Robust Sliding Mode Control Based on GA Optimization and CMAC Compensation for Lower Limb Exoskeleton

A lower limb assistive exoskeleton is designed to help operators walk or carry payloads. The exoskeleton is required to shadow human motion intent accurately and compliantly to prevent incoordination. If the user’s intention is estimated accurately, a precise position control strategy will improve collaboration between the user and the exoskeleton. In this paper, a hybrid position control scheme, combining sliding mode control (SMC) with a cerebellar model articulation controller (CMAC) neural network, is proposed to control the exoskeleton to react appropriately to human motion intent. A genetic algorithm (GA) is utilized to determine the optimal sliding surface and the sliding control law to improve performance of SMC. The proposed control strategy (SMC_GA_CMAC) is compared with three other types of approaches, that is, conventional SMC without optimization, optimal SMC with GA (SMC_GA), and SMC with CMAC compensation (SMC_CMAC), all of which are employed to track the desired joint angular position which is deduced from Clinical Gait Analysis (CGA) data. Position tracking performance is investigated with cosimulation using ADAMS and MATLAB/SIMULINK in two cases, of which the first case is without disturbances while the second case is with a bounded disturbance. The cosimulation results show the effectiveness of the proposed control strategy which can be employed in similar exoskeleton systems

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

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Stability and weight smoothing in CMAC neural networks

Although the CMAC (Cerebellar Model Articulation Controller) neural network has been successfully used in control systems for many years, its property of local generalization, the availability of trained information for network responses at adjacent untrained locations, although responsible for the networks rapid learning and efficient implementation, results in network responses that is, when trained with sparse or widely spaced training data, spiky in nature even when the underlying function being learned is quite smooth. Since the derivative of such a network response can vary widely, the CMAC\u27s usefulness for solving optimization problems as well as for certain other control system applications can be severely limited. This dissertation presents the CMAC algorithm in sufficient detail to explore its strengths and weaknesses. Its properties of information generalization and storage are discussed and comparisons are made with other neural network algorithms and with other adaptive control algorithms. A synopsis of the development of the fields of neural networks and adaptive control is included to lend historical perspective. A stability analysis of the CMAC algorithm for open-loop function learning is developed. This stability analysis casts the function learning problem as a unique implementation of the model reference structure and develops a Lyapunov function to prove convergence of the CMAC to the target model. A new CMAC learning rule is developed by treating the CMAC as a set of simultaneous equations in a constrained optimization problem and making appropriate choices for the weight penalty matrix in the cost equation. This dissertation then presents a new CMAC learning algorithm which has the property of weight smoothing to improve generalization, function approximation in partially trained networks and the partial derivatives of learned functions. This new learning algorithm is significant in that it derives from an optimum solution and demonstrates a dramatic performance improvement for function learning in the presence of widely spaced training data. Developed from a completely unique analytical direction, this algorithm represents a coupling and extension of single- and multi-resolution CMAC algorithms developed by other researchers. The insights derived from the analysis of the optimum solution and the resulting new learning rules are discussed and suggestions for future work are presented

System Identification for Nonlinear Control Using Neural Networks

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique