tune the PID gains of a ROV. Once the best set of gains has been achieved the ROV is ready to work. However, when the vehicle changes its tool or it is subject to ocean currents, its performance deteriorates since the fixed set of gains is no longer valid for the new conditions. Thus, an online PID gains tuning algorithm should be implemented to overcome this problem. In this paper, an auto-tune PID-like controller based on Neural Networks (NN) is proposed. The NN plays the role of automatically estimating the suitable set of PID gains that achieves stability of the system. The NN adjusts online the controller gains that attain the smaller position tracking error. Simulation results are given considering an underactuated 6 DOF (degrees of freedom) underwater ROV. Real time experiments on an underactuated mini ROV are conducted to show the effectiveness of the proposed scheme.

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Adaptive control of a submerged vehicle with sliding fuzzy relations

Cited by 6 publications

References 15 publications

Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution

Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution

Application of Fuzzy Theory and Optimum Computing to the Obstacle Avoidance Control of Unmanned Underwater Vehicles

Neural Network-Based Self-Tuning PID Control for Underwater Vehicles

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