基于克隆选择优化的船舶航向自适应控制
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摘要
克隆选择算法是受生物免疫系统克隆选择机制启发而发展起来的一种新兴的进化算法,为解决复杂问题提供了一种新颖的方法,目前正吸引广大智能计算学者的兴趣。船舶运动控制是一个备受航海科技界和控制理论界关注的领域,船舶自动舵的性能直接关系到船舶的航行安全和经济效益。本文从研究人工免疫克隆选择算法出发,以船舶航向自适应控制为目的,研究了免疫克隆选择全局优化算法及其在船舶航向自适应控制中的应用。
1.将混沌机制引入克隆选择算法中,将混沌优化机制和免疫克隆进化算法有机结合,提出混沌-克隆进化算法。用混沌浮点数编码代替克隆选择算法的二进制编码,利用混沌随机序列产生初始种群,保证初始种群的多样性。对高亲和度抗体采用混沌扰动策略,对抗体根据其亲和度大小加以不同的混沌扰动;混沌扰动系数随进化代数而变,进化前期加速搜索,进化后期加速收敛。对个体的混沌扰动实质上是个体的局部混沌搜索过程,但种群中各个体又具有与亲和度相关的不同搜索步长,使得种群在进化中能同步实现全局搜索和局部搜索;而动态地改变混沌扰动尺度,又提高了进化后期的搜索精度。对低亲和度抗体采用混沌再生策略,保持种群多样性。应用复杂函数进行优化试验验证了该算法的有效性。
2.基于免疫克隆选择原理,采用浮点数编码,提出一种分级变异的动态免疫克隆选择优化算法。采用了分级变异和动态参数策略,使得该算法在种群内部变异和种群进化过程中都能有机地结合全局搜索和局部搜索。根据抗体的亲和度把种群分解为三个子种群,分配以不同的搜索任务,实施不同的变异策略;在进化过程中动态改变种群规模、克隆规模和变异参数,从而加快全局搜索速度和提高局部搜索精度。对5个复杂函数的优化仿真试验表明该算法有效地改善了克隆选择算法的性能,收敛速度快,精度高,不易陷入局部最优。
3.基于免疫克隆选择原理,借鉴遗传算法的精英选择算子和交叉算子,提出一种启发式自适应免疫克隆选择算法。该算法采用精英克隆变异算子、启发式克隆交叉算子,对少量的高亲和度抗体进行小幅变异,搜索局部最优解;对其它低亲和度抗体则依据其亲和度随机地与高亲和度抗体进行不同程度的交叉,使其融入优质基因,同时还能保持种群的多样性;从而使得克隆选择算法能在加快全局搜索的同时还能保证局部收敛的精度;其参数自适应机制能有效地防止种群进化停滞。仿真对比试验表明该算法明显改善了克隆选择算法的性能,计算量小,性能稳定,精度高,不易陷入局部最优。
4.将克隆选择算法用于自适应控制问题,提出一类基于对象模型的克隆选择自适应控制算法。克隆选择算法利用对象模型预估对象未来的输出,评估候选控制器性能,选择最适合当前环境的控制器控制实际对象,从而实现控制器参数的实时整定。将上述克隆选择自适应控制算法用于常规船舶航向PID控制,提出一种船舶航向克隆选择直接自适应PID自动舵;在其自适应模块中加入一个参考模型,利用参考模型监督PID控制器性能评估,试图控制船舶输出与参考模型输出一致,提出了参考模型监督的船舶航向克隆选择自适应自动舵,其中的参考模型与控制系统为并联形式;在闭环反馈前,串联一个参考模型对参考输入进行滤波,使PID控制器直接修正对象输出与参考模型输出误差,实现对参考模型的跟踪控制,提出参考模型跟踪的船舶航向克隆选择自适应自动舵控制算法。以某货船的三阶非线性模型作为被控对象,以其二阶线性模型作为对象模型的仿真试验表明将克隆选择算法应用于航向控制问题是有效的,三种克隆选择航向自适应控制算法对船舶模型摄动和外界干扰有着较强的自适应能力。
5.针对船舶模型参数摄动和外界干扰不确定问题,提出两种分别为带干扰补偿和不带干扰补偿的克隆选择模型辨识的模型参考PD控制算法。将船舶当成“黑箱”系统,克隆选择算法根据船首向和舵角信息将船舶在线辨识为一个二阶线性模型,然后应用二阶线性系统的模型参考PD控制理论求取PD控制器参数,使船舶跟踪参考模型输出。通过对一个二阶线性船舶和一个三阶非线性船舶两例在不同环境下的仿真对比试验,其结果表明带干扰辨识和补偿的控制算法效果更为出色,而不带干扰辨识和补偿的控制算法也能在一定程度上将外界干扰转化为等效的模型参数辨识,从而提高其鲁棒性。
Based on ideas gleaned from the clone selection principle in immunology system, Clone Selection Algorithms (CSA) has become an advanced method, offering a fresh perspective for complicated calculation problems. CSA attracts foremost attention among calculation researchers. Accordingly, Ship Motion Control is a study focus attended by scientists of both navigation science and control theory because the performance of autopilot for ship steering is of great importance for a ship to navigate safely and economically. Aiming at the research of adaptive control for ship steering, this dissertation examines the Clone Selection Algorithms for global optimization and applications to the adaptive control for ship steering.
1. A Chaos-Clone based Evolutionary Algorithm (CCEA) is proposed by integrating chaos search and clone selection algorithm (CLONALG). CCEA uses chaotic floating point numbers code instead of the binary code of CLONALG, and produces the initial diversity of antibody population. The algorithm adopts a chaotic disturbance strategy for the antibodies with high affinity; the different chaotic disturbance is added to an antibody according to its affinity to antigen; also, the disturbance factor changes with the evolutionary generation so as to speed search during prophase and convergence during anaphase. CCEA uses a chaos to reshuffle operation for those antibodies with low affinity to maintain the diversity of the population. Simulations on complex benchmark functions demonstrate that the CCEA has better performance than both the chaos optimization and CLONALG individually used.
2. A dynamic immune clone selection algorithm with classified mutation is proposed based on traditional floating point coding. Applying grading mutation and dynamic parameter strategies, the method speeds up the global search and improves the local con- vergence precision in the process of inner population mutation and their evolution. On one hand, according to the antibody affinity in relation to the antigen, the antibody population is decomposed into three subsets, and they are submitted to respective mutation processes for their different given tasks. On the other hand, the population size, the clone size and the mutation parameters are dynamically changed with evolution process. The proposed algorithm is use to optimize complex functions for testing, and the results show merits of its effectiveness in CSA, such as high accuracy, quick convergence and less opportunity for local optima.
3. Based on clone selection principle, a novel evolutionary algorithm is proposed with the aid of traditional method using elitism clone mutation and heuristic crossover. The two main operations of elitism clone mutation and heuristic crossover are defined. The elitist antibodies with highest affinity are subject to a small mutation process to search local optima. Those antibodies with general affinity are suffered to a heuristic crossover with elitist antibodies to speed global optimization. The antibodies with lowest affinity are replaced by new individuals to maintain the diversity of the population. Also, to prevent evolutionary stagnation, the scaling factors of affinity are adaptively adjusted in order to guarantee the accuracy of local convergence when speeding up global searches. The computer simulation results adopted complex benchmark functions demonstrate that the proposed algorithm has good performance in the aspect of improvement of CLONALG, easy operation, and high accuracy.
4. A class of model-based clone selection adaptive control algorithm is developed by applying CSA to adaptive control problem. CSA dopes out the plant output by a plant model and evaluates the candidate controllers before selecting the fittest controller to the current plant operating conditions. Such approach realizes on-line controller tuning. Then, a CSA direct adaptive PID control is developed by applying this approach to conventional PID controller for ship steering. Moreover, we introduce two new, but closely related approaches to CSA adaptive control which is called "CSA reference model supervisory adaptive PID autopilot" and "CSA reference model tracking adaptive PID autopilot" for ship steering. In these techniques a reference model is used to guide the system output towards the reference model output. Simulations verified that CSA can carry on the adaptive control excellently, the above three adaptive control algorithm for ship steering have good robustness to environmental disturbance and ship model error.
5. Two CSA model-identified model reference PD controllers, respectively with and without Disturbance Identification-and-Compensation (DIC) are proposed to resist both ship model possible uncertainty and changeful operating conditions. Regarding ship system as a "black box ", CSA on-line identifies the ship with a second order linear model according to the information of both course and rudder angle. Then, the second order linear together with a model reference is used to calculate the PD controller parameters. Their goals are to steer the ship tracking reference model output. Simulations show the controller with DIC has better performance than that without DIC, though the controller without DIC can also cope with the disturbance by translating some disturbance into plant model parameters.
1.将混沌机制引入克隆选择算法中,将混沌优化机制和免疫克隆进化算法有机结合,提出混沌-克隆进化算法。用混沌浮点数编码代替克隆选择算法的二进制编码,利用混沌随机序列产生初始种群,保证初始种群的多样性。对高亲和度抗体采用混沌扰动策略,对抗体根据其亲和度大小加以不同的混沌扰动;混沌扰动系数随进化代数而变,进化前期加速搜索,进化后期加速收敛。对个体的混沌扰动实质上是个体的局部混沌搜索过程,但种群中各个体又具有与亲和度相关的不同搜索步长,使得种群在进化中能同步实现全局搜索和局部搜索;而动态地改变混沌扰动尺度,又提高了进化后期的搜索精度。对低亲和度抗体采用混沌再生策略,保持种群多样性。应用复杂函数进行优化试验验证了该算法的有效性。
2.基于免疫克隆选择原理,采用浮点数编码,提出一种分级变异的动态免疫克隆选择优化算法。采用了分级变异和动态参数策略,使得该算法在种群内部变异和种群进化过程中都能有机地结合全局搜索和局部搜索。根据抗体的亲和度把种群分解为三个子种群,分配以不同的搜索任务,实施不同的变异策略;在进化过程中动态改变种群规模、克隆规模和变异参数,从而加快全局搜索速度和提高局部搜索精度。对5个复杂函数的优化仿真试验表明该算法有效地改善了克隆选择算法的性能,收敛速度快,精度高,不易陷入局部最优。
3.基于免疫克隆选择原理,借鉴遗传算法的精英选择算子和交叉算子,提出一种启发式自适应免疫克隆选择算法。该算法采用精英克隆变异算子、启发式克隆交叉算子,对少量的高亲和度抗体进行小幅变异,搜索局部最优解;对其它低亲和度抗体则依据其亲和度随机地与高亲和度抗体进行不同程度的交叉,使其融入优质基因,同时还能保持种群的多样性;从而使得克隆选择算法能在加快全局搜索的同时还能保证局部收敛的精度;其参数自适应机制能有效地防止种群进化停滞。仿真对比试验表明该算法明显改善了克隆选择算法的性能,计算量小,性能稳定,精度高,不易陷入局部最优。
4.将克隆选择算法用于自适应控制问题,提出一类基于对象模型的克隆选择自适应控制算法。克隆选择算法利用对象模型预估对象未来的输出,评估候选控制器性能,选择最适合当前环境的控制器控制实际对象,从而实现控制器参数的实时整定。将上述克隆选择自适应控制算法用于常规船舶航向PID控制,提出一种船舶航向克隆选择直接自适应PID自动舵;在其自适应模块中加入一个参考模型,利用参考模型监督PID控制器性能评估,试图控制船舶输出与参考模型输出一致,提出了参考模型监督的船舶航向克隆选择自适应自动舵,其中的参考模型与控制系统为并联形式;在闭环反馈前,串联一个参考模型对参考输入进行滤波,使PID控制器直接修正对象输出与参考模型输出误差,实现对参考模型的跟踪控制,提出参考模型跟踪的船舶航向克隆选择自适应自动舵控制算法。以某货船的三阶非线性模型作为被控对象,以其二阶线性模型作为对象模型的仿真试验表明将克隆选择算法应用于航向控制问题是有效的,三种克隆选择航向自适应控制算法对船舶模型摄动和外界干扰有着较强的自适应能力。
5.针对船舶模型参数摄动和外界干扰不确定问题,提出两种分别为带干扰补偿和不带干扰补偿的克隆选择模型辨识的模型参考PD控制算法。将船舶当成“黑箱”系统,克隆选择算法根据船首向和舵角信息将船舶在线辨识为一个二阶线性模型,然后应用二阶线性系统的模型参考PD控制理论求取PD控制器参数,使船舶跟踪参考模型输出。通过对一个二阶线性船舶和一个三阶非线性船舶两例在不同环境下的仿真对比试验,其结果表明带干扰辨识和补偿的控制算法效果更为出色,而不带干扰辨识和补偿的控制算法也能在一定程度上将外界干扰转化为等效的模型参数辨识,从而提高其鲁棒性。
Based on ideas gleaned from the clone selection principle in immunology system, Clone Selection Algorithms (CSA) has become an advanced method, offering a fresh perspective for complicated calculation problems. CSA attracts foremost attention among calculation researchers. Accordingly, Ship Motion Control is a study focus attended by scientists of both navigation science and control theory because the performance of autopilot for ship steering is of great importance for a ship to navigate safely and economically. Aiming at the research of adaptive control for ship steering, this dissertation examines the Clone Selection Algorithms for global optimization and applications to the adaptive control for ship steering.
1. A Chaos-Clone based Evolutionary Algorithm (CCEA) is proposed by integrating chaos search and clone selection algorithm (CLONALG). CCEA uses chaotic floating point numbers code instead of the binary code of CLONALG, and produces the initial diversity of antibody population. The algorithm adopts a chaotic disturbance strategy for the antibodies with high affinity; the different chaotic disturbance is added to an antibody according to its affinity to antigen; also, the disturbance factor changes with the evolutionary generation so as to speed search during prophase and convergence during anaphase. CCEA uses a chaos to reshuffle operation for those antibodies with low affinity to maintain the diversity of the population. Simulations on complex benchmark functions demonstrate that the CCEA has better performance than both the chaos optimization and CLONALG individually used.
2. A dynamic immune clone selection algorithm with classified mutation is proposed based on traditional floating point coding. Applying grading mutation and dynamic parameter strategies, the method speeds up the global search and improves the local con- vergence precision in the process of inner population mutation and their evolution. On one hand, according to the antibody affinity in relation to the antigen, the antibody population is decomposed into three subsets, and they are submitted to respective mutation processes for their different given tasks. On the other hand, the population size, the clone size and the mutation parameters are dynamically changed with evolution process. The proposed algorithm is use to optimize complex functions for testing, and the results show merits of its effectiveness in CSA, such as high accuracy, quick convergence and less opportunity for local optima.
3. Based on clone selection principle, a novel evolutionary algorithm is proposed with the aid of traditional method using elitism clone mutation and heuristic crossover. The two main operations of elitism clone mutation and heuristic crossover are defined. The elitist antibodies with highest affinity are subject to a small mutation process to search local optima. Those antibodies with general affinity are suffered to a heuristic crossover with elitist antibodies to speed global optimization. The antibodies with lowest affinity are replaced by new individuals to maintain the diversity of the population. Also, to prevent evolutionary stagnation, the scaling factors of affinity are adaptively adjusted in order to guarantee the accuracy of local convergence when speeding up global searches. The computer simulation results adopted complex benchmark functions demonstrate that the proposed algorithm has good performance in the aspect of improvement of CLONALG, easy operation, and high accuracy.
4. A class of model-based clone selection adaptive control algorithm is developed by applying CSA to adaptive control problem. CSA dopes out the plant output by a plant model and evaluates the candidate controllers before selecting the fittest controller to the current plant operating conditions. Such approach realizes on-line controller tuning. Then, a CSA direct adaptive PID control is developed by applying this approach to conventional PID controller for ship steering. Moreover, we introduce two new, but closely related approaches to CSA adaptive control which is called "CSA reference model supervisory adaptive PID autopilot" and "CSA reference model tracking adaptive PID autopilot" for ship steering. In these techniques a reference model is used to guide the system output towards the reference model output. Simulations verified that CSA can carry on the adaptive control excellently, the above three adaptive control algorithm for ship steering have good robustness to environmental disturbance and ship model error.
5. Two CSA model-identified model reference PD controllers, respectively with and without Disturbance Identification-and-Compensation (DIC) are proposed to resist both ship model possible uncertainty and changeful operating conditions. Regarding ship system as a "black box ", CSA on-line identifies the ship with a second order linear model according to the information of both course and rudder angle. Then, the second order linear together with a model reference is used to calculate the PD controller parameters. Their goals are to steer the ship tracking reference model output. Simulations show the controller with DIC has better performance than that without DIC, though the controller without DIC can also cope with the disturbance by translating some disturbance into plant model parameters.
引文
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